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168 Program Highlights »
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Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #1
Experimental Design for Cost-Aware Learning of Causal Graphs
Erik Lindgren · Murat Kocaoglu · Alexandros Dimakis · Sriram Vishwanath
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #2
Removing Hidden Confounding by Experimental Grounding
Nathan Kallus · Aahlad Manas Puli · Uri Shalit
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #3
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions
Sara Magliacane · Thijs van Ommen · Tom Claassen · Stephan Bongers · Philip Versteeg · Joris M Mooij
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #4
Structural Causal Bandits: Where to Intervene?
Sanghack Lee · Elias Bareinboim
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #5
Uplift Modeling from Separate Labels
Ikko Yamane · Florian Yger · Jamal Atif · Masashi Sugiyama
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #6
Causal Inference with Noisy and Missing Covariates via Matrix Factorization
Nathan Kallus · Xiaojie Mao · Madeleine Udell
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #7
Fast Estimation of Causal Interactions using Wold Processes
Flavio Figueiredo · Guilherme Resende Borges · Pedro O.S. Vaz de Melo · Renato Assunção
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #8
Learning and Testing Causal Models with Interventions
Jayadev Acharya · Arnab Bhattacharyya · Constantinos Daskalakis · Saravanan Kandasamy
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #9
Causal Inference via Kernel Deviance Measures
Jovana Mitrovic · Dino Sejdinovic · Yee Whye Teh
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #10
Multi-domain Causal Structure Learning in Linear Systems
AmirEmad Ghassami · Negar Kiyavash · Biwei Huang · Kun Zhang
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #11
Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models
Shoubo Hu · Zhitang Chen · Vahid Partovi Nia · Laiwan CHAN · Yanhui Geng
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #12
Direct Estimation of Differences in Causal Graphs
Yuhao Wang · Chandler Squires · Anastasiya Belyaeva · Caroline Uhler
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #13
Identification and Estimation of Causal Effects from Dependent Data
Eli Sherman · Ilya Shpitser
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #14
Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages
Michelle Yuan · Benjamin Van Durme · Jordan Boyd-Graber
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #15
Submodular Field Grammars: Representation, Inference, and Application to Image Parsing
Abram L Friesen · Pedro Domingos
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #16
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language
Matthew D. Hoffman · Matthew Johnson · Dustin Tran
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #17
Distributionally Robust Graphical Models
Rizal Fathony · Ashkan Rezaei · Mohammad Ali Bashiri · Xinhua Zhang · Brian Ziebart
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #18
Flexible and accurate inference and learning for deep generative models
Eszter Vértes · Maneesh Sahani
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #19
Provable Gaussian Embedding with One Observation
Ming Yu · Zhuoran Yang · Tuo Zhao · Mladen Kolar · Princeton Zhaoran Wang
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #20
Learning and Inference in Hilbert Space with Quantum Graphical Models
Siddarth Srinivasan · Carlton Downey · Byron Boots
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #21
Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations
Tong Wang
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #22
Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks
Quan Zhang · Mingyuan Zhou
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #23
Theoretical guarantees for EM under misspecified Gaussian mixture models
Raaz Dwivedi · nhật Hồ · Koulik Khamaru · Martