NeurIPS 2022 Wednesday 11/30

Timezone: America/Chicago

Full Schedule Mon 11/28 Tue 11/29 Wed 11/30 Thu 12/1 Fri 12/2 Sat 12/3 Sun 12/4 Mon 12/5 Tue 12/6 Wed 12/7 Thu 12/8 Fri 12/9

Registration Desk

8:00 AM - 6:00 PM

Invited Talk

Interaction-Centric AI

Juho Kim

9:30 AM - 10:30 AM

Remarkable model performance makes news headlines and compelling demos, but these advances rarely translate to a lasting impact on real-world users. A common anti-pattern is overlooking the dynamic, complex, and unexpected ways humans interact with AI, which in turn limits the adoption and usage of AI in practical contexts. To address this, I argue that human-AI interaction should be considered a first-class object in designing AI applications.

In this talk, I present a few novel interactive systems that use AI to support complex real-life tasks. I discuss tensions and solutions in designing human-AI interaction, and critically reflect on my own research to share hard-earned design lessons. Factors such as user motivation, coordination between stakeholders, social dynamics, and user’s and AI’s adaptivity to each other often play a crucial role in determining the user experience of AI, even more so than model accuracy. My call to action is that we need to establish robust building blocks for “Interaction-Centric AI”—a systematic approach to designing and engineering human-AI interaction that complements and overcomes the limitations of model- and data-centric views.

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Poster

Poster Session 3

11:00 AM - 1:00 PM

411 Events in this session

Machine Learning on Graphs: A Model and Comprehensive Taxonomy

Ines Chami · Sami Abu-El-Haija · Bryan Perozzi · Christopher Ré · Kevin Murphy

Cost-Sensitive Self-Training for Optimizing Non-Decomposable Metrics

Harsh Rangwani · shrinivas ramasubramanian · Sho Takemori · Kato Takashi · Yuhei Umeda · Venkatesh Babu R

Discovering and Overcoming Limitations of Noise-engineered Data-free Knowledge Distillation

Piyush Raikwar · Deepak Mishra

Exploration via Elliptical Episodic Bonuses

Mikael Henaff · Roberta Raileanu · Minqi Jiang · Tim Rocktäschel

Uni[MASK]: Unified Inference in Sequential Decision Problems

Micah Carroll · Orr Paradise · Jessy Lin · Raluca Georgescu · Mingfei Sun · David Bignell · Stephanie Milani · Katja Hofmann · Matthew Hausknecht · Anca Dragan · Sam Devlin

Learning to Navigate Wikipedia by Taking Random Walks

Manzil Zaheer · Kenneth Marino · Will Grathwohl · John Schultz · Wendy Shang · Sheila Babayan · Arun Ahuja · Ishita Dasgupta · Christine Kaeser-Chen · Rob Fergus

Reinforcement Learning with Neural Radiance Fields

Danny Driess · Ingmar Schubert · Pete Florence · Yunzhu Li · Marc Toussaint

Few-Shot Audio-Visual Learning of Environment Acoustics

Sagnik Majumder · Changan Chen · Ziad Al-Halah · Kristen Grauman

You Can’t Count on Luck: Why Decision Transformers and RvS Fail in Stochastic Environments

Keiran Paster · Sheila McIlraith · Jimmy Ba

Decision-Focused Learning without Decision-Making: Learning Locally Optimized Decision Losses

Sanket Shah · Kai Wang · Bryan Wilder · Andrew Perrault · Milind Tambe

Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation

Yao Qin · Chiyuan Zhang · Ting Chen · Balaji Lakshminarayanan · Alex Beutel · Xuezhi Wang

Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo

Ignacio Peis · Chao Ma · José Miguel Hernández-Lobato

Local Latent Space Bayesian Optimization over Structured Inputs

Natalie Maus · Haydn Jones · Juston Moore · Matt Kusner · John Bradshaw · Jacob Gardner

Bayesian Clustering of Neural Spiking Activity Using a Mixture of Dynamic Poisson Factor Analyzers

Ganchao Wei · Ian H Stevenson · Xiaojing Wang

Spherical Channels for Modeling Atomic Interactions

Larry Zitnick · Abhishek Das · Adeesh Kolluru · Janice Lan · Muhammed Shuaibi · Anuroop Sriram · Zachary Ulissi · Brandon Wood

Understanding the Evolution of Linear Regions in Deep Reinforcement Learning

Setareh Cohan · Nam Hee Kim · David Rolnick · Michiel van de Panne

A General Framework for Auditing Differentially Private Machine Learning

Fred Lu · Joseph Munoz · Maya Fuchs · Tyler LeBlond · Elliott Zaresky-Williams · Edward Raff · Francis Ferraro · Brian Testa

CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis

Shichong Peng · Seyed Alireza Moazenipourasil · Ke Li

On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning

Lorenzo Bonicelli · Matteo Boschini · Angelo Porrello · Concetto Spampinato · SIMONE CALDERARA

Efficient Non-Parametric Optimizer Search for Diverse Tasks

Ruochen Wang · Yuanhao Xiong · Minhao Cheng · Cho-Jui Hsieh

Transform Once: Efficient Operator Learning in Frequency Domain

Michael Poli · Stefano Massaroli · Federico Berto · Jinkyoo Park · Tri Dao · Christopher Ré · Stefano Ermon

Sparse Interaction Additive Networks via Feature Interaction Detection and Sparse Selection

James Enouen · Yan Liu

Meta-Learning Dynamics Forecasting Using Task Inference

Rui Wang · Robin Walters · Rose Yu

Implicit Neural Representations with Levels-of-Experts

Zekun Hao · Arun Mallya · Serge Belongie · Ming-Yu Liu

Towards Theoretically Inspired Neural Initialization Optimization

Yibo Yang · Hong Wang · Haobo Yuan · Zhouchen Lin

Improving GANs with A Dynamic Discriminator

Ceyuan Yang · Yujun Shen · Yinghao Xu · Deli Zhao · Bo Dai · Bolei Zhou

Towards Learning Universal Hyperparameter Optimizers with Transformers

Yutian Chen · Xingyou Song · Chansoo Lee · Zi Wang · Richard Zhang · David Dohan · Kazuya Kawakami · Greg Kochanski · Arnaud Doucet · Marc'Aurelio Ranzato · Sagi Perel · Nando de Freitas

Graph Self-supervised Learning with Accurate Discrepancy Learning

Dongki Kim · Jinheon Baek · Sung Ju Hwang

A2: Efficient Automated Attacker for Boosting Adversarial Training

Zhuoer Xu · Guanghui Zhu · Changhua Meng · shiwen cui · Zhenzhe Ying · Weiqiang Wang · Ming GU · Yihua Huang

Boosting Out-of-distribution Detection with Typical Features

Yao Zhu · YueFeng Chen · Chuanlong Xie · Xiaodan Li · Rong Zhang · Hui Xue' · Xiang Tian · bolun zheng · Yaowu Chen

Unified Optimal Transport Framework for Universal Domain Adaptation

Wanxing Chang · Ye Shi · Hoang Tuan · Jingya Wang

Geometric Order Learning for Rank Estimation

Seon-Ho Lee · Nyeong Ho Shin · Chang-Su Kim

MACK: Multimodal Aligned Conceptual Knowledge for Unpaired Image-text Matching

Yan Huang · Yuming Wang · Yunan Zeng · Liang Wang

Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection

Hanoona Bangalath · Muhammad Maaz · Muhammad Uzair Khattak · Salman Khan · Fahad Shahbaz Khan

Meta-Learning with Self-Improving Momentum Target

Jihoon Tack · Jongjin Park · Hankook Lee · Jaeho Lee · Jinwoo Shin

GBA: A Tuning-free Approach to Switch between Synchronous and Asynchronous Training for Recommendation Models

Wenbo Su · Yuanxing Zhang · Yufeng Cai · Kaixu Ren · Pengjie Wang · Huimin Yi · Yue Song · Jing Chen · Hongbo Deng · Jian Xu · Lin Qu · Bo Zheng

CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference

Ran Ran · Wei Wang · Quan Gang · Jieming Yin · Nuo Xu · Wujie Wen

Semi-supervised Vision Transformers at Scale

Zhaowei Cai · Avinash Ravichandran · Paolo Favaro · Manchen Wang · Davide Modolo · Rahul Bhotika · Zhuowen Tu · Stefano Soatto

AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition

Shoufa Chen · Chongjian GE · Zhan Tong · Jiangliu Wang · Yibing Song · Jue Wang · Ping Luo

Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively

Haojie Zhang · Ge Li · Jia Li · Zhongjin Zhang · YUQI ZHU · Zhi Jin

Self-Supervised Visual Representation Learning with Semantic Grouping

Xin Wen · Bingchen Zhao · Anlin Zheng · Xiangyu Zhang · Xiaojuan Qi

Geometry-aware Two-scale PIFu Representation for Human Reconstruction

Zheng Dong · Ke Xu · Ziheng Duan · Hujun Bao · Weiwei Xu · Rynson Lau

360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter Tuning

Bolivar Solarte · Chin-Hsuan Wu · Yueh-Cheng Liu · Yi-Hsuan Tsai · Min Sun

FeLMi : Few shot Learning with hard Mixup

Aniket Roy · Anshul Shah · Ketul Shah · Prithviraj Dhar · Anoop Cherian · Rama Chellappa

Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification

Massimiliano Patacchiola · John Bronskill · Aliaksandra Shysheya · Katja Hofmann · Sebastian Nowozin · Richard Turner

Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks

Hongjoon Ahn · Yongyi Yang · Quan Gan · Taesup Moon · David P Wipf

One-shot Neural Backdoor Erasing via Adversarial Weight Masking

Shuwen Chai · Jinghui Chen

BILCO: An Efficient Algorithm for Joint Alignment of Time Series

Xuelong Mi · Mengfan Wang · Alex Chen · Jing-Xuan Lim · Yizhi Wang · Misha B Ahrens · Guoqiang Yu

LOT: Layer-wise Orthogonal Training on Improving l2 Certified Robustness

Xiaojun Xu · Linyi Li · Bo Li

Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation

Kien Do · Thai Hung Le · Dung Nguyen · Dang Nguyen · HARIPRIYA HARIKUMAR · Truyen Tran · Santu Rana · Svetha Venkatesh

AutoMTL: A Programming Framework for Automating Efficient Multi-Task Learning

Lijun Zhang · Xiao Liu · Hui Guan

Learning to Discover and Detect Objects

Vladimir Fomenko · Ismail Elezi · Deva Ramanan · Laura Leal-Taixé · Aljosa Osep

Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation

Ioana Bica · Mihaela van der Schaar

Unsupervised Learning of Group Invariant and Equivariant Representations

Robin Winter · Marco Bertolini · Tuan Le · Frank Noe · Djork-Arné Clevert

Towards Understanding Grokking: An Effective Theory of Representation Learning

Ziming Liu · Ouail Kitouni · Niklas S Nolte · Eric Michaud · Max Tegmark · Mike Williams

DMAP: a Distributed Morphological Attention Policy for learning to locomote with a changing body

Alberto Silvio Chiappa · Alessandro Marin Vargas · Alexander Mathis

Decentralized Training of Foundation Models in Heterogeneous Environments

Binhang Yuan · Yongjun He · Jared Davis · Tianyi Zhang · Tri Dao · Beidi Chen · Percy Liang · Christopher Ré · Ce Zhang

Model-based Lifelong Reinforcement Learning with Bayesian Exploration

Haotian Fu · Shangqun Yu · Michael Littman · George Konidaris

Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation

Peide Huang · Mengdi Xu · Jiacheng Zhu · Laixi Shi · Fei Fang · DING ZHAO

FedSR: A Simple and Effective Domain Generalization Method for Federated Learning

A. Tuan Nguyen · Philip Torr · Ser Nam Lim

Sample-Then-Optimize Batch Neural Thompson Sampling

Zhongxiang Dai · YAO SHU · Bryan Kian Hsiang Low · Patrick Jaillet

Improving Diffusion Models for Inverse Problems using Manifold Constraints

Hyungjin Chung · Byeongsu Sim · Dohoon Ryu · Jong Chul Ye

FedAvg with Fine Tuning: Local Updates Lead to Representation Learning

Liam Collins · Hamed Hassani · Aryan Mokhtari · Sanjay Shakkottai

Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition

Daeha Kim · Byung Cheol Song

Orthogonal Transformer: An Efficient Vision Transformer Backbone with Token Orthogonalization

Huaibo Huang · Xiaoqiang Zhou · Ran He

Jump Self-attention: Capturing High-order Statistics in Transformers

Haoyi Zhou · Siyang Xiao · Shanghang Zhang · Jieqi Peng · Shuai Zhang · Jianxin Li

Flamingo: a Visual Language Model for Few-Shot Learning

Jean-Baptiste Alayrac · Jeff Donahue · Pauline Luc · Antoine Miech · Iain Barr · Yana Hasson · Karel Lenc · Arthur Mensch · Katherine Millican · Malcolm Reynolds · Roman Ring · Eliza Rutherford · Serkan Cabi · Tengda Han · Zhitao Gong · Sina Samangooei · Marianne Monteiro · Jacob L Menick · Sebastian Borgeaud · Andy Brock · Aida Nematzadeh · Sahand Sharifzadeh · Mikołaj Bińkowski · Ricardo Barreira · Oriol Vinyals · Andrew Zisserman · Karén Simonyan

Visual Prompting via Image Inpainting

Amir Bar · Yossi Gandelsman · Trevor Darrell · Amir Globerson · Alexei Efros

MAtt: A Manifold Attention Network for EEG Decoding

Yue-Ting Pan · Jing-Lun Chou · Chun-Shu Wei

Distilled Gradient Aggregation: Purify Features for Input Attribution in the Deep Neural Network

Giyoung Jeon · Haedong Jeong · Jaesik Choi

Visual Concepts Tokenization

Tao Yang · Yuwang Wang · Yan Lu · Nanning Zheng

Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification

Linhao Qu · xiaoyuan luo · Manning Wang · Zhijian Song

HierSpeech: Bridging the Gap between Text and Speech by Hierarchical Variational Inference using Self-supervised Representations for Speech Synthesis

Sang-Hoon Lee · Seung-Bin Kim · Ji-Hyun Lee · Eunwoo Song · Min-Jae Hwang · Seong-Whan Lee

Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning

Dongze Lian · Daquan Zhou · Jiashi Feng · Xinchao Wang

Weighted Mutual Learning with Diversity-Driven Model Compression

Miao Zhang · Li Wang · David Campos · Wei Huang · Chenjuan Guo · Bin Yang

Understanding Benign Overfitting in Gradient-Based Meta Learning

Lisha Chen · Songtao Lu · Tianyi Chen

Friendly Noise against Adversarial Noise: A Powerful Defense against Data Poisoning Attack

Tian Yu Liu · Yu Yang · Baharan Mirzasoleiman

TransTab: Learning Transferable Tabular Transformers Across Tables

Zifeng Wang · Jimeng Sun

Federated Learning from Pre-Trained Models: A Contrastive Learning Approach

Yue Tan · Guodong Long · Jie Ma · LU LIU · Tianyi Zhou · Jing Jiang

Multi-objective Deep Data Generation with Correlated Property Control

Shiyu Wang · Xiaojie Guo · Xuanyang Lin · Bo Pan · Yuanqi Du · Yinkai Wang · Yanfang Ye · Ashley Petersen · Austin Leitgeb · Saleh Alkhalifa · Kevin Minbiole · William M. Wuest · Amarda Shehu · Liang Zhao

Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs

Ruijie Wang · Zheng Li · Dachun Sun · Shengzhong Liu · Jinning Li · Bing Yin · Tarek Abdelzaher

The Policy-gradient Placement and Generative Routing Neural Networks for Chip Design

Ruoyu Cheng · Xianglong Lyu · Yang Li · Junjie Ye · Jianye Hao · Junchi Yan

Staircase Attention for Recurrent Processing of Sequences

Da JU · Stephen Roller · Sainbayar Sukhbaatar · Jason E Weston

On the Stability and Scalability of Node Perturbation Learning

Naoki Hiratani · Yash Mehta · Timothy Lillicrap · Peter E Latham

Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity

Abhishek Gupta · Aldo Pacchiano · Yuexiang Zhai · Sham Kakade · Sergey Levine

Doubly Robust Counterfactual Classification

Kwangho Kim · Edward Kennedy · Jose Zubizarreta

PlasticityNet: Learning to Simulate Metal, Sand, and Snow for Optimization Time Integration

Xuan Li · Yadi Cao · Minchen Li · Yin Yang · Craig Schroeder · Chenfanfu Jiang

Neural Estimation of Submodular Functions with Applications to Differentiable Subset Selection

Abir De · Soumen Chakrabarti

Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare

Shengpu Tang · Maggie Makar · Michael Sjoding · Finale Doshi-Velez · Jenna Wiens

Learning to Branch with Tree MDPs

Lara Scavuzzo · Feng Chen · Didier Chetelat · Maxime Gasse · Andrea Lodi · Neil Yorke-Smith · Karen Aardal

Generalization Bounds for Stochastic Gradient Descent via Localized $\varepsilon$-Covers

Sejun Park · Umut Simsekli · Murat Erdogdu

Global Linear and Local Superlinear Convergence of IRLS for Non-Smooth Robust Regression

Liangzu Peng · Christian Kümmerle · Rene Vidal

A Unifying Framework for Online Optimization with Long-Term Constraints

Matteo Castiglioni · Andrea Celli · Alberto Marchesi · Giulia Romano · Nicola Gatti

Beyond IID: data-driven decision-making in heterogeneous environments

Omar Besbes · Will Ma · Omar Mouchtaki

Consistent Interpolating Ensembles via the Manifold-Hilbert Kernel

Yutong Wang · Clay Scott

Optimal Efficiency-Envy Trade-Off via Optimal Transport

Steven Yin · Christian Kroer

On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL

Jinglin Chen · Aditya Modi · Akshay Krishnamurthy · Nan Jiang · Alekh Agarwal

On the Epistemic Limits of Personalized Prediction

Lucas Monteiro Paes · Carol Long · Berk Ustun · Flavio Calmon

Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees

Daniil Tiapkin · Denis Belomestny · Daniele Calandriello · Eric Moulines · Remi Munos · Alexey Naumov · Mark Rowland · Michal Valko · Pierre Ménard

Multi-Class $H$-Consistency Bounds

Pranjal Awasthi · Anqi Mao · Mehryar Mohri · Yutao Zhong

Debiased Machine Learning without Sample-Splitting for Stable Estimators

Qizhao Chen · Vasilis Syrgkanis · Morgane Austern

The Sample Complexity of One-Hidden-Layer Neural Networks

Gal Vardi · Ohad Shamir · Nati Srebro

coVariance Neural Networks

Saurabh Sihag · Gonzalo Mateos · Corey McMillan · Alejandro Ribeiro

On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach

Dennis Wei · Rahul Nair · Amit Dhurandhar · Kush Varshney · Elizabeth Daly · Moninder Singh

Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales

Tao Liu · P. R. Kumar · Ruida Zhou · Xi Liu

Adaptive Data Debiasing through Bounded Exploration

Yifan Yang · Yang Liu · Parinaz Naghizadeh

Enhancing Safe Exploration Using Safety State Augmentation

Aivar Sootla · Alexander Cowen-Rivers · Jun Wang · Haitham Bou Ammar

Leveraging the Hints: Adaptive Bidding in Repeated First-Price Auctions

Wei Zhang · Yanjun Han · Zhengyuan Zhou · Aaron Flores · Tsachy Weissman

Off-Policy Evaluation for Action-Dependent Non-stationary Environments

Yash Chandak · Shiv Shankar · Nathaniel Bastian · Bruno da Silva · Emma Brunskill · Philip Thomas

Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression

Xinmeng Huang · Yiming Chen · Wotao Yin · Kun Yuan

Bounding and Approximating Intersectional Fairness through Marginal Fairness

Mathieu Molina · Patrick Loiseau

Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints

Justin Whitehouse · Aaditya Ramdas · Steven Wu · Ryan Rogers

Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays

Konstantin Mishchenko · Francis Bach · Mathieu Even · Blake Woodworth

A Reduction to Binary Approach for Debiasing Multiclass Datasets

Ibrahim Alabdulmohsin · Jessica Schrouff · Sanmi Koyejo

Post-hoc estimators for learning to defer to an expert

Harikrishna Narasimhan · Wittawat Jitkrittum · Aditya Menon · Ankit Rawat · Sanjiv Kumar

A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases

James Harrison · Luke Metz · Jascha Sohl-Dickstein

The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift

Jingfeng Wu · Difan Zou · Vladimir Braverman · Quanquan Gu · Sham Kakade

Policy Gradient With Serial Markov Chain Reasoning

Edoardo Cetin · Oya Celiktutan

Beyond neural scaling laws: beating power law scaling via data pruning

Ben Sorscher · Robert Geirhos · Shashank Shekhar · Surya Ganguli · Ari Morcos

Left Heavy Tails and the Effectiveness of the Policy and Value Networks in DNN-based best-first search for Sokoban Planning

Dieqiao Feng · Carla Gomes · Bart Selman

Uncertainty-Aware Reinforcement Learning for Risk-Sensitive Player Evaluation in Sports Game

Guiliang Liu · Yudong Luo · Oliver Schulte · Pascal Poupart

Structure-Aware Image Segmentation with Homotopy Warping

Xiaoling Hu

PhysGNN: A Physics--Driven Graph Neural Network Based Model for Predicting Soft Tissue Deformation in Image--Guided Neurosurgery

Yasmin Salehi · Dennis Giannacopoulos

Generating Long Videos of Dynamic Scenes

Tim Brooks · Janne Hellsten · Miika Aittala · Ting-Chun Wang · Timo Aila · Jaakko Lehtinen · Ming-Yu Liu · Alexei Efros · Tero Karras

Rethinking Individual Global Max in Cooperative Multi-Agent Reinforcement Learning

Yitian Hong · Yaochu Jin · Yang Tang

Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias

Zihan Liu · Yun Luo · Lirong Wu · Zicheng Liu · Stan Z. Li

SInGE: Sparsity via Integrated Gradients Estimation of Neuron Relevance

Edouard YVINEC · Arnaud Dapogny · Matthieu Cord · Kevin Bailly

Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs

Djordje Miladinovic · Kumar Shridhar · Kushal Jain · Max Paulus · Joachim M Buhmann · Carl Allen

Torsional Diffusion for Molecular Conformer Generation

Bowen Jing · Gabriele Corso · Jeffrey Chang · Regina Barzilay · Tommi Jaakkola

Personalized Online Federated Learning with Multiple Kernels

Pouya M. Ghari · Yanning Shen

Normalizing Flows for Knockoff-free Controlled Feature Selection

Derek Hansen · Brian Manzo · Jeffrey Regier

Efficiency Ordering of Stochastic Gradient Descent

Jie Hu · Vishwaraj Doshi · Do-Young Eun

Learning Bipartite Graphs: Heavy Tails and Multiple Components

José Vinícius de Miranda Cardoso · Jiaxi Ying · Daniel Palomar

Batch Bayesian optimisation via density-ratio estimation with guarantees

Rafael Oliveira · Louis Tiao · Fabio Ramos

Discovery of Single Independent Latent Variable

Uri Shaham · Jonathan Svirsky · Ori Katz · Ronen Talmon

Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations

Michael Poli · Winnie Xu · Stefano Massaroli · Chenlin Meng · Kuno Kim · Stefano Ermon

Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction

Hansheng Xue · Vaibhav Rajan · Yu Lin

Better Uncertainty Calibration via Proper Scores for Classification and Beyond

Sebastian Gruber · Florian Buettner

Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations

Alexander Immer · Tycho van der Ouderaa · Gunnar Rätsch · Vincent Fortuin · Mark van der Wilk

Rotation-Equivariant Conditional Spherical Neural Fields for Learning a Natural Illumination Prior

James Gardner · Bernhard Egger · William Smith

Understanding Programmatic Weak Supervision via Source-aware Influence Function

Jieyu Zhang · Haonan Wang · Cheng-Yu Hsieh · Alexander Ratner

CoPur: Certifiably Robust Collaborative Inference via Feature Purification

Jing Liu · Chulin Xie · Sanmi Koyejo · Bo Li

TA-GATES: An Encoding Scheme for Neural Network Architectures

Xuefei Ning · Zixuan Zhou · Junbo Zhao · Tianchen Zhao · Yiping Deng · Changcheng Tang · Shuang Liang · Huazhong Yang · Yu Wang

MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning

Yao Lai · Yao Mu · Ping Luo

BiMLP: Compact Binary Architectures for Vision Multi-Layer Perceptrons

Yixing Xu · Xinghao Chen · Yunhe Wang

ORIENT: Submodular Mutual Information Measures for Data Subset Selection under Distribution Shift

Athresh Karanam · Krishnateja Killamsetty · Harsha Kokel · Rishabh Iyer

Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations

Ivan Marisca · Andrea Cini · Cesare Alippi

Decomposed Knowledge Distillation for Class-Incremental Semantic Segmentation

Donghyeon Baek · Youngmin Oh · Sanghoon Lee · Junghyup Lee · Bumsub Ham

Rethinking Value Function Learning for Generalization in Reinforcement Learning

Seungyong Moon · JunYeong Lee · Hyun Oh Song

Weighted Distillation with Unlabeled Examples

Fotis Iliopoulos · Vasilis Kontonis · Cenk Baykal · Gaurav Menghani · Khoa Trinh · Erik Vee

A Theoretical View on Sparsely Activated Networks

Cenk Baykal · Nishanth Dikkala · Rina Panigrahy · Cyrus Rashtchian · Xin Wang

Deep Learning Methods for Proximal Inference via Maximum Moment Restriction

Benjamin Kompa · David Bellamy · Tom Kolokotrones · james m robins · Andrew Beam

Stability and Generalization Analysis of Gradient Methods for Shallow Neural Networks

Yunwen Lei · Rong Jin · Yiming Ying

A Quantitative Geometric Approach to Neural-Network Smoothness

Zi Wang · Gautam Prakriya · Somesh Jha

Large-Scale Differentiable Causal Discovery of Factor Graphs

Romain Lopez · Jan-Christian Huetter · Jonathan Pritchard · Aviv Regev

Off-Team Learning

Brandon Cui · Hengyuan Hu · Andrei Lupu · Samuel Sokota · Jakob Foerster

Neural Basis Models for Interpretability

Filip Radenovic · Abhimanyu Dubey · Dhruv Mahajan

Semantic Exploration from Language Abstractions and Pretrained Representations

Allison Tam · Neil Rabinowitz · Andrew Lampinen · Nicholas Roy · Stephanie Chan · DJ Strouse · Jane Wang · Andrea Banino · Felix Hill

Deconfounded Representation Similarity for Comparison of Neural Networks

Tianyu Cui · Yogesh Kumar · Pekka Marttinen · Samuel Kaski

If Influence Functions are the Answer, Then What is the Question?

Juhan Bae · Nathan Ng · Alston Lo · Marzyeh Ghassemi · Roger Grosse

Early Stage Convergence and Global Convergence of Training Mildly Parameterized Neural Networks

Mingze Wang · Chao Ma

Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments

Yining Chen · Elan Rosenfeld · Mark Sellke · Tengyu Ma · Andrej Risteski

Chaotic Regularization and Heavy-Tailed Limits for Deterministic Gradient Descent

Soon Hoe Lim · Yijun Wan · Umut Simsekli

Challenging Common Assumptions in Convex Reinforcement Learning

Mirco Mutti · Riccardo De Santi · Piersilvio De Bartolomeis · Marcello Restelli

Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification

Takumi Tanabe · Rei Sato · Kazuto Fukuchi · Jun Sakuma · Youhei Akimoto

Contrastive Neural Ratio Estimation

Benjamin K Miller · Christoph Weniger · Patrick Forré

Symmetry Teleportation for Accelerated Optimization

Bo Zhao · Nima Dehmamy · Robin Walters · Rose Yu

VectorAdam for Rotation Equivariant Geometry Optimization

Selena Zihan Ling · Nicholas Sharp · Alec Jacobson

Neural Stochastic PDEs: Resolution-Invariant Learning of Continuous Spatiotemporal Dynamics

Cristopher Salvi · Maud Lemercier · Andris Gerasimovics

Constrained Predictive Coding as a Biologically Plausible Model of the Cortical Hierarchy

Siavash Golkar · Tiberiu Tesileanu · Yanis Bahroun · Anirvan Sengupta · Dmitri Chklovskii

Fault-Aware Neural Code Rankers

Jeevana Priya Inala · Chenglong Wang · Mei Yang · Andres Codas · Mark Encarnación · Shuvendu Lahiri · Madanlal Musuvathi · Jianfeng Gao

End-to-end Symbolic Regression with Transformers

Pierre-alexandre Kamienny · Stéphane d'Ascoli · Guillaume Lample · Francois Charton

Masked Prediction: A Parameter Identifiability View

Bingbin Liu · Daniel Hsu · Pradeep Ravikumar · Andrej Risteski

Moment Distributionally Robust Tree Structured Prediction

Yeshu Li · Danyal Saeed · Xinhua Zhang · Brian Ziebart · Kevin Gimpel

Inference and Sampling for Archimax Copulas

Yuting Ng · Ali Hasan · Vahid Tarokh

Operative dimensions in unconstrained connectivity of recurrent neural networks

Renate Krause · Matthew Cook · Sepp Kollmorgen · Valerio Mante · Giacomo Indiveri

Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch

Hossein Souri · Liam Fowl · Rama Chellappa · Micah Goldblum · Tom Goldstein

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Jason Wei · Xuezhi Wang · Dale Schuurmans · Maarten Bosma · brian ichter · Fei Xia · Ed Chi · Quoc V Le · Denny Zhou

Flexible Diffusion Modeling of Long Videos

William Harvey · Saeid Naderiparizi · Vaden Masrani · Christian Weilbach · Frank Wood

Free Probability for predicting the performance of feed-forward fully connected neural networks

Reda CHHAIBI · Tariq Daouda · Ezechiel Kahn

Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need?

Leon Gerard · Michael Scherbela · Philipp Marquetand · Philipp Grohs

Manifold Interpolating Optimal-Transport Flows for Trajectory Inference

Guillaume Huguet · Daniel Sumner Magruder · Alexander Tong · Oluwadamilola Fasina · Manik Kuchroo · Guy Wolf · Smita Krishnaswamy

Reinforcement Learning with a Terminator

Guy Tennenholtz · Nadav Merlis · Lior Shani · Shie Mannor · Uri Shalit · Gal Chechik · Assaf Hallak · Gal Dalal

DigGAN: Discriminator gradIent Gap Regularization for GAN Training with Limited Data

Tiantian Fang · Ruoyu Sun · Alex Schwing

Multi-Agent Reinforcement Learning is a Sequence Modeling Problem

Muning Wen · Jakub Kuba · Runji Lin · Weinan Zhang · Ying Wen · Jun Wang · Yaodong Yang

Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation

Cansu Sancaktar · Sebastian Blaes · Georg Martius

VoxGRAF: Fast 3D-Aware Image Synthesis with Sparse Voxel Grids

Katja Schwarz · Axel Sauer · Michael Niemeyer · Yiyi Liao · Andreas Geiger

Symmetry-induced Disentanglement on Graphs

Giangiacomo Mercatali · Andre Freitas · Vikas Garg

An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries

Aryan Pedawi · Pawel Gniewek · Chaoyi Chang · Brandon Anderson · Henry van den Bedem

STaR: Bootstrapping Reasoning With Reasoning

Eric Zelikman · Yuhuai Wu · Jesse Mu · Noah Goodman

Learning Physics Constrained Dynamics Using Autoencoders

Tsung-Yen Yang · Justinian Rosca · Karthik Narasimhan · Peter J Ramadge

LAMP: Extracting Text from Gradients with Language Model Priors

Mislav Balunovic · Dimitar Dimitrov · Nikola Jovanović · Martin Vechev

Imbalance Trouble: Revisiting Neural-Collapse Geometry

Christos Thrampoulidis · Ganesh Ramachandra Kini · Vala Vakilian · Tina Behnia

ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs

Limei Wang · Yi Liu · Yuchao Lin · Haoran Liu · Shuiwang Ji

Improved Imaging by Invex Regularizers with Global Optima Guarantees

Samuel Pinilla · Tingting Mu · Neil Bourne · Jeyan Thiyagalingam

Trade-off between Payoff and Model Rewards in Shapley-Fair Collaborative Machine Learning

Quoc Phong Nguyen · Bryan Kian Hsiang Low · Patrick Jaillet

Follow-the-Perturbed-Leader for Adversarial Markov Decision Processes with Bandit Feedback

Yan Dai · Haipeng Luo · Liyu Chen

Learning from Distributed Users in Contextual Linear Bandits Without Sharing the Context

Osama Hanna · Lin Yang · Christina Fragouli

Consistency of Constrained Spectral Clustering under Graph Induced Fair Planted Partitions

Shubham Gupta · Ambedkar Dukkipati

Adversarial Reprogramming Revisited

Matthias Englert · Ranko Lazic

AutoML Two-Sample Test

Jonas M. Kübler · Vincent Stimper · Simon Buchholz · Krikamol Muandet · Bernhard Schölkopf

A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning

Zhenyu Sun · Ermin Wei

Randomized Sketches for Clustering: Fast and Optimal Kernel $k$-Means

Rong Yin · Yong Liu · Weiping Wang · Dan Meng

On-Demand Sampling: Learning Optimally from Multiple Distributions

Nika Haghtalab · Michael Jordan · Eric Zhao

Constrained Langevin Algorithms with L-mixing External Random Variables

Yuping Zheng · Andrew Lamperski

Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization

Tianyi Lin · Zeyu Zheng · Michael Jordan

Fair Wrapping for Black-box Predictions

Alexander Soen · Ibrahim Alabdulmohsin · Sanmi Koyejo · Yishay Mansour · Nyalleng Moorosi · Richard Nock · Ke Sun · Lexing Xie

Do Residual Neural Networks discretize Neural Ordinary Differential Equations?

Michael Sander · Pierre Ablin · Gabriel Peyré

Recipe for a General, Powerful, Scalable Graph Transformer

Ladislav Rampášek · Michael Galkin · Vijay Prakash Dwivedi · Anh Tuan Luu · Guy Wolf · Dominique Beaini

Confident Adaptive Language Modeling

Tal Schuster · Adam Fisch · Jai Gupta · Mostafa Dehghani · Dara Bahri · Vinh Tran · Yi Tay · Donald Metzler

Are AlphaZero-like Agents Robust to Adversarial Perturbations?

Li-Cheng Lan · Huan Zhang · Ti-Rong Wu · Meng-Yu Tsai · I-Chen Wu · Cho-Jui Hsieh

Non-rigid Point Cloud Registration with Neural Deformation Pyramid

YANG LI · Tatsuya Harada

Peripheral Vision Transformer

Juhong Min · Yucheng Zhao · Chong Luo · Minsu Cho

Mind Reader: Reconstructing complex images from brain activities

Sikun Lin · Thomas Sprague · Ambuj K Singh

RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer

Jian Wang · Chenhui Gou · Qiman Wu · Haocheng Feng · Junyu Han · Errui Ding · Jingdong Wang

Few-shot Relational Reasoning via Connection Subgraph Pretraining

Qian Huang · Hongyu Ren · Jure Leskovec

ASPiRe: Adaptive Skill Priors for Reinforcement Learning

Mengda Xu · Manuela Veloso · Shuran Song

Point Transformer V2: Grouped Vector Attention and Partition-based Pooling

Xiaoyang Wu · Yixing Lao · Li Jiang · Xihui Liu · Hengshuang Zhao

M³ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design

hanxue liang · Zhiwen Fan · Rishov Sarkar · Ziyu Jiang · Tianlong Chen · Kai Zou · Yu Cheng · Cong Hao · Zhangyang Wang

Deep Compression of Pre-trained Transformer Models

Naigang Wang · Chi-Chun (Charlie) Liu · Swagath Venkataramani · Sanchari Sen · Chia-Yu Chen · Kaoutar El Maghraoui · Vijayalakshmi (Viji) Srinivasan · Leland Chang

A Variational Edge Partition Model for Supervised Graph Representation Learning

Yilin He · Chaojie Wang · Hao Zhang · Bo Chen · Mingyuan Zhou

MCMAE: Masked Convolution Meets Masked Autoencoders

Peng Gao · Teli Ma · Hongsheng Li · Ziyi Lin · Jifeng Dai · Yu Qiao

Neural-Symbolic Entangled Framework for Complex Query Answering

Zezhong Xu · Wen Zhang · Peng Ye · Hui Chen · Huajun Chen

SwinTrack: A Simple and Strong Baseline for Transformer Tracking

Liting Lin · Heng Fan · Zhipeng Zhang · Yong Xu · Haibin Ling

OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models

Xingyi He · Jiaming Sun · Yuang Wang · Di Huang · Hujun Bao · Xiaowei Zhou

Learning Neural Acoustic Fields

Andrew Luo · Yilun Du · Michael Tarr · Josh Tenenbaum · Antonio Torralba · Chuang Gan

Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation

Josh Gardner · Zoran Popovic · Ludwig Schmidt

Interpolation and Regularization for Causal Learning

Leena Chennuru Vankadara · Luca Rendsburg · Ulrike Luxburg · Debarghya Ghoshdastidar

Blackbox Attacks via Surrogate Ensemble Search

Zikui Cai · Chengyu Song · Srikanth Krishnamurthy · Amit Roy-Chowdhury · Salman Asif

Green Hierarchical Vision Transformer for Masked Image Modeling

Lang Huang · Shan You · Mingkai Zheng · Fei Wang · Chen Qian · Toshihiko Yamasaki

A Practical, Progressively-Expressive GNN

Lingxiao Zhao · Neil Shah · Leman Akoglu

Improving Neural Ordinary Differential Equations with Nesterov's Accelerated Gradient Method

Ho Huu Nghia Nguyen · Tan Nguyen · Huyen Vo · Stanley Osher · Thieu Vo

Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime

Difan Zou · Jingfeng Wu · Vladimir Braverman · Quanquan Gu · Sham Kakade

Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness

Sarah Sachs · Hedi Hadiji · Tim van Erven · Cristóbal Guzmán

DeepTOP: Deep Threshold-Optimal Policy for MDPs and RMABs

Khaled Nakhleh · I-Hong Hou

Learning Symmetric Rules with SATNet

Sangho Lim · Eun-Gyeol Oh · Hongseok Yang

Evaluation beyond Task Performance: Analyzing Concepts in AlphaZero in Hex

Charles Lovering · Jessica Forde · George Konidaris · Ellie Pavlick · Michael Littman

Distributionally Adaptive Meta Reinforcement Learning

Anurag Ajay · Abhishek Gupta · Dibya Ghosh · Sergey Levine · Pulkit Agrawal

HyperTree Proof Search for Neural Theorem Proving

Guillaume Lample · Timothee Lacroix · Marie-Anne Lachaux · Aurelien Rodriguez · Amaury Hayat · Thibaut Lavril · Gabriel Ebner · Xavier Martinet

You Only Live Once: Single-Life Reinforcement Learning

Annie Chen · Archit Sharma · Sergey Levine · Chelsea Finn

CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion

Philippe Weinzaepfel · Vincent Leroy · Thomas Lucas · Romain BRÉGIER · Yohann Cabon · Vaibhav ARORA · Leonid Antsfeld · Boris Chidlovskii · Gabriela Csurka · Jerome Revaud

Are all Frames Equal? Active Sparse Labeling for Video Action Detection

Aayush Rana · Yogesh Rawat

The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning

Vindula Jayawardana · Catherine Tang · Sirui Li · Dajiang Suo · Cathy Wu

Diffusion-LM Improves Controllable Text Generation

Xiang Li · John Thickstun · Ishaan Gulrajani · Percy Liang · Tatsunori Hashimoto

Variable-rate hierarchical CPC leads to acoustic unit discovery in speech

Santiago Cuervo · Adrian Lancucki · Ricard Marxer · Paweł Rychlikowski · Jan Chorowski

Low-rank lottery tickets: finding efficient low-rank neural networks via matrix differential equations

Steffen Schotthöfer · Emanuele Zangrando · Jonas Kusch · Gianluca Ceruti · Francesco Tudisco

Stochastic Adaptive Activation Function

Kyungsu Lee · Jaeseung Yang · Haeyun Lee · Jae Youn Hwang

Benefits of Permutation-Equivariance in Auction Mechanisms

Tian Qin · Fengxiang He · Dingfeng Shi · Wenbing Huang · Dacheng Tao

Random Sharpness-Aware Minimization

Yong Liu · Siqi Mai · Minhao Cheng · Xiangning Chen · Cho-Jui Hsieh · Yang You

On-Device Training Under 256KB Memory

Ji Lin · Ligeng Zhu · Wei-Ming Chen · Wei-Chen Wang · Chuang Gan · Song Han

Factorized-FL: Personalized Federated Learning with Parameter Factorization & Similarity Matching

Wonyong Jeong · Sung Ju Hwang

A Closer Look at Prototype Classifier for Few-shot Image Classification

Mingcheng Hou · Issei Sato

On the Strong Correlation Between Model Invariance and Generalization

Weijian Deng · Stephen Gould · Liang Zheng

“Why Not Other Classes?”: Towards Class-Contrastive Back-Propagation Explanations

Yipei Wang · Xiaoqian Wang

Parameter-Efficient Masking Networks

Yue Bai · Huan Wang · Xu Ma · Yitian Zhang · Zhiqiang Tao · Yun Fu

Make Some Noise: Reliable and Efficient Single-Step Adversarial Training

Pau de Jorge Aranda · Adel Bibi · Riccardo Volpi · Amartya Sanyal · Philip Torr · Gregory Rogez · Puneet Dokania

ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization

Qishi Dong · Awais Muhammad · Fengwei Zhou · Chuanlong Xie · Tianyang Hu · Yongxin Yang · Sung-Ho Bae · Zhenguo Li

One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement

Ning Xu · Congyu Qiao · Jiaqi Lv · Xin Geng · Min-Ling Zhang

Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters

Kamyar Ghasemipour · Shixiang (Shane) Gu · Ofir Nachum

Identifiability of deep generative models without auxiliary information

Bohdan Kivva · Goutham Rajendran · Pradeep Ravikumar · Bryon Aragam

Beyond spectral gap: the role of the topology in decentralized learning

Thijs Vogels · Hadrien Hendrikx · Martin Jaggi

Change-point Detection for Sparse and Dense Functional Data in General Dimensions

Carlos Misael Madrid Padilla · Daren Wang · Zifeng Zhao · Yi Yu

Invariant and Transportable Representations for Anti-Causal Domain Shifts

Yibo Jiang · Victor Veitch

Robust Feature-Level Adversaries are Interpretability Tools

Stephen Casper · Max Nadeau · Dylan Hadfield-Menell · Gabriel Kreiman

Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks

Agustinus Kristiadi · Runa Eschenhagen · Philipp Hennig

Anonymous Bandits for Multi-User Systems

Hossein Esfandiari · Vahab Mirrokni · Jon Schneider

Branch & Learn for Recursively and Iteratively Solvable Problems in Predict+Optimize

Xinyi Hu · Jasper Lee · Jimmy Lee · Allen Z. Zhong

Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity

Alekh Agarwal · Tong Zhang

Reproducibility in Optimization: Theoretical Framework and Limits

Kwangjun Ahn · Prateek Jain · Ziwei Ji · Satyen Kale · Praneeth Netrapalli · Gil I Shamir

Adversarial Robustness is at Odds with Lazy Training

Yunjuan Wang · Enayat Ullah · Poorya Mianjy · Raman Arora

The Curse of Unrolling: Rate of Differentiating Through Optimization

Damien Scieur · Gauthier Gidel · Quentin Bertrand · Fabian Pedregosa

On the Theoretical Properties of Noise Correlation in Stochastic Optimization

Aurelien Lucchi · Frank Proske · Antonio Orvieto · Francis Bach · Hans Kersting

Support Recovery in Sparse PCA with Incomplete Data

Hanbyul Lee · Qifan Song · Jean Honorio

Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits

Tianyuan Jin · Pan Xu · Xiaokui Xiao · Anima Anandkumar

A gradient estimator via L1-randomization for online zero-order optimization with two point feedback

Arya Akhavan · Evgenii Chzhen · Massimiliano Pontil · Alexandre Tsybakov

Hypothesis Testing for Differentially Private Linear Regression

Daniel Alabi · Salil Vadhan

Sound and Complete Causal Identification with Latent Variables Given Local Background Knowledge

Tian-Zuo Wang · Tian Qin · Zhi-Hua Zhou

One for All: Simultaneous Metric and Preference Learning over Multiple Users

Gregory Canal · Blake Mason · Ramya Korlakai Vinayak · Robert Nowak

AdaFocal: Calibration-aware Adaptive Focal Loss

Arindam Ghosh · Thomas Schaaf · Matthew Gormley

Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game

Zhiyuan Yao · Zihan Ding

Fair Ranking with Noisy Protected Attributes

Anay Mehrotra · Nisheeth Vishnoi

Off-Policy Evaluation with Deficient Support Using Side Information

Nicolò Felicioni · Maurizio Ferrari Dacrema · Marcello Restelli · Paolo Cremonesi

How to talk so AI will learn: Instructions, descriptions, and autonomy

Theodore Sumers · Robert Hawkins · Mark Ho · Tom Griffiths · Dylan Hadfield-Menell

Capturing Failures of Large Language Models via Human Cognitive Biases

Erik Jones · Jacob Steinhardt

A time-resolved theory of information encoding in recurrent neural networks

Rainer Engelken · Sven Goedeke

Learning Chaotic Dynamics in Dissipative Systems

Zongyi Li · Miguel Liu-Schiaffini · Nikola Kovachki · Kamyar Azizzadenesheli · Burigede Liu · Kaushik Bhattacharya · Andrew Stuart · Anima Anandkumar

On Elimination Strategies for Bandit Fixed-Confidence Identification

Andrea Tirinzoni · Rémy Degenne

(Optimal) Online Bipartite Matching with Degree Information

Anders Aamand · Justin Chen · Piotr Indyk

Scalable and Efficient Non-adaptive Deterministic Group Testing

Dariusz Kowalski · Dominik Pajak

Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap

Luca Pesce · Bruno Loureiro · Florent Krzakala · Lenka Zdeborová

A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation

Philip Amortila · Nan Jiang · Dhruv Madeka · Dean Foster

Sample Complexity of Learning Heuristic Functions for Greedy-Best-First and A* Search

Shinsaku Sakaue · Taihei Oki

Distributionally Robust Optimization via Ball Oracle Acceleration

Yair Carmon · Danielle Hausler

Online Bipartite Matching with Advice: Tight Robustness-Consistency Tradeoffs for the Two-Stage Model

Billy Jin · Will Ma

GraB: Finding Provably Better Data Permutations than Random Reshuffling

Yucheng Lu · Wentao Guo · Christopher De Sa

House of Cans: Covert Transmission of Internal Datasets via Capacity-Aware Neuron Steganography

Xudong Pan · Shengyao Zhang · Mi Zhang · Yifan Yan · Min Yang

A Causal Analysis of Harm

Sander Beckers · Hana Chockler · Joseph Halpern

Simple Mechanisms for Welfare Maximization in Rich Advertising Auctions

Gagan Aggarwal · Kshipra Bhawalkar · Aranyak Mehta · Divyarthi Mohan · Alexandros Psomas

On Image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and Dice

Marcus Nordstrom · Henrik Hult · Fredrik Löfman · Jonas Söderberg

Online Algorithms for the Santa Claus Problem

Max Springer · MohammadTaghi Hajiaghayi · Debmalya Panigrahi · Mohammad Khani

Learning on the Edge: Online Learning with Stochastic Feedback Graphs

Emmanuel Esposito · Federico Fusco · Dirk van der Hoeven · Nicolò Cesa-Bianchi

Optimal Dynamic Regret in LQR Control

Dheeraj Baby · Yu-Xiang Wang

Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs

Yeoneung Kim · Insoon Yang · Kwang-Sung Jun

Optimistic Mirror Descent Either Converges to Nash or to Strong Coarse Correlated Equilibria in Bimatrix Games

Ioannis Anagnostides · Gabriele Farina · Ioannis Panageas · Tuomas Sandholm

Online Convex Optimization with Hard Constraints: Towards the Best of Two Worlds and Beyond

Hengquan Guo · Xin Liu · Honghao Wei · Lei Ying

Pessimism for Offline Linear Contextual Bandits using $\ell_p$ Confidence Sets

Gene Li · Cong Ma · Nati Srebro

Mean Estimation in High-Dimensional Binary Markov Gaussian Mixture Models

Yihan Zhang · Nir Weinberger

Queue Up Your Regrets: Achieving the Dynamic Capacity Region of Multiplayer Bandits

Ilai Bistritz · Nicholas Bambos

The First Optimal Algorithm for Smooth and Strongly-Convex-Strongly-Concave Minimax Optimization

Dmitry Kovalev · Alexander Gasnikov

Beyond black box densities: Parameter learning for the deviated components

Dat Do · Nhat Ho · XuanLong Nguyen

A Best-of-Both-Worlds Algorithm for Bandits with Delayed Feedback

Saeed Masoudian · Julian Zimmert · Yevgeny Seldin

New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma

Gautam Kamath · Argyris Mouzakis · Vikrant Singhal

Policy Optimization for Markov Games: Unified Framework and Faster Convergence

Runyu Zhang · Qinghua Liu · Huan Wang · Caiming Xiong · Na Li · Yu Bai

A Damped Newton Method Achieves Global $\mathcal O \left(\frac{1}{k^2}\right)$ and Local Quadratic Convergence Rate

Slavomír Hanzely · Dmitry Kamzolov · Dmitry Pasechnyuk · Alexander Gasnikov · Peter Richtarik · Martin Takac

Local Identifiability of Deep ReLU Neural Networks: the Theory

Joachim Bona-Pellissier · François Malgouyres · Francois Bachoc

Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discovery

Wiebke Günther · Urmi Ninad · Jonas Wahl · Jakob Runge

Online Allocation and Learning in the Presence of Strategic Agents

Steven Yin · Shipra Agrawal · Assaf Zeevi

Signal Recovery with Non-Expansive Generative Network Priors

Jorio Cocola

Practical Adversarial Multivalid Conformal Prediction

Osbert Bastani · Varun Gupta · Christopher Jung · Georgy Noarov · Ramya Ramalingam · Aaron Roth

Sublinear Algorithms for Hierarchical Clustering

Arpit Agarwal · Sanjeev Khanna · Huan Li · Prathamesh Patil

Large-scale Optimization of Partial AUC in a Range of False Positive Rates

Yao Yao · Qihang Lin · Tianbao Yang

Stability Analysis and Generalization Bounds of Adversarial Training

Jiancong Xiao · Yanbo Fan · Ruoyu Sun · Jue Wang · Zhi-Quan Luo

Globally Gated Deep Linear Networks

Qianyi Li · Haim Sompolinsky

Generalization Analysis on Learning with a Concurrent Verifier

Masaaki Nishino · Kengo Nakamura · Norihito Yasuda

Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation

Zhouxing Shi · Yihan Wang · Huan Zhang · J. Zico Kolter · Cho-Jui Hsieh

Test Time Adaptation via Conjugate Pseudo-labels

Sachin Goyal · Mingjie Sun · Aditi Raghunathan · J. Zico Kolter

Best of Both Worlds Model Selection

Aldo Pacchiano · Christoph Dann · Claudio Gentile

Logical Credal Networks

Radu Marinescu · Haifeng Qian · Alexander Gray · Debarun Bhattacharjya · Francisco Barahona · Tian Gao · Ryan Riegel · Pravinda Sahu

Low-Rank Modular Reinforcement Learning via Muscle Synergy

Heng Dong · Tonghan Wang · Jiayuan Liu · Chongjie Zhang

LogiGAN: Learning Logical Reasoning via Adversarial Pre-training

Xinyu Pi · Wanjun Zhong · Yan Gao · Nan Duan · Jian-Guang Lou

Self-supervised surround-view depth estimation with volumetric feature fusion

Jung-Hee Kim · Junhwa Hur · Tien Phuoc Nguyen · Seong-Gyun Jeong

Promising or Elusive? Unsupervised Object Segmentation from Real-world Single Images

Yafei YANG · Bo Yang

Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework

Henger Li · Xiaolin Sun · Zizhan Zheng

Sparse Probabilistic Circuits via Pruning and Growing

Meihua Dang · Anji Liu · Guy Van den Broeck

Explaining Preferences with Shapley Values

Robert Hu · Siu Lun Chau · Jaime Ferrando Huertas · Dino Sejdinovic

Implications of Model Indeterminacy for Explanations of Automated Decisions

Marc-Etienne Brunet · Ashton Anderson · Richard Zemel

Self-Explaining Deviations for Coordination

Hengyuan Hu · Samuel Sokota · David Wu · Anton Bakhtin · Andrei Lupu · Brandon Cui · Jakob Foerster

A Direct Approximation of AIXI Using Logical State Abstractions

Samuel Yang-Zhao · Tianyu Wang · Kee Siong Ng

Adjoint-aided inference of Gaussian process driven differential equations

Paterne GAHUNGU · Christopher Lanyon · Mauricio A Álvarez · Engineer Bainomugisha · Michael T Smith · Richard Wilkinson

Beyond L1: Faster and Better Sparse Models with skglm

Quentin Bertrand · Quentin Klopfenstein · Pierre-Antoine Bannier · Gauthier Gidel · Mathurin Massias

Robust Imitation of a Few Demonstrations with a Backwards Model

Jung Yeon Park · Lawson Wong

Improving Policy Learning via Language Dynamics Distillation

Victor Zhong · Jesse Mu · Luke Zettlemoyer · Edward Grefenstette · Tim Rocktäschel

Monocular Dynamic View Synthesis: A Reality Check

Hang Gao · Ruilong Li · Shubham Tulsiani · Bryan Russell · Angjoo Kanazawa

VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming

Eleonora Misino · Giuseppe Marra · Emanuele Sansone

Test-Time Training with Masked Autoencoders

Yossi Gandelsman · Yu Sun · Xinlei Chen · Alexei Efros

Learning Modular Simulations for Homogeneous Systems

Jayesh Gupta · Sai Vemprala · Ashish Kapoor

Flowification: Everything is a normalizing flow

Bálint Máté · Samuel Klein · Tobias Golling · François Fleuret

Focal Modulation Networks

Jianwei Yang · Chunyuan Li · Xiyang Dai · Jianfeng Gao

OpenAUC: Towards AUC-Oriented Open-Set Recognition

Zitai Wang · Qianqian Xu · Zhiyong Yang · Yuan He · Xiaochun Cao · Qingming Huang

Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations

Jeff Z. HaoChen · Colin Wei · Ananya Kumar · Tengyu Ma

Contrastive Adapters for Foundation Model Group Robustness

Michael Zhang · Christopher Ré

Two-layer neural network on infinite dimensional data: global optimization guarantee in the mean-field regime

Naoki Nishikawa · Taiji Suzuki · Atsushi Nitanda · Denny Wu

Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective

Bohang Zhang · Du Jiang · Di He · Liwei Wang

Riemannian Neural SDE: Learning Stochastic Representations on Manifolds

Sung Woo Park · Hyomin Kim · Kyungjae Lee · Junseok Kwon

Discovering Design Concepts for CAD Sketches

Yuezhi Yang · Hao Pan

🏘️ ProcTHOR: Large-Scale Embodied AI Using Procedural Generation

Matt Deitke · Eli VanderBilt · Alvaro Herrasti · Luca Weihs · Kiana Ehsani · Jordi Salvador · Winson Han · Eric Kolve · Aniruddha Kembhavi · Roozbeh Mottaghi

Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?

Rishi Bommasani · Kathleen A. Creel · Ananya Kumar · Dan Jurafsky · Percy Liang

Risk-Driven Design of Perception Systems

Anthony Corso · Sydney Katz · Craig Innes · Xin Du · Subramanian Ramamoorthy · Mykel J Kochenderfer

Finding Differences Between Transformers and ConvNets Using Counterfactual Simulation Testing

Nataniel Ruiz · Sarah Bargal · Cihang Xie · Kate Saenko · Stan Sclaroff

On the Generalization Power of the Overfitted Three-Layer Neural Tangent Kernel Model

Peizhong Ju · Xiaojun Lin · Ness Shroff

Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuits

Kaining Zhang · Liu Liu · Min-Hsiu Hsieh · Dacheng Tao

On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds

Biraj Dahal · Alexander Havrilla · Minshuo Chen · Tuo Zhao · Wenjing Liao

Constants of motion network

Muhammad Firmansyah Kasim · Yi Heng Lim

Graph Scattering beyond Wavelet Shackles

Christian Koke · Gitta Kutyniok

On Margins and Generalisation for Voting Classifiers

Felix Biggs · Valentina Zantedeschi · Benjamin Guedj

Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality

Teodor Vanislavov Marinov · Mehryar Mohri · Julian Zimmert

Learning Infinite-Horizon Average-Reward Restless Multi-Action Bandits via Index Awareness

GUOJUN XIONG · Shufan Wang · Jian Li

Conservative Dual Policy Optimization for Efficient Model-Based Reinforcement Learning

Shenao Zhang

Provable Defense against Backdoor Policies in Reinforcement Learning

Shubham Bharti · Xuezhou Zhang · Adish Singla · Jerry Zhu

Cost-efficient Gaussian tensor network embeddings for tensor-structured inputs

Linjian Ma · Edgar Solomonik

Online Learning and Pricing for Network Revenue Management with Reusable Resources

Huiwen Jia · Cong Shi · Siqian Shen

MultiScan: Scalable RGBD scanning for 3D environments with articulated objects

Yongsen Mao · Yiming Zhang · Hanxiao Jiang · Angel Chang · Manolis Savva

FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning

Tao Qi · Fangzhao Wu · Chuhan Wu · Lingjuan Lyu · Tong Xu · Hao Liao · Zhongliang Yang · Yongfeng Huang · Xing Xie

DeepFoids: Adaptive Bio-Inspired Fish Simulation with Deep Reinforcement Learning

Yuko Ishiwaka · Xiao Zeng · Shun Ogawa · Donovan Westwater · Tadayuki Tone · Masaki Nakada

Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty

Luning Sun · Daniel Huang · Hao Sun · Jian-Xun Wang

Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective

Rongzhe Wei · Haoteng YIN · Junteng Jia · Austin Benson · Pan Li

Graph Reordering for Cache-Efficient Near Neighbor Search

Benjamin Coleman · Santiago Segarra · Alexander Smola · Anshumali Shrivastava

Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness

Avrim Blum · Omar Montasser · Greg Shakhnarovich · Hongyang Zhang

The Gyro-Structure of Some Matrix Manifolds

Xuan Son Nguyen

Revisit last-iterate convergence of mSGD under milder requirement on step size

ruinan Jin · Xingkang He · Lang Chen · Difei Cheng · Vijay Gupta

Robustness Analysis of Video-Language Models Against Visual and Language Perturbations

Madeline Chantry · Shruti Vyas · Hamid Palangi · Yogesh Rawat · Vibhav Vineet

Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs

Seiji Maekawa · Koki Noda · Yuya Sasaki · makoto onizuka

AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation

Yuanfeng Ji · Haotian Bai · Chongjian GE · Jie Yang · Ye Zhu · Ruimao Zhang · Zhen Li · Lingyan Zhanng · Wanling Ma · Xiang Wan · Ping Luo

JAHS-Bench-201: A Foundation For Research On Joint Architecture And Hyperparameter Search

Archit Bansal · Danny Stoll · Maciej Janowski · Arber Zela · Frank Hutter

Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time

Huaxiu Yao · Caroline Choi · Bochuan Cao · Yoonho Lee · Pang Wei Koh · Chelsea Finn

Towards Better Evaluation for Dynamic Link Prediction

Farimah Poursafaei · Shenyang Huang · Kellin Pelrine · Reihaneh Rabbany

Avalon: A Benchmark for RL Generalization Using Procedurally Generated Worlds

Joshua Albrecht · Abraham Fetterman · Bryden Fogelman · Ellie Kitanidis · Bartosz Wróblewski · Nicole Seo · Michael Rosenthal · Maksis Knutins · Zack Polizzi · James Simon · Kanjun Qiu

GriddlyJS: A Web IDE for Reinforcement Learning

Christopher Bamford · Minqi Jiang · Mikayel Samvelyan · Tim Rocktäschel

AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies

Li Siyao · Yuhang Li · Bo Li · Chao Dong · Ziwei Liu · Chen Change Loy

VLMbench: A Compositional Benchmark for Vision-and-Language Manipulation

Kaizhi Zheng · Xiaotong Chen · Odest Chadwicke Jenkins · Xin Wang

OpenFilter: A Framework to Democratize Research Access to Social Media AR Filters

Piera Riccio · Bill Psomas · Francesco Galati · Francisco Escolano · Thomas Hofmann · Nuria Oliver

Forecasting Future World Events With Neural Networks

Andy Zou · Tristan Xiao · Ryan Jia · Joe Kwon · Mantas Mazeika · Richard Li · Dawn Song · Jacob Steinhardt · Owain Evans · Dan Hendrycks

Active-Passive SimStereo - Benchmarking the Cross-Generalization Capabilities of Deep Learning-based Stereo Methods

Laurent Jospin · Allen Antony · Lian Xu · Hamid Laga · Farid Boussaid · Mohammed Bennamoun

TAP-Vid: A Benchmark for Tracking Any Point in a Video

Carl Doersch · Ankush Gupta · Larisa Markeeva · Adria Recasens · Lucas Smaira · Yusuf Aytar · Joao Carreira · Andrew Zisserman · Yi Yang

EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records

Gyubok Lee · Hyeonji Hwang · Seongsu Bae · Yeonsu Kwon · Woncheol Shin · Seongjun Yang · Minjoon Seo · Jong-Yeup Kim · Edward Choi

IKEA-Manual: Seeing Shape Assembly Step by Step

Ruocheng Wang · Yunzhi Zhang · Jiayuan Mao · Ran Zhang · Chin-Yi Cheng · Jiajun Wu

Model Zoos: A Dataset of Diverse Populations of Neural Network Models

Konstantin Schürholt · Diyar Taskiran · Boris Knyazev · Xavier Giró-i-Nieto · Damian Borth

mRI: Multi-modal 3D Human Pose Estimation Dataset using mmWave, RGB-D, and Inertial Sensors

Sizhe An · Yin Li · Umit Ogras

A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement Prediction

Wonseok Hwang · Dongjun Lee · Kyoungyeon Cho · Hanuhl Lee · Minjoon Seo

Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models

Maribeth Rauh · John Mellor · Jonathan Uesato · Po-Sen Huang · Johannes Welbl · Laura Weidinger · Sumanth Dathathri · Amelia Glaese · Geoffrey Irving · Iason Gabriel · William Isaac · Lisa Anne Hendricks

A Large Scale Search Dataset for Unbiased Learning to Rank

Lixin Zou · Haitao Mao · Xiaokai Chu · Jiliang Tang · Wenwen Ye · Shuaiqiang Wang · Dawei Yin

Open High-Resolution Satellite Imagery: The WorldStrat Dataset – With Application to Super-Resolution

Julien Cornebise · Ivan Oršolić · Freddie Kalaitzis

Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization

Wenhao Gao · Tianfan Fu · Jimeng Sun · Connor Coley

MSDS: A Large-Scale Chinese Signature and Token Digit String Dataset for Handwriting Verification

Peirong Zhang · Jiajia Jiang · Yuliang Liu · Lianwen Jin

Truncated Emphatic Temporal Difference Methods for Prediction and Control

Shangtong Zhang · Shimon Whiteson

IALE: Imitating Active Learner Ensembles

Christoffer Löffler · Christopher Mutschler

Fast and Robust Rank Aggregation against Model Misspecification

YUANGANG PAN · Ivor W. Tsang · Weijie Chen · Gang Niu · Masashi Sugiyama

All You Need is a Good Functional Prior for Bayesian Deep Learning

Ba-Hien Tran · Simone Rossi · Dimitrios Milios · Maurizio Filippone

[Re] Solving Phase Retrieval With a Learned Reference

Nick Rucks · Tobias Uelwer · Stefan Harmeling

[Re] Strategic classification made practical: reproduction

Guilly Kolkman · Jan Athmer · Alex Labro · Maksymilian Kulicki

[Re] Graph Edit Networks

Vid Stropnik · Maruša Oražem

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Invited Talk

Blueprint for an AI Bill of Rights Making Automated Systems Work for the American People

Alondra Nelson

2:30 PM - 3:30 PM

Among the great challenges posed to democracy today is the use of technology, data, and automated systems in ways that threaten the rights of the American public. Too often, these tools are used to limit our opportunities and prevent our access to critical resources or services. These problems are well documented. In America and around the world, systems supposed to help with patient care have proven unsafe, ineffective, or biased. Algorithms used in hiring and credit decisions have been found to reflect and reproduce existing unwanted inequities or embed new harmful bias and discrimination. Unchecked social media data collection has been used to threaten people’s opportunities, undermine their privacy, or pervasively track their activity—often without their knowledge or consent.

These outcomes are deeply harmful—but they are not inevitable. Automated systems have brought about extraordinary benefits, from technology that helps farmers grow food more efficiently and computers that predict storm paths, to algorithms that can identify diseases in patients. These tools now drive important decisions across sectors, while data is helping to revolutionize global industries. Fueled by the power of American innovation, these tools hold the potential to redefine every part of our society and make life better for everyone.

This important progress must not come at the price of civil rights or democratic values, foundational American principles that President Biden has affirmed as a cornerstone of his Administration. To advance President Biden’s vision, the White House Office of Science and Technology Policy has identified five principles that should guide the design, use, and deployment of automated systems to protect the American public in the age of artificial intelligence.

The Blueprint for an AI Bill of Rights is a guide for a society that protects all people from these threats—and uses technologies in ways that reinforce our highest values. Responding to the experiences of the American public, and informed by insights from researchers, technologists, advocates, journalists, and policymakers, this framework is accompanied by From Principles to Practice—a handbook for anyone seeking to incorporate these protections into policy and practice, including detailed steps toward actualizing these principles in the technological design process. These principles help provide guidance whenever automated systems can meaningfully impact the public’s rights, opportunities, or access to critical needs.

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Speaker Bio

Alondra Nelson, Ph.D., (NAM) is the Harold F. Linder Professor at the Institute for Advanced Study. She currently serves as Deputy Assistant to the President and Deputy Director for Science and Society in the White House Office of Science and Technology Policy, where she performed the duties of the Director from February to October 2022. Dr. Nelson is most widely known for her research at the intersection of science, technology, medicine, and social inequality, and as the acclaimed author of award-winning books, including The Social Life of DNA: Race, Reparations, and Reconciliation after the Genome (2016); Body and Soul: The Black Panther Party and the Fight against Medical Discrimination (2011); Genetics and the Unsettled Past: The Collision of DNA, Race, and History (2012; with Keith Wailoo and Catherine Lee); and Technicolor: Race, Technology, and Everyday Life (2001; with Thuy Linh Tu). Before joining the Biden Administration, Nelson was co-chair of the National Academy of Medicine Committee on Emerging Science, Technology, and Innovation and was a member of the National Academy of Engineering Committee on Responsible Computing Research. She served as a past president of the Social Science Research Council, an international research nonprofit, and was previously the inaugural Dean of Social Science at Columbia University. Dr. Nelson began her academic career on the faculty of Yale University, and there was recognized with the Poorvu Prize for interdisciplinary teaching excellence. Dr. Nelson is an elected member of the National Academy of Medicine, the American Academy of Arts and Sciences, the American Philosophical Society, the American Association for the Advancement of Science, and the American Academy of Political and Social Science.

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Poster

Poster Session 4

4:00 PM - 6:00 PM

413 Events in this session

Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data

Harsh Rangwani · Sumukh K Aithal · Mayank Mishra · Venkatesh Babu R

Active Learning Through a Covering Lens

Ofer Yehuda · Avihu Dekel · Guy Hacohen · Daphna Weinshall

Sparse Winning Tickets are Data-Efficient Image Recognizers

Mukund Varma T · Xuxi Chen · Zhenyu Zhang · Tianlong Chen · Subhashini Venugopalan · Zhangyang Wang

Active Learning Helps Pretrained Models Learn the Intended Task

Alex Tamkin · Dat Nguyen · Salil Deshpande · Jesse Mu · Noah Goodman

Star Temporal Classification: Sequence Modeling with Partially Labeled Data

Vineel Pratap · Awni Hannun · Gabriel Synnaeve · Ronan Collobert

S-PIFu: Integrating Parametric Human Models with PIFu for Single-view Clothed Human Reconstruction

Kennard Chan · Guosheng Lin · Haiyu Zhao · Weisi Lin

Interaction-Grounded Learning with Action-Inclusive Feedback

Tengyang Xie · Akanksha Saran · Dylan J Foster · Lekan Molu · Ida Momennejad · Nan Jiang · Paul Mineiro · John Langford

Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm

Ashish K Jayant · Shalabh Bhatnagar

NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matching

Souhaib Attaiki · Maks Ovsjanikov

Environment Diversification with Multi-head Neural Network for Invariant Learning

Bo-Wei Huang · Keng-Te Liao · Chang-Sheng Kao · Shou-De Lin

Denoising Diffusion Restoration Models

Bahjat Kawar · Michael Elad · Stefano Ermon · Jiaming Song

Predictive Coding beyond Gaussian Distributions

Luca Pinchetti · Tommaso Salvatori · Yordan Yordanov · Beren Millidge · Yuhang Song · Thomas Lukasiewicz

Trajectory balance: Improved credit assignment in GFlowNets

Nikolay Malkin · Moksh Jain · Emmanuel Bengio · Chen Sun · Yoshua Bengio

The least-control principle for local learning at equilibrium

Alexander Meulemans · Nicolas Zucchet · Seijin Kobayashi · Johannes von Oswald · João Sacramento

Universally Expressive Communication in Multi-Agent Reinforcement Learning

Matthew Morris · Thomas D Barrett · Arnu Pretorius

Introspective Learning : A Two-Stage approach for Inference in Neural Networks

Mohit Prabhushankar · Ghassan AlRegib

Exploit Reward Shifting in Value-Based Deep-RL: Optimistic Curiosity-Based Exploration and Conservative Exploitation via Linear Reward Shaping

Hao Sun · Lei Han · Rui Yang · Xiaoteng Ma · Jian Guo · Bolei Zhou

Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis

Wuyang Chen · Wei Huang · Xinyu Gong · Boris Hanin · Zhangyang Wang

ToDD: Topological Compound Fingerprinting in Computer-Aided Drug Discovery

Andaç Demir · Baris Coskunuzer · Yulia Gel · Ignacio Segovia-Dominguez · Yuzhou Chen · Bulent Kiziltan

Spartan: Differentiable Sparsity via Regularized Transportation

Kai Sheng Tai · Taipeng Tian · Ser Nam Lim

S3GC: Scalable Self-Supervised Graph Clustering

Fnu Devvrit · Aditya Sinha · Inderjit Dhillon · Prateek Jain

HUMUS-Net: Hybrid Unrolled Multi-scale Network Architecture for Accelerated MRI Reconstruction

Zalan Fabian · Berk Tinaz · Mahdi Soltanolkotabi

DreamShard: Generalizable Embedding Table Placement for Recommender Systems

Daochen Zha · Louis Feng · Qiaoyu Tan · Zirui Liu · Kwei-Herng Lai · Bhargav Bhushanam · Yuandong Tian · Arun Kejariwal · Xia Hu

Navigating Memory Construction by Global Pseudo-Task Simulation for Continual Learning

Yejia Liu · Wang Zhu · Shaolei Ren

Graph Learning Assisted Multi-Objective Integer Programming

Yaoxin Wu · Wen Song · Zhiguang Cao · Jie Zhang · Abhishek Gupta · Mingyan Lin

CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations

Kai Yan · Alex Schwing · Yu-Xiong Wang

Roadblocks for Temporarily Disabling Shortcuts and Learning New Knowledge

Hongjing Niu · Hanting Li · Feng Zhao · Bin Li

Contrastive Graph Structure Learning via Information Bottleneck for Recommendation

Chunyu Wei · Jian Liang · Di Liu · Fei Wang

Mining Multi-Label Samples from Single Positive Labels

Youngin Cho · Daejin Kim · MOHAMMAD AZAM KHAN · Jaegul Choo

Reinforced Genetic Algorithm for Structure-based Drug Design

Tianfan Fu · Wenhao Gao · Connor Coley · Jimeng Sun

Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport

Dandan Guo · Long Tian · He Zhao · Mingyuan Zhou · Hongyuan Zha

CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP

Andreas Fürst · Elisabeth Rumetshofer · Johannes Lehner · Viet T. Tran · Fei Tang · Hubert Ramsauer · David Kreil · Michael Kopp · Günter Klambauer · Angela Bitto · Sepp Hochreiter

Class-Aware Adversarial Transformers for Medical Image Segmentation

Chenyu You · Ruihan Zhao · Fenglin Liu · Siyuan Dong · Sandeep Chinchali · Ufuk Topcu · Lawrence Staib · James Duncan

Debiased Self-Training for Semi-Supervised Learning

Baixu Chen · Junguang Jiang · Ximei Wang · Pengfei Wan · Jianmin Wang · Mingsheng Long

Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning

WEICONG LIANG · YUHUI YUAN · Henghui Ding · Xiao Luo · Weihong Lin · Ding Jia · Zheng Zhang · Chao Zhang · Han Hu

Single-Stage Visual Relationship Learning using Conditional Queries

Alakh Desai · Tz-Ying Wu · Subarna Tripathi · Nuno Vasconcelos

Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging

Yuanhao Cai · Jing Lin · Haoqian Wang · Xin Yuan · Henghui Ding · Yulun Zhang · Radu Timofte · Luc V Gool

DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection

Lewei Yao · Jianhua Han · Youpeng Wen · Xiaodan Liang · Dan Xu · Wei Zhang · Zhenguo Li · Chunjing XU · Hang Xu

OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds

Ziyang Song · Bo Yang

Physically-Based Face Rendering for NIR-VIS Face Recognition

Yunqi Miao · Alexandros Lattas · Jiankang Deng · Jungong Han · Stefanos Zafeiriou

HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces

Qing Li · Yu-Shen Liu · Jin-San Cheng · Cheng Wang · Yi Fang · Zhizhong Han

Scaling Multimodal Pre-Training via Cross-Modality Gradient Harmonization

Junru Wu · Yi Liang · feng han · Hassan Akbari · Zhangyang Wang · Cong Yu

Deep Bidirectional Language-Knowledge Graph Pretraining

Michihiro Yasunaga · Antoine Bosselut · Hongyu Ren · Xikun Zhang · Christopher D Manning · Percy Liang · Jure Leskovec

Vision Transformers provably learn spatial structure

Samy Jelassi · Michael Sander · Yuanzhi Li

Rethinking and Improving Robustness of Convolutional Neural Networks: a Shapley Value-based Approach in Frequency Domain

Yiting Chen · Qibing Ren · Junchi Yan

Self-Supervised Fair Representation Learning without Demographics

Junyi Chai · Xiaoqian Wang

Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing

Yonggan Fu · Yang Zhang · Kaizhi Qian · Zhifan Ye · Zhongzhi Yu · Cheng-I Jeff Lai · Celine Lin

Sequence-to-Set Generative Models

Longtao Tang · Ying Zhou · Yu Yang

Does GNN Pretraining Help Molecular Representation?

Ruoxi Sun · Hanjun Dai · Adams Wei Yu

Visual correspondence-based explanations improve AI robustness and human-AI team accuracy

Mohammad Reza Taesiri · Giang Nguyen · Anh Nguyen

Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens

Elad Ben Avraham · Roei Herzig · Karttikeya Mangalam · Amir Bar · Anna Rohrbach · Leonid Karlinsky · Trevor Darrell · Amir Globerson

Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design

Jörg Franke · Frederic Runge · Frank Hutter

Transformers from an Optimization Perspective

Yongyi Yang · zengfeng Huang · David P Wipf

Human-Robotic Prosthesis as Collaborating Agents for Symmetrical Walking

Ruofan Wu · Junmin Zhong · Brent Wallace · Xiang Gao · He Huang · Jennie Si

Locating and Editing Factual Associations in GPT

Kevin Meng · David Bau · Alex Andonian · Yonatan Belinkov

Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantification

Ruoxi Jiang · Rebecca Willett

Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics

Lukas Prantl · Benjamin Ummenhofer · Vladlen Koltun · Nils Thuerey

DOMINO: Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement Learning

Yao Mu · Yuzheng Zhuang · Fei Ni · Bin Wang · Jianyu Chen · Jianye Hao · Ping Luo

UniGAN: Reducing Mode Collapse in GANs using a Uniform Generator

Ziqi Pan · Li Niu · Liqing Zhang

On the Convergence Theory for Hessian-Free Bilevel Algorithms

Daouda Sow · Kaiyi Ji · Yingbin Liang

Meta-Auto-Decoder for Solving Parametric Partial Differential Equations

Xiang Huang · Zhanhong Ye · Hongsheng Liu · Shi Ji · Zidong Wang · Kang Yang · Yang Li · Min Wang · Haotian CHU · Fan Yu · Bei Hua · Lei Chen · Bin Dong

Deep Active Learning by Leveraging Training Dynamics

Haonan Wang · Wei Huang · Ziwei Wu · Hanghang Tong · Andrew J Margenot · Jingrui He

Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence

Sunghwan Hong · Jisu Nam · Seokju Cho · Susung Hong · Sangryul Jeon · Dongbo Min · Seungryong Kim

Invertible Monotone Operators for Normalizing Flows

Byeongkeun Ahn · Chiyoon Kim · Youngjoon Hong · Hyunwoo Kim

S$^3$-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint

Wenqi Yang · Guanying Chen · Chaofeng Chen · Zhenfang Chen · Kwan-Yee K. Wong

Cross-Linked Unified Embedding for cross-modality representation learning

Xinming Tu · Zhi-Jie Cao · xia chenrui · Sara Mostafavi · Ge Gao

u-HuBERT: Unified Mixed-Modal Speech Pretraining And Zero-Shot Transfer to Unlabeled Modality

Wei-Ning Hsu · Bowen Shi

A Closer Look at the Adversarial Robustness of Deep Equilibrium Models

Zonghan Yang · Tianyu Pang · Yang Liu

A Transformer-Based Object Detector with Coarse-Fine Crossing Representations

Zhishan Li · Ying Nie · Kai Han · Jianyuan Guo · Lei Xie · Yunhe Wang

Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks

Daiki Chijiwa · Shin'ya Yamaguchi · Atsutoshi Kumagai · Yasutoshi Ida

Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant

Ying Jin · Jiaqi Wang · Dahua Lin

BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis

Yichong Leng · Zehua Chen · Junliang Guo · Haohe Liu · Jiawei Chen · Xu Tan · Danilo Mandic · Lei He · Xiangyang Li · Tao Qin · sheng zhao · Tie-Yan Liu

Deep Attentive Belief Propagation: Integrating Reasoning and Learning for Solving Constraint Optimization Problems

Yanchen Deng · Shufeng Kong · Caihua Liu · Bo An

Robust Calibration with Multi-domain Temperature Scaling

Yaodong Yu · Stephen Bates · Yi Ma · Michael Jordan

CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks

Xuanli He · Qiongkai Xu · Yi Zeng · Lingjuan Lyu · Fangzhao Wu · Jiwei Li · Ruoxi Jia

Data-Efficient Augmentation for Training Neural Networks

Tian Yu Liu · Baharan Mirzasoleiman

Learning Distinct and Representative Modes for Image Captioning

Qi Chen · Chaorui Deng · Qi Wu

GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs

Zenan Li · Qitian Wu · Fan Nie · Junchi Yan

Bootstrapped Transformer for Offline Reinforcement Learning

Kerong Wang · Hanye Zhao · Xufang Luo · Kan Ren · Weinan Zhang · Dongsheng Li

INRAS: Implicit Neural Representation for Audio Scenes

Kun Su · Mingfei Chen · Eli Shlizerman

AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness

Dacheng Li · Hongyi Wang · Eric Xing · Hao Zhang

On the Symmetries of Deep Learning Models and their Internal Representations

Charles Godfrey · Davis Brown · Tegan Emerson · Henry Kvinge

Gradient Descent: The Ultimate Optimizer

Kartik Chandra · Audrey Xie · Jonathan Ragan-Kelley · ERIK MEIJER

Maximizing Revenue under Market Shrinkage and Market Uncertainty

Maria-Florina Balcan · Siddharth Prasad · Tuomas Sandholm

Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations

Andreas Besginow · Markus Lange-Hegermann

Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions

Nikolaos Karalias · Joshua Robinson · Andreas Loukas · Stefanie Jegelka

In Defense of the Unitary Scalarization for Deep Multi-Task Learning

Vitaly Kurin · Alessandro De Palma · Ilya Kostrikov · Shimon Whiteson · Pawan K Mudigonda

A Unified Analysis of Federated Learning with Arbitrary Client Participation

Shiqiang Wang · Mingyue Ji

DNA: Proximal Policy Optimization with a Dual Network Architecture

Matthew Aitchison · Penny Sweetser

Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent

Ruqi Zhang · Qiang Liu · Xin Tong

Sampling without Replacement Leads to Faster Rates in Finite-Sum Minimax Optimization

Aniket Das · Bernhard Schölkopf · Michael Muehlebach

Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs

Andrea Tirinzoni · Aymen Al Marjani · Emilie Kaufmann

Gradient Descent Is Optimal Under Lower Restricted Secant Inequality And Upper Error Bound

Charles Guille-Escuret · Adam Ibrahim · Baptiste Goujaud · Ioannis Mitliagkas

Posterior and Computational Uncertainty in Gaussian Processes

Jonathan Wenger · Geoff Pleiss · Marvin Pförtner · Philipp Hennig · John Cunningham

Luckiness in Multiscale Online Learning

Wouter Koolen · Muriel F. Pérez-Ortiz

Learning-Augmented Algorithms for Online Linear and Semidefinite Programming

Elena Grigorescu · Young-San Lin · Sandeep Silwal · Maoyuan Song · Samson Zhou

Dynamic Pricing with Monotonicity Constraint under Unknown Parametric Demand Model

Su Jia · Andrew Li · R Ravi

An Asymptotically Optimal Batched Algorithm for the Dueling Bandit Problem

Arpit Agarwal · Rohan Ghuge · viswanath nagarajan

Precise Regret Bounds for Log-loss via a Truncated Bayesian Algorithm

Changlong Wu · Mohsen Heidari · Ananth Grama · Wojciech Szpankowski

The Neural Covariance SDE: Shaped Infinite Depth-and-Width Networks at Initialization

Mufan Li · Mihai Nica · Dan Roy

Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks

Blake Bordelon · Cengiz Pehlevan

Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes

Maxim Kodryan · Ekaterina Lobacheva · Maksim Nakhodnov · Dmitry Vetrov

A Data-Augmentation Is Worth A Thousand Samples: Analytical Moments And Sampling-Free Training

Randall Balestriero · Ishan Misra · Yann LeCun

Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis

Ronan Perry · Julius von Kügelgen · Bernhard Schölkopf

Maximizing and Satisficing in Multi-armed Bandits with Graph Information

Parth Thaker · Mohit Malu · Nikhil Rao · Gautam Dasarathy

Thompson Sampling Efficiently Learns to Control Diffusion Processes

Mohamad Kazem Shirani Faradonbeh · Mohamad Sadegh Shirani Faradonbeh · Mohsen Bayati

Counterfactual Fairness with Partially Known Causal Graph

Aoqi Zuo · Susan Wei · Tongliang Liu · Bo Han · Kun Zhang · Mingming Gong

Non-Convex Bilevel Games with Critical Point Selection Maps

Michael Arbel · Julien Mairal

Provably tuning the ElasticNet across instances

Maria-Florina Balcan · Misha Khodak · Dravyansh Sharma · Ameet Talwalkar

On Learning and Refutation in Noninteractive Local Differential Privacy

Alexander Edmonds · Aleksandar Nikolov · Toniann Pitassi

Learning in Observable POMDPs, without Computationally Intractable Oracles

Noah Golowich · Ankur Moitra · Dhruv Rohatgi

On the Robustness of Graph Neural Diffusion to Topology Perturbations

Yang Song · Qiyu Kang · Sijie Wang · Kai Zhao · Wee Peng Tay

Robust Learning against Relational Adversaries

Yizhen Wang · Mohannad Alhanahnah · Xiaozhu Meng · Ke Wang · Mihai Christodorescu · Somesh Jha

Toward Understanding Privileged Features Distillation in Learning-to-Rank

Shuo Yang · Sujay Sanghavi · Holakou Rahmanian · Jan Bakus · Vishwanathan S. V. N.

Lifting Weak Supervision To Structured Prediction

Harit Vishwakarma · Frederic Sala

Meta Reinforcement Learning with Finite Training Tasks - a Density Estimation Approach

Zohar Rimon · Aviv Tamar · Gilad Adler

Symbolic Distillation for Learned TCP Congestion Control

S P Sharan · Wenqing Zheng · Kuo-Feng Hsu · Jiarong Xing · Ang Chen · Zhangyang Wang

On the Effectiveness of Persistent Homology

Renata Turkes · Guido Montufar · Nina Otter

Fairness without Demographics through Knowledge Distillation

Junyi Chai · Taeuk Jang · Xiaoqian Wang

Pre-Trained Model Reusability Evaluation for Small-Data Transfer Learning

Yao-Xiang Ding · Xi-Zhu Wu · Kun Zhou · Zhi-Hua Zhou

HSDF: Hybrid Sign and Distance Field for Modeling Surfaces with Arbitrary Topologies

Li Wang · jie Yang · Weikai Chen · Xiaoxu Meng · Bo Yang · Jintao Li · Lin Gao

Where to Pay Attention in Sparse Training for Feature Selection?

Ghada Sokar · Zahra Atashgahi · Mykola Pechenizkiy · Decebal Constantin Mocanu

An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects

Thanh Vinh Vo · Arnab Bhattacharyya · Young Lee · Tze-Yun Leong

RényiCL: Contrastive Representation Learning with Skew Rényi Divergence

Kyungmin Lee · Jinwoo Shin

Look where you look! Saliency-guided Q-networks for generalization in visual Reinforcement Learning

David Bertoin · Adil Zouitine · Mehdi Zouitine · Emmanuel Rachelson

On Embeddings for Numerical Features in Tabular Deep Learning

Yury Gorishniy · Ivan Rubachev · Artem Babenko

SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery

Yezhen Cong · Samar Khanna · Chenlin Meng · Patrick Liu · Erik Rozi · Yutong He · Marshall Burke · David Lobell · Stefano Ermon

(De-)Randomized Smoothing for Decision Stump Ensembles

Miklós Horváth · Mark Müller · Marc Fischer · Martin Vechev

Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning

Yongyuan Liang · Yanchao Sun · Ruijie Zheng · Furong Huang

Wasserstein $K$-means for clustering probability distributions

Yubo Zhuang · Xiaohui Chen · Yun Yang

Coordinate Linear Variance Reduction for Generalized Linear Programming

Chaobing Song · Cheuk Yin Lin · Stephen Wright · Jelena Diakonikolas

Proximal Point Imitation Learning

Luca Viano · Angeliki Kamoutsi · Gergely Neu · Igor Krawczuk · Volkan Cevher

Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation

Viktor Bengs · Eyke Hüllermeier · Willem Waegeman

Why neural networks find simple solutions: The many regularizers of geometric complexity

Benoit Dherin · Michael Munn · Mihaela Rosca · David Barrett

Learning Articulated Rigid Body Dynamics with Lagrangian Graph Neural Network

Ravinder Bhattoo · Sayan Ranu · N M Anoop Krishnan

Learning interacting dynamical systems with latent Gaussian process ODEs

Çağatay Yıldız · Melih Kandemir · Barbara Rakitsch

Capturing Graphs with Hypo-Elliptic Diffusions

Csaba Toth · Darrick Lee · Celia Hacker · Harald Oberhauser

MOVE: Unsupervised Movable Object Segmentation and Detection

Adam Bielski · Paolo Favaro

3DILG: Irregular Latent Grids for 3D Generative Modeling

Biao Zhang · Matthias Niessner · Peter Wonka

Unsupervised Multi-Object Segmentation by Predicting Probable Motion Patterns

Laurynas Karazija · Subhabrata Choudhury · Iro Laina · Christian Rupprecht · Andrea Vedaldi

Learning Concept Credible Models for Mitigating Shortcuts

Jiaxuan Wang · Sarah Jabbour · Maggie Makar · Michael Sjoding · Jenna Wiens

Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning

Byungchan Ko · Jungseul Ok

BayesPCN: A Continually Learnable Predictive Coding Associative Memory

Jinsoo Yoo · Frank Wood

DeVRF: Fast Deformable Voxel Radiance Fields for Dynamic Scenes

Jia-Wei Liu · Yan-Pei Cao · Weijia Mao · Wenqiao Zhang · David Junhao Zhang · Jussi Keppo · Ying Shan · Xiaohu Qie · Mike Zheng Shou

ATD: Augmenting CP Tensor Decomposition by Self Supervision

Chaoqi Yang · Cheng Qian · Navjot Singh · Cao (Danica) Xiao · M Westover · Edgar Solomonik · Jimeng Sun

Robust Models are less Over-Confident

Julia Grabinski · Paul Gavrikov · Janis Keuper · Margret Keuper

HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details

Yiqun Wang · Ivan Skorokhodov · Peter Wonka

ELIAS: End-to-End Learning to Index and Search in Large Output Spaces

Nilesh Gupta · Patrick Chen · Hsiang-Fu Yu · Cho-Jui Hsieh · Inderjit Dhillon

Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold

Can Yaras · Peng Wang · Zhihui Zhu · Laura Balzano · Qing Qu

DISCO: Adversarial Defense with Local Implicit Functions

Chih-Hui Ho · Nuno Vasconcelos

Invariance-Aware Randomized Smoothing Certificates

Jan Schuchardt · Stephan Günnemann

Bayesian inference via sparse Hamiltonian flows

Naitong Chen · Zuheng Xu · Trevor Campbell

Score-based Generative Modeling Secretly Minimizes the Wasserstein Distance

Dohyun Kwon · Ying Fan · Kangwook Lee

Relational Reasoning via Set Transformers: Provable Efficiency and Applications to MARL

Fengzhuo Zhang · Boyi Liu · Kaixin Wang · Vincent Tan · Zhuoran Yang · Zhaoran Wang

Robustness to Unbounded Smoothness of Generalized SignSGD

Michael Crawshaw · Mingrui Liu · Francesco Orabona · Wei Zhang · Zhenxun Zhuang

Doubly-Asynchronous Value Iteration: Making Value Iteration Asynchronous in Actions

Tian Tian · Kenny Young · Richard Sutton

Associating Objects and Their Effects in Video through Coordination Games

Erika Lu · Forrester Cole · Weidi Xie · Tali Dekel · Bill Freeman · Andrew Zisserman · Michael Rubinstein

Masked Autoencoding for Scalable and Generalizable Decision Making

Fangchen Liu · Hao Liu · Aditya Grover · Pieter Abbeel

In the Eye of the Beholder: Robust Prediction with Causal User Modeling

Amir Feder · Guy Horowitz · Yoav Wald · Roi Reichart · Nir Rosenfeld

Autoregressive Perturbations for Data Poisoning

Pedro Sandoval-Segura · Vasu Singla · Jonas Geiping · Micah Goldblum · Tom Goldstein · David Jacobs

Generalization Analysis of Message Passing Neural Networks on Large Random Graphs

Sohir Maskey · Ron Levie · Yunseok Lee · Gitta Kutyniok

Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions

Haanvid Lee · Jongmin Lee · Yunseon Choi · Wonseok Jeon · Byung-Jun Lee · Yung-Kyun Noh · Kee-Eung Kim

Graph Neural Network Bandits

Parnian Kassraie · Andreas Krause · Ilija Bogunovic

Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium

Chris Junchi Li · Dongruo Zhou · Quanquan Gu · Michael Jordan

Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization

Samuel Daulton · Xingchen Wan · David Eriksson · Maximilian Balandat · Michael A Osborne · Eytan Bakshy

Grounding Aleatoric Uncertainty for Unsupervised Environment Design

Minqi Jiang · Michael Dennis · Jack Parker-Holder · Andrei Lupu · Heinrich Küttler · Edward Grefenstette · Tim Rocktäschel · Jakob Foerster

BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach

Bo Liu · Mao Ye · Stephen Wright · Peter Stone · Qiang Liu

A Theoretical Framework for Inference Learning

Nick Alonso · Beren Millidge · Jeffrey Krichmar · Emre O Neftci

High-dimensional Additive Gaussian Processes under Monotonicity Constraints

Andrés López-Lopera · Francois Bachoc · Olivier Roustant

On the generalization of learning algorithms that do not converge

Nisha Chandramoorthy · Andreas Loukas · Khashayar Gatmiry · Stefanie Jegelka

Proppo: a Message Passing Framework for Customizable and Composable Learning Algorithms

Paavo Parmas · Takuma Seno

FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation

Mehmet Ozgur Turkoglu · Alexander Becker · Hüseyin Anil Gündüz · Mina Rezaei · Bernd Bischl · Rodrigo Caye Daudt · Stefano D'Aronco · Jan Wegner · Konrad Schindler

Reinforcement Learning with Non-Exponential Discounting

Matthias Schultheis · Constantin Rothkopf · Heinz Koeppl

A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs

Fabian Falck · Christopher Williams · Dominic Danks · George Deligiannidis · Christopher Yau · Chris C Holmes · Arnaud Doucet · Matthew Willetts

A Lagrangian Duality Approach to Active Learning

Juan Elenter · Navid Naderializadeh · Alejandro Ribeiro

Non-identifiability and the Blessings of Misspecification in Models of Molecular Fitness

Eli Weinstein · Alan Amin · Jonathan Frazer · Debora Marks

Uncalibrated Models Can Improve Human-AI Collaboration

Kailas Vodrahalli · Tobias Gerstenberg · James Zou

Increasing Confidence in Adversarial Robustness Evaluations

Roland S. Zimmermann · Wieland Brendel · Florian Tramer · Nicholas Carlini

Procedural Image Programs for Representation Learning

Manel Baradad · Richard Chen · Jonas Wulff · Tongzhou Wang · Rogerio Feris · Antonio Torralba · Phillip Isola

Structuring Representations Using Group Invariants

Mehran Shakerinava · Arnab Kumar Mondal · Siamak Ravanbakhsh

Most Activation Functions Can Win the Lottery Without Excessive Depth

Rebekka Burkholz

Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport

Matthias Bitzer · Mona Meister · Christoph Zimmer

Faster Deep Reinforcement Learning with Slower Online Network

Kavosh Asadi · Rasool Fakoor · Omer Gottesman · Taesup Kim · Michael Littman · Alexander Smola

Online Reinforcement Learning for Mixed Policy Scopes

Junzhe Zhang · Elias Bareinboim

What are the best Systems? New Perspectives on NLP Benchmarking

Pierre Colombo · Nathan Noiry · Ekhine Irurozki · Stephan Clémençon

ALMA: Hierarchical Learning for Composite Multi-Agent Tasks

Shariq Iqbal · Robby Costales · Fei Sha

Intra-agent speech permits zero-shot task acquisition

Chen Yan · Federico Carnevale · Petko I Georgiev · Adam Santoro · Aurelia Guy · Alistair Muldal · Chia-Chun Hung · Joshua Abramson · Timothy Lillicrap · Gregory Wayne

Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies

Shachi Deshpande · Kaiwen Wang · Dhruv Sreenivas · Zheng Li · Volodymyr Kuleshov

Hybrid Neural Autoencoders for Stimulus Encoding in Visual and Other Sensory Neuroprostheses

Jacob Granley · Lucas Relic · Michael Beyeler

Data Distributional Properties Drive Emergent In-Context Learning in Transformers

Stephanie Chan · Adam Santoro · Andrew Lampinen · Jane Wang · Aaditya Singh · Pierre Richemond · James McClelland · Felix Hill

Drawing out of Distribution with Neuro-Symbolic Generative Models

Yichao Liang · Josh Tenenbaum · Tuan Anh Le · Siddharth N

FIRE: Semantic Field of Words Represented as Non-Linear Functions

Xin Du · Kumiko Tanaka-Ishii

Do Current Multi-Task Optimization Methods in Deep Learning Even Help?

Derrick Xin · Behrooz Ghorbani · Justin Gilmer · Ankush Garg · Orhan Firat

Diffusion Models as Plug-and-Play Priors

Alexandros Graikos · Nikolay Malkin · Nebojsa Jojic · Dimitris Samaras

ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicine

Ilker Demirel · Ahmet Alparslan Celik · Cem Tekin

Robust Bayesian Regression via Hard Thresholding

Fan zheyi · Zhaohui Li · Qingpei Hu

Optimal-er Auctions through Attention

Dmitry Ivanov · Iskander Safiulin · Igor Filippov · Ksenia Balabaeva

Multi-Fidelity Best-Arm Identification

Riccardo Poiani · Alberto Maria Metelli · Marcello Restelli

Adaptive Oracle-Efficient Online Learning

Guanghui Wang · Zihao Hu · Vidya Muthukumar · Jacob Abernethy

Towards Consistency in Adversarial Classification

Laurent Meunier · Raphael Ettedgui · Rafael Pinot · Yann Chevaleyre · Jamal Atif

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks

Rodrigo Veiga · Ludovic Stephan · Bruno Loureiro · Florent Krzakala · Lenka Zdeborová

Beyond Adult and COMPAS: Fair Multi-Class Prediction via Information Projection

Wael Alghamdi · Hsiang Hsu · Haewon Jeong · Hao Wang · Peter Michalak · Shahab Asoodeh · Flavio Calmon

Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization

Quanqi Hu · YONGJIAN ZHONG · Tianbao Yang

A Single-timescale Analysis for Stochastic Approximation with Multiple Coupled Sequences

Han Shen · Tianyi Chen

A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits

Jiafan He · Tianhao Wang · Yifei Min · Quanquan Gu

Evaluated CMI Bounds for Meta Learning: Tightness and Expressiveness

Fredrik Hellström · Giuseppe Durisi

Discrete-Convex-Analysis-Based Framework for Warm-Starting Algorithms with Predictions

Shinsaku Sakaue · Taihei Oki

projUNN: efficient method for training deep networks with unitary matrices

Bobak Kiani · Randall Balestriero · Yann LeCun · Seth Lloyd

$\alpha$-ReQ : Assessing Representation Quality in Self-Supervised Learning by measuring eigenspectrum decay

Kumar K Agrawal · Arnab Kumar Mondal · Arna Ghosh · Blake Richards

Structural Knowledge Distillation for Object Detection

Philip de Rijk · Lukas Schneider · Marius Cordts · Dariu Gavrila

Scalable Interpretability via Polynomials

Abhimanyu Dubey · Filip Radenovic · Dhruv Mahajan

An empirical analysis of compute-optimal large language model training

Jordan Hoffmann · Sebastian Borgeaud · Arthur Mensch · Elena Buchatskaya · Trevor Cai · Eliza Rutherford · Diego de Las Casas · Lisa Anne Hendricks · Johannes Welbl · Aidan Clark · Thomas Hennigan · Eric Noland · Katherine Millican · George van den Driessche · Bogdan Damoc · Aurelia Guy · Simon Osindero · Karén Simonyan · Erich Elsen · Oriol Vinyals · Jack Rae · Laurent Sifre

Object Scene Representation Transformer

Mehdi S. M. Sajjadi · Daniel Duckworth · Aravindh Mahendran · Sjoerd van Steenkiste · Filip Pavetic · Mario Lucic · Leonidas Guibas · Klaus Greff · Thomas Kipf

Generative Neural Articulated Radiance Fields

Alexander Bergman · Petr Kellnhofer · Wang Yifan · Eric Chan · David Lindell · Gordon Wetzstein

Hand-Object Interaction Image Generation

Hezhen Hu · Weilun Wang · Wengang Zhou · Houqiang Li

Paraphrasing Is All You Need for Novel Object Captioning

Cheng-Fu Yang · Yao-Hung Hubert Tsai · Wan-Cyuan Fan · Russ Salakhutdinov · Louis-Philippe Morency · Frank Wang

A Theoretical Study on Solving Continual Learning

Gyuhak Kim · Changnan Xiao · Tatsuya Konishi · Zixuan Ke · Bing Liu

RecursiveMix: Mixed Learning with History

Lingfeng Yang · Xiang Li · Borui Zhao · Renjie Song · Jian Yang

Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs

Jinguo Zhu · Xizhou Zhu · Wenhai Wang · Xiaohua Wang · Hongsheng Li · Xiaogang Wang · Jifeng Dai

GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models

Chen Liang · Wenguan Wang · Jiaxu Miao · Yi Yang

Multiview Human Body Reconstruction from Uncalibrated Cameras

Zhixuan Yu · Linguang Zhang · Yuanlu Xu · Chengcheng Tang · LUAN TRAN · Cem Keskin · Hyun Soo Park

Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks

Yijing Liu · Qinxian Liu · Jian-Wei Zhang · Haozhe Feng · Zhongwei Wang · Zihan Zhou · Wei Chen

RainNet: A Large-Scale Imagery Dataset and Benchmark for Spatial Precipitation Downscaling

Xuanhong Chen · Kairui Feng · Naiyuan Liu · Bingbing Ni · Yifan Lu · Zhengyan Tong · Ziang Liu

Alleviating "Posterior Collapse'' in Deep Topic Models via Policy Gradient

Yewen Li · Chaojie Wang · Zhibin Duan · Dongsheng Wang · Bo Chen · Bo An · Mingyuan Zhou

Refining Low-Resource Unsupervised Translation by Language Disentanglement of Multilingual Translation Model

Xuan-Phi Nguyen · Shafiq Joty · Kui Wu · Ai Ti Aw

ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time

Tailin Wu · Megan Tjandrasuwita · Zhengxuan Wu · Xuelin Yang · Kevin Liu · Rok Sosic · Jure Leskovec

Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules

Yuhan Helena Liu · Arna Ghosh · Blake Richards · Eric Shea-Brown · Guillaume Lajoie

What Can the Neural Tangent Kernel Tell Us About Adversarial Robustness?

Nikolaos Tsilivis · Julia Kempe

Understanding Hyperdimensional Computing for Parallel Single-Pass Learning

Tao Yu · Yichi Zhang · Zhiru Zhang · Christopher De Sa

Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties

Zeren Shui · Daniel Karls · Mingjian Wen · ilia Nikiforov · Ellad Tadmor · George Karypis

Accelerating Sparse Convolution with Column Vector-Wise Sparsity

Yijun Tan · Kai Han · Kang Zhao · Xianzhi Yu · Zidong Du · Yunji Chen · Yunhe Wang · Jun Yao

Exact Shape Correspondence via 2D graph convolution

Barakeel Fanseu Kamhoua · Lin Zhang · Yongqiang Chen · Han Yang · MA Kaili · Bo Han · Bo Li · James Cheng

Global Optimal K-Medoids Clustering of One Million Samples

Jiayang Ren · Kaixun Hua · Yankai Cao

Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise

Eduard Gorbunov · Marina Danilova · David Dobre · Pavel Dvurechenskii · Alexander Gasnikov · Gauthier Gidel

Sample-Efficient Reinforcement Learning of Partially Observable Markov Games

Qinghua Liu · Csaba Szepesvari · Chi Jin

FasterRisk: Fast and Accurate Interpretable Risk Scores

Jiachang Liu · Chudi Zhong · Boxuan Li · Margo Seltzer · Cynthia Rudin

LieGG: Studying Learned Lie Group Generators

Artem Moskalev · Anna Sepliarskaia · Ivan Sosnovik · Arnold Smeulders

Shield Decentralization for Safe Multi-Agent Reinforcement Learning

Daniel Melcer · Christopher Amato · Stavros Tripakis

ProtoX: Explaining a Reinforcement Learning Agent via Prototyping

Ronilo Ragodos · Tong Wang · Qihang Lin · Xun Zhou

On the Generalizability and Predictability of Recommender Systems

Duncan McElfresh · Sujay Khandagale · Jonathan Valverde · John Dickerson · Colin White

Adversarial Unlearning: Reducing Confidence Along Adversarial Directions

Amrith Setlur · Benjamin Eysenbach · Virginia Smith · Sergey Levine

Neurosymbolic Deep Generative Models for Sequence Data with Relational Constraints

Halley Young · Maxwell Du · Osbert Bastani

Memory Efficient Continual Learning with Transformers

Beyza Ermis · Giovanni Zappella · Martin Wistuba · Aditya Rawal · Cedric Archambeau

Inverse Design for Fluid-Structure Interactions using Graph Network Simulators

Kelsey Allen · Tatiana Lopez-Guevara · Kimberly Stachenfeld · Alvaro Sanchez Gonzalez · Peter Battaglia · Jessica Hamrick · Tobias Pfaff

Cross-modal Learning for Image-Guided Point Cloud Shape Completion

Emanuele Aiello · Diego Valsesia · Enrico Magli

Beyond Mahalanobis Distance for Textual OOD Detection

Pierre Colombo · Eduardo Dadalto · Guillaume Staerman · Nathan Noiry · Pablo Piantanida

Panchromatic and Multispectral Image Fusion via Alternating Reverse Filtering Network

Keyu Yan · Man Zhou · Jie Huang · Feng Zhao · Chengjun Xie · Chongyi Li · Danfeng Hong

Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study

Yongtao Wu · Zhenyu Zhu · Fanghui Liu · Grigorios Chrysos · Volkan Cevher

Tight Analysis of Extra-gradient and Optimistic Gradient Methods For Nonconvex Minimax Problems

Pouria Mahdavinia · Yuyang Deng · Haochuan Li · Mehrdad Mahdavi

Look More but Care Less in Video Recognition

Yitian Zhang · Yue Bai · Huan Wang · Yi Xu · Yun Fu

Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation

Peihao Chen · Dongyu Ji · Kunyang Lin · Runhao Zeng · Thomas Li · Mingkui Tan · Chuang Gan

APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction

Bencheng Yan · Pengjie Wang · Kai Zhang · Feng Li · Hongbo Deng · Jian Xu · Bo Zheng

Rethinking the Reverse-engineering of Trojan Triggers

Zhenting Wang · Kai Mei · Hailun Ding · Juan Zhai · Shiqing Ma

The Mechanism of Prediction Head in Non-contrastive Self-supervised Learning

Zixin Wen · Yuanzhi Li

Exploring evolution-aware & -free protein language models as protein function predictors

Mingyang Hu · Fajie Yuan · Kevin Yang · Fusong Ju · Jin Su · Hui Wang · Fei Yang · Qiuyang Ding

M$^4$I: Multi-modal Models Membership Inference

Pingyi Hu · Zihan Wang · Ruoxi Sun · Hu Wang · Minhui Xue

Deep Model Reassembly

Xingyi Yang · Daquan Zhou · Songhua Liu · Jingwen Ye · Xinchao Wang

Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering

An Zhang · Wenchang Ma · Xiang Wang · Tat-Seng Chua

Stochastic Multiple Target Sampling Gradient Descent

Hoang Phan · Ngoc Tran · Trung Le · Toan Tran · Nhat Ho · Dinh Phung

Probable Domain Generalization via Quantile Risk Minimization

Cian Eastwood · Alexander Robey · Shashank Singh · Julius von Kügelgen · Hamed Hassani · George J. Pappas · Bernhard Schölkopf

Fair and Optimal Decision Trees: A Dynamic Programming Approach

Jacobus van der Linden · Mathijs de Weerdt · Emir Demirović

An Information-Theoretic Framework for Deep Learning

Hong Jun Jeon · Benjamin Van Roy

Learning Tractable Probabilistic Models from Inconsistent Local Estimates

Shasha Jin · Vasundhara Komaragiri · Tahrima Rahman · Vibhav Gogate

Domain Adaptation meets Individual Fairness. And they get along.

Debarghya Mukherjee · Felix Petersen · Mikhail Yurochkin · Yuekai Sun

Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms

Surbhi Goel · Sham Kakade · Adam Kalai · Cyril Zhang

Interventions, Where and How? Experimental Design for Causal Models at Scale

Panagiotis Tigas · Yashas Annadani · Andrew Jesson · Bernhard Schölkopf · Yarin Gal · Stefan Bauer

On A Mallows-type Model For (Ranked) Choices

Yifan Feng · Yuxuan Tang

Forward-Backward Latent State Inference for Hidden Continuous-Time semi-Markov Chains

Nicolai Engelmann · Heinz Koeppl

Regret Bounds for Risk-Sensitive Reinforcement Learning

Osbert Bastani · Jason Yecheng Ma · Estelle Shen · Wanqiao Xu

Rate-Optimal Online Convex Optimization in Adaptive Linear Control

Asaf Benjamin Cassel · Alon Peled-Cohen · Tomer Koren

Zonotope Domains for Lagrangian Neural Network Verification

Matt Jordan · Jonathan Hayase · Alex Dimakis · Sewoong Oh

Generalizing Bayesian Optimization with Decision-theoretic Entropies

Willie Neiswanger · Lantao Yu · Shengjia Zhao · Chenlin Meng · Stefano Ermon

Regularized Gradient Descent Ascent for Two-Player Zero-Sum Markov Games

Sihan Zeng · Thinh Doan · Justin Romberg

Wasserstein Logistic Regression with Mixed Features

Aras Selvi · Mohammad Reza Belbasi · Martin Haugh · Wolfram Wiesemann

The Role of Baselines in Policy Gradient Optimization

Jincheng Mei · Wesley Chung · Valentin Thomas · Bo Dai · Csaba Szepesvari · Dale Schuurmans

Active Learning Polynomial Threshold Functions

Omri Ben-Eliezer · Max Hopkins · Chutong Yang · Hantao Yu

Quantile Constrained Reinforcement Learning: A Reinforcement Learning Framework Constraining Outage Probability

Whiyoung Jung · Myungsik Cho · Jongeui Park · Youngchul Sung

Generalised Implicit Neural Representations

Daniele Grattarola · Pierre Vandergheynst

Score-Based Generative Models Detect Manifolds

Jakiw Pidstrigach

Mixture-of-Experts with Expert Choice Routing

Yanqi Zhou · Tao Lei · Hanxiao Liu · Nan Du · Yanping Huang · Vincent Zhao · Andrew Dai · zhifeng Chen · Quoc V Le · James Laudon

Coresets for Relational Data and The Applications

Jiaxiang Chen · Qingyuan Yang · Ruomin Huang · Hu Ding

Factored Adaptation for Non-Stationary Reinforcement Learning

Fan Feng · Biwei Huang · Kun Zhang · Sara Magliacane

Counterfactual harm

Jonathan Richens · Rory Beard · Daniel H. Thompson

Addressing Leakage in Concept Bottleneck Models

Marton Havasi · Sonali Parbhoo · Finale Doshi-Velez

Compositional generalization through abstract representations in human and artificial neural networks

Takuya Ito · Tim Klinger · Doug Schultz · John Murray · Michael Cole · Mattia Rigotti

Efficient coding, channel capacity, and the emergence of retinal mosaics

Na Young Jun · Greg Field · John Pearson

Learning in Congestion Games with Bandit Feedback

Qiwen Cui · Zhihan Xiong · Maryam Fazel · Simon Du

Optimal Weak to Strong Learning

Kasper Green Larsen · Martin Ritzert

Log-Linear-Time Gaussian Processes Using Binary Tree Kernels

Michael K. Cohen · Samuel Daulton · Michael A Osborne

Sharp Analysis of Stochastic Optimization under Global Kurdyka-Lojasiewicz Inequality

Ilyas Fatkhullin · Jalal Etesami · Niao He · Negar Kiyavash

Learning Distributions Generated by Single-Layer ReLU Networks in the Presence of Arbitrary Outliers

Saikiran Bulusu · Geethu Joseph · M. Cenk Gursoy · Pramod Varshney

Instance-optimal PAC Algorithms for Contextual Bandits

Zhaoqi Li · Lillian Ratliff · houssam nassif · Kevin Jamieson · Lalit Jain

Learning the Structure of Large Networked Systems Obeying Conservation Laws

Anirudh Rayas · Rajasekhar Anguluri · Gautam Dasarathy

Local Linear Convergence of Gradient Methods for Subspace Optimization via Strict Complementarity

Ron Fisher · Dan Garber

A Near-Optimal Primal-Dual Method for Off-Policy Learning in CMDP

Fan Chen · Junyu Zhang · Zaiwen Wen

The alignment property of SGD noise and how it helps select flat minima: A stability analysis

Lei Wu · Mingze Wang · Weijie Su

Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited

Nils M. Kriege

Polynomial-Time Optimal Equilibria with a Mediator in Extensive-Form Games

Brian Zhang · Tuomas Sandholm

An $\alpha$-regret analysis of Adversarial Bilateral Trade

Yossi Azar · Amos Fiat · Federico Fusco

Algorithms and Hardness for Learning Linear Thresholds from Label Proportions

Rishi Saket

Decomposable Non-Smooth Convex Optimization with Nearly-Linear Gradient Oracle Complexity

Sally Dong · Haotian Jiang · Yin Tat Lee · Swati Padmanabhan · Guanghao Ye

Optimal Query Complexities for Dynamic Trace Estimation

David Woodruff · Fred Zhang · Richard Zhang

On Scalable Testing of Samplers

Yash Pote · Kuldeep S Meel

Private Isotonic Regression

Badih Ghazi · Pritish Kamath · Ravi Kumar · Pasin Manurangsi

Neural Network Architecture Beyond Width and Depth

Shijun Zhang · Zuowei Shen · Haizhao Yang

Faster and Scalable Algorithms for Densest Subgraph and Decomposition

Elfarouk Harb · Kent Quanrud · Chandra Chekuri

Differentially Private Covariance Revisited

Wei Dong · Yuting Liang · Ke Yi

A Fourier Approach to Mixture Learning

Mingda Qiao · Guru Guruganesh · Ankit Rawat · Kumar Avinava Dubey · Manzil Zaheer

On Non-Linear operators for Geometric Deep Learning

Grégoire Sergeant-Perthuis · Jakob Maier · Joan Bruna · Edouard Oyallon

Linear Label Ranking with Bounded Noise

Dimitris Fotakis · Alkis Kalavasis · Vasilis Kontonis · Christos Tzamos

Estimation of Entropy in Constant Space with Improved Sample Complexity

Maryam Aliakbarpour · Andrew McGregor · Jelani Nelson · Erik Waingarten

Efficient Sampling on Riemannian Manifolds via Langevin MCMC

Xiang Cheng · Jingzhao Zhang · Suvrit Sra

Near-Optimal Regret for Adversarial MDP with Delayed Bandit Feedback

Tiancheng Jin · Tal Lancewicki · Haipeng Luo · Yishay Mansour · Aviv Rosenberg

Understanding Deep Neural Function Approximation in Reinforcement Learning via $\epsilon$-Greedy Exploration

Fanghui Liu · Luca Viano · Volkan Cevher

Communication-efficient distributed eigenspace estimation with arbitrary node failures

Vasileios Charisopoulos · Anil Damle

Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent

Zhiyuan Li · Tianhao Wang · Jason Lee · Sanjeev Arora

Causal Discovery in Linear Latent Variable Models Subject to Measurement Error

Yuqin Yang · AmirEmad Ghassami · Mohamed Nafea · Negar Kiyavash · Kun Zhang · Ilya Shpitser

Log-Concave and Multivariate Canonical Noise Distributions for Differential Privacy

Jordan Awan · Jinshuo Dong

A Quadrature Rule combining Control Variates and Adaptive Importance Sampling

Rémi Leluc · François Portier · Johan Segers · Aigerim Zhuman

Decision Trees with Short Explainable Rules

Victor Feitosa Souza · Ferdinando Cicalese · Eduardo Laber · Marco Molinaro

Task-level Differentially Private Meta Learning

Xinyu Zhou · Raef Bassily

Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity

William de Vazelhes · Hualin Zhang · Huimin Wu · Xiaotong Yuan · Bin Gu

Robust Imitation via Mirror Descent Inverse Reinforcement Learning

Dong-Sig Han · Hyunseo Kim · Hyundo Lee · JeHwan Ryu · Byoung-Tak Zhang

Learning Partial Equivariances From Data

David W. Romero · Suhas Lohit

Exploitability Minimization in Games and Beyond

Denizalp Goktas · Amy Greenwald

Communication-Efficient Topologies for Decentralized Learning with $O(1)$ Consensus Rate

Zhuoqing Song · Weijian Li · Kexin Jin · Lei Shi · Ming Yan · Wotao Yin · Kun Yuan

Hardness in Markov Decision Processes: Theory and Practice

Michelangelo Conserva · Paulo Rauber

Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget

Jasmin Brandt · Viktor Bengs · Björn Haddenhorst · Eyke Hüllermeier

Decoupled Context Processing for Context Augmented Language Modeling

Zonglin Li · Ruiqi Guo · Sanjiv Kumar

PerfectDou: Dominating DouDizhu with Perfect Information Distillation

Guan Yang · Minghuan Liu · Weijun Hong · Weinan Zhang · Fei Fang · Guangjun Zeng · Yue Lin

Neural Transmitted Radiance Fields

Chengxuan Zhu · Renjie Wan · Boxin Shi

Self-Supervised Pretraining for Large-Scale Point Clouds

Zaiwei Zhang · Min Bai · Erran Li Li

GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis

Yushi Cao · Zhiming Li · Tianpei Yang · Hao Zhang · YAN ZHENG · Yi Li · Jianye Hao · Yang Liu

Streaming Radiance Fields for 3D Video Synthesis

Lingzhi LI · Zhen Shen · Zhongshu Wang · Li Shen · Ping Tan

MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

Ilyes Batatia · David P Kovacs · Gregor Simm · Christoph Ortner · Gabor Csanyi

Analyzing Data-Centric Properties for Graph Contrastive Learning

Puja Trivedi · Ekdeep S Lubana · Mark Heimann · Danai Koutra · Jayaraman Thiagarajan

On the Limitations of Stochastic Pre-processing Defenses

Yue Gao · I Shumailov · Kassem Fawaz · Nicolas Papernot

Hedging as Reward Augmentation in Probabilistic Graphical Models

Debarun Bhattacharjya · Radu Marinescu

Riemannian Score-Based Generative Modelling

Valentin De Bortoli · Emile Mathieu · Michael Hutchinson · James Thornton · Yee Whye Teh · Arnaud Doucet

On the Spectral Bias of Convolutional Neural Tangent and Gaussian Process Kernels

Amnon Geifman · Meirav Galun · David Jacobs · Basri Ronen

Causality Preserving Chaotic Transformation and Classification using Neurochaos Learning

Harikrishnan N B · Aditi Kathpalia · Nithin Nagaraj

Exploring through Random Curiosity with General Value Functions

Aditya Ramesh · Louis Kirsch · Sjoerd van Steenkiste · Jürgen Schmidhuber

Masked Autoencoders that Listen

Po-Yao Huang · Hu Xu · Juncheng Li · Alexei Baevski · Michael Auli · Wojciech Galuba · Florian Metze · Christoph Feichtenhofer

Sharing Knowledge for Meta-learning with Feature Descriptions

Tomoharu Iwata · Atsutoshi Kumagai

ReFactor GNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective

Yihong Chen · Pushkar Mishra · Luca Franceschi · Pasquale Minervini · Pontus Lars Erik Saito Stenetorp · Sebastian Riedel

New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound

Arushi Gupta · Nikunj Saunshi · Dingli Yu · Kaifeng Lyu · Sanjeev Arora

Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning

Joseph Early · Tom Bewley · Christine Evers · Sarvapali Ramchurn

FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness

Tri Dao · Dan Fu · Stefano Ermon · Atri Rudra · Christopher Ré

Conditional Diffusion Process for Inverse Halftoning

Hao Jiang · Yadong Mu

Prompt Certified Machine Unlearning with Randomized Gradient Smoothing and Quantization

Zijie Zhang · Yang Zhou · Xin Zhao · Tianshi Che · Lingjuan Lyu

Training language models to follow instructions with human feedback

Long Ouyang · Jeffrey Wu · Xu Jiang · Diogo Almeida · Carroll Wainwright · Pamela Mishkin · Chong Zhang · Sandhini Agarwal · Katarina Slama · Alex Ray · John Schulman · Jacob Hilton · Fraser Kelton · Luke Miller · Maddie Simens · Amanda Askell · Peter Welinder · Paul Christiano · Jan Leike · Ryan Lowe

Learning to Accelerate Partial Differential Equations via Latent Global Evolution

Tailin Wu · Takashi Maruyama · Jure Leskovec

Robust $\phi$-Divergence MDPs

Chin Pang Ho · Marek Petrik · Wolfram Wiesemann

Offline Goal-Conditioned Reinforcement Learning via $f$-Advantage Regression

Jason Yecheng Ma · Jason Yan · Dinesh Jayaraman · Osbert Bastani

Translation-equivariant Representation in Recurrent Networks with a Continuous Manifold of Attractors

Wenhao Zhang · Ying Nian Wu · Si Wu

Visual Clues: Bridging Vision and Language Foundations for Image Paragraph Captioning

Yujia Xie · Luowei Zhou · Xiyang Dai · Lu Yuan · Nguyen Bach · Ce Liu · Michael Zeng

Learning Audio-Visual Dynamics Using Scene Graphs for Audio Source Separation

Moitreya Chatterjee · Narendra Ahuja · Anoop Cherian

Towards a Standardised Performance Evaluation Protocol for Cooperative MARL

Rihab Gorsane · Omayma Mahjoub · Ruan John de Kock · Roland Dubb · Siddarth Singh · Arnu Pretorius

Mismatched No More: Joint Model-Policy Optimization for Model-Based RL

Benjamin Eysenbach · Alexander Khazatsky · Sergey Levine · Russ Salakhutdinov

Continual Learning with Evolving Class Ontologies

Zhiqiu Lin · Deepak Pathak · Yu-Xiong Wang · Deva Ramanan · Shu Kong

The Franz-Parisi Criterion and Computational Trade-offs in High Dimensional Statistics

Afonso S Bandeira · Ahmed El Alaoui · Samuel Hopkins · Tselil Schramm · Alexander S Wein · Ilias Zadik

Conformalized Fairness via Quantile Regression

Meichen Liu · Lei Ding · Dengdeng Yu · Wulong Liu · Linglong Kong · Bei Jiang

Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules

Kazuki Irie · Francesco Faccio · Jürgen Schmidhuber

An Analysis of Ensemble Sampling

Chao Qin · Zheng Wen · Xiuyuan Lu · Benjamin Van Roy

Resolving the data ambiguity for periodic crystals

Daniel Widdowson · Vitaliy Kurlin

Split-kl and PAC-Bayes-split-kl Inequalities for Ternary Random Variables

Yi-Shan Wu · Yevgeny Seldin

On the Sample Complexity of Stabilizing LTI Systems on a Single Trajectory

Yang Hu · Adam Wierman · Guannan Qu

On the convergence of policy gradient methods to Nash equilibria in general stochastic games

Angeliki Giannou · Kyriakos Lotidis · Panayotis Mertikopoulos · Emmanouil-Vasileios Vlatakis-Gkaragkounis

Certifying Some Distributional Fairness with Subpopulation Decomposition

Mintong Kang · Linyi Li · Maurice Weber · Yang Liu · Ce Zhang · Bo Li

Biologically Inspired Dynamic Thresholds for Spiking Neural Networks

Jianchuan Ding · Bo Dong · Felix Heide · Yufei Ding · Yunduo Zhou · Baocai Yin · Xin Yang

Precise Learning Curves and Higher-Order Scalings for Dot-product Kernel Regression

Lechao Xiao · Hong Hu · Theodor Misiakiewicz · Yue Lu · Jeffrey Pennington

Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees

Wenying Deng · Beau Coker · Rajarshi Mukherjee · Jeremiah Liu · Brent Coull

Sketching based Representations for Robust Image Classification with Provable Guarantees

Nishanth Dikkala · Sankeerth Rao Karingula · Raghu Meka · Jelani Nelson · Rina Panigrahy · Xin Wang

Causality-driven Hierarchical Structure Discovery for Reinforcement Learning

shaohui peng · Xing Hu · Rui Zhang · Ke Tang · Jiaming Guo · Qi Yi · Ruizhi Chen · xishan zhang · Zidong Du · Ling Li · Qi Guo · Yunji Chen

Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners

Zhenhailong Wang · Manling Li · Ruochen Xu · Luowei Zhou · Jie Lei · Xudong Lin · Shuohang Wang · Ziyi Yang · Chenguang Zhu · Derek Hoiem · Shih-Fu Chang · Mohit Bansal · Heng Ji

Foundation Posteriors for Approximate Probabilistic Inference

Mike Wu · Noah Goodman

Unsupervised Image-to-Image Translation with Density Changing Regularization

Shaoan Xie · Qirong Ho · Kun Zhang

Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptions

Murad Tukan · Loay Mualem · Alaa Maalouf

Overparameterization from Computational Constraints

Sanjam Garg · Somesh Jha · Saeed Mahloujifar · Mohammad Mahmoody · Mingyuan Wang

Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets

Yifei Min · Tianhao Wang · Ruitu Xu · Zhaoran Wang · Michael Jordan · Zhuoran Yang

Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation

JoonHo Jang · Byeonghu Na · Dong Hyeok Shin · Mingi Ji · Kyungwoo Song · Il-chul Moon

Giga-scale Kernel Matrix-Vector Multiplication on GPU

Robert Hu · Siu Lun Chau · Dino Sejdinovic · Joan Glaunès

USB: A Unified Semi-supervised Learning Benchmark for Classification

Yidong Wang · Hao Chen · Yue Fan · Wang SUN · Ran Tao · Wenxin Hou · Renjie Wang · Linyi Yang · Zhi Zhou · Lan-Zhe Guo · Heli Qi · Zhen Wu · Yu-Feng Li · Satoshi Nakamura · Wei Ye · Marios Savvides · Bhiksha Raj · Takahiro Shinozaki · Bernt Schiele · Jindong Wang · Xing Xie · Yue Zhang

DC-BENCH: Dataset Condensation Benchmark

Justin CUI · Ruochen Wang · Si Si · Cho-Jui Hsieh

FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings

Jean Ogier du Terrail · Samy-Safwan Ayed · Edwige Cyffers · Felix Grimberg · Chaoyang He · Regis Loeb · Paul Mangold · Tanguy Marchand · Othmane Marfoq · Erum Mushtaq · Boris Muzellec · Constantin Philippenko · Santiago Silva · Maria Teleńczuk · Shadi Albarqouni · Salman Avestimehr · Aurélien Bellet · Aymeric Dieuleveut · Martin Jaggi · Sai Praneeth Karimireddy · Marco Lorenzi · Giovanni Neglia · Marc Tommasi · Mathieu Andreux

NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies

Arjun Krishnakumar · Colin White · Arber Zela · Renbo Tu · Mahmoud Safari · Frank Hutter

PROSPECT: Labeled Tandem Mass Spectrometry Dataset for Machine Learning in Proteomics

Omar Shouman · Wassim Gabriel · Victor-George Giurcoiu · Vitor Sternlicht · Mathias Wilhelm

FLAIR: Federated Learning Annotated Image Repository

Congzheng Song · Filip Granqvist · Kunal Talwar

The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games

Chao Yu · Akash Velu · Eugene Vinitsky · Jiaxuan Gao · Yu Wang · Alexandre Bayen · YI WU

ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in the Wild

Chirag Raman · Jose Vargas Quiros · Stephanie Tan · Ashraful Islam · Ekin Gedik · Hayley Hung

MOMA-LRG: Language-Refined Graphs for Multi-Object Multi-Actor Activity Parsing

Zelun Luo · Zane Durante · Linden Li · Wanze Xie · Ruochen Liu · Emily Jin · Zhuoyi Huang · Lun Yu Li · Jiajun Wu · Juan Carlos Niebles · Ehsan Adeli · Fei-Fei Li

Touch and Go: Learning from Human-Collected Vision and Touch

Fengyu Yang · Chenyang Ma · Jiacheng Zhang · Jing Zhu · Wenzhen Yuan · Andrew Owens

A Survey and Datasheet Repository of Publicly Available US Criminal Justice Datasets

Miri Zilka · Bradley Butcher · Adrian Weller

MATE: Benchmarking Multi-Agent Reinforcement Learning in Distributed Target Coverage Control

Xuehai Pan · Mickel Liu · Fangwei Zhong · Yaodong Yang · Song-Chun Zhu · Yizhou Wang

How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios

Mantas Mazeika · Eric Tang · Andy Zou · Steven Basart · Jun Shern Chan · Dawn Song · David Forsyth · Jacob Steinhardt · Dan Hendrycks

MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control

Nolan Wagener · Andrey Kolobov · Felipe Vieira Frujeri · Ricky Loynd · Ching-An Cheng · Matthew Hausknecht

OpenSRH: optimizing brain tumor surgery using intraoperative stimulated Raman histology

Cheng Jiang · Asadur Chowdury · Xinhai Hou · Akhil Kondepudi · Christian Freudiger · Kyle Conway · Sandra Camelo-Piragua · Daniel Orringer · Honglak Lee · Todd Hollon

DDXPlus: A New Dataset For Automatic Medical Diagnosis

Arsene Fansi Tchango · Rishab Goel · Zhi Wen · Julien Martel · Joumana Ghosn

ViSioNS: Visual Search in Natural Scenes Benchmark

Fermín Travi · Gonzalo Ruarte · Gaston Bujia · Juan Esteban Kamienkowski

NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning

Rong-Jun Qin · Xingyuan Zhang · Songyi Gao · Xiong-Hui Chen · Zewen Li · Weinan Zhang · Yang Yu

Finding Naturally Occurring Physical Backdoors in Image Datasets

Emily Wenger · Roma Bhattacharjee · Arjun Nitin Bhagoji · Josephine Passananti · Emilio Andere · Heather Zheng · Ben Zhao

AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier–Stokes Solutions

Florent Bonnet · Jocelyn Mazari · Paola Cinnella · Patrick Gallinari

OpenXAI: Towards a Transparent Evaluation of Model Explanations

Chirag Agarwal · Satyapriya Krishna · Eshika Saxena · Martin Pawelczyk · Nari Johnson · Isha Puri · Marinka Zitnik · Himabindu Lakkaraju

LAION-5B: An open large-scale dataset for training next generation image-text models

Christoph Schuhmann · Romain Beaumont · Richard Vencu · Cade Gordon · Ross Wightman · Mehdi Cherti · Theo Coombes · Aarush Katta · Clayton Mullis · Mitchell Wortsman · Patrick Schramowski · Srivatsa Kundurthy · Katherine Crowson · Ludwig Schmidt · Robert Kaczmarczyk · Jenia Jitsev

OccGen: Selection of Real-world Multilingual Parallel Data Balanced in Gender within Occupations

Marta Costa-jussà · Christine Basta · Oriol Domingo · André Rubungo

Fairness-Aware PAC Learning from Corrupted Data

Nikola Konstantinov · Christoph Lampert

LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data

Ali Eshragh · Fred Roosta · Asef Nazari · Michael Mahoney

Multi-Agent Multi-Armed Bandits with Limited Communication

Mridul Agarwal · Vaneet Aggarwal · Kamyar Azizzadenesheli

When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint

Yoav S Freund · Yi-An Ma · Tong Zhang

[Re] Nondeterminism and Instability in Neural Network Optimization

Waqas Ahmed · Sheeba Samuel

[Re] Replication Study of "Fairness and Bias in Online Selection"

Roxana-Maria Petcu · Pim Praat · Jeroen Wijnen · Manolis Rerres

[Re] Reproduction and Extension of "Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation"

Erica Eaton · Pirouz Naghavi

[Re] Learning to count everything

Maša Kljun · Matija Teršek · Domen Vreš

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Industry, Academia, and the In-Betweens

Robert Laurella · Sebastian Raschka

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The purpose of this Social is to explore and begin eliminating the ostensible barriers between research communities in industry and academia. By inviting NeurIPS participants to attend regardless of their institutional affiliation, employment status, or educational background, our intention is to foster connections and networking opportunities between practitioners who may not otherwise meet through conventional forums. Our discussion will explore the transition from academia to industry (and vice-versa), as well as the fruitful collaborations that follow from dismantling the idea of such transitions altogether.

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NeurIPS Town Hall

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The Town Hall meeting is an opportunity to connect and to learn about how this year's conference was organized. It is also a place where you can ask questions and provide feedback to the NeurIPS organizers and Board. We encourage you to submit questions in advance via townhall@neurips.cc. This event is open to all registered attendees of NeurIPS 2022.

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Women in AI Ignite

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Join us for 5-minute Ignite talks by women in AI. The idea of Women in AI Ignite Social is based on our experience hosting Ignite sessions at conferences around the world. Everyone is welcome; our speakers are women.

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Gulf Coast AI

Nicholas Mattei

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NeurIPS 2022 is happening in New Orleans, one of the hearts of the Gulf Coast region and a vibrant area for AI research and application. The goal of this social is to provide a forum and opportunity to raise awareness about the work happening in the region, including the unique and pressing challenges where AI/ML/DS research can have impact including coastal, climate, logistics, and materials. And also, a fun and interactive forum for participants to discuss these challenges and socialize within the broader NeurIPS community. We will gather for drinks and an interactive program where we will hear from a panel of both AI researchers and others working in key industries along the Gulf Coast. Join us for a great time and meeting new folks from around the Gulf Coast AI community!

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Early-Career Researchers Interested in ML & Climate Change

Sigrid Passano Hellan · Tiffany Vlaar

6:00 PM - 8:00 PM

This is an in-person social for early-career researchers wanting to work in the ML & Climate Change research field. The nature of this research area is inherently interdisciplinary and we welcome researchers from different backgrounds and interests. The social offers the opportunity for networking and informal discussion; aiming to both connect people starting out and introducing them to current ML & Climate Change researchers. We will start with an icebreaker bingo, followed by roundtable discussions on different topics, such as how to start interdisciplinary collaborations, finding and working with real-world data, the most impactful/urgent areas of research, and how to find jobs in ML & Climate Change, etc.

Confirmed roundtable moderators: Alex Hernandez-Garcia (Mila), Konstantin Klemmer (Microsoft Research New England and Climate Change AI), Anna Kwa (Allen Institute for AI), Alexandre Lacoste (ServiceNow), Mélisande Teng (Mila), and Bianca Zadrozny (IBM Research).

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From Old Biases to New Opportunities: Annotator Empowerment and Data Excellence

Amina Abieva

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We’d like to welcome participants to discuss how to build ML systems immune to biases, obtain representative datasets, and resolve disagreements among annotators in order to bring out high-quality AI products. We’d like to do that while also empowering digital workers, i.e., getting tangible benefits from improving AI systems through data excellence. We are especially interested in inviting people from different backgrounds and opinions on how imperfect AI systems impact people’s lives and how the training data can be changed to improve the state of affairs.

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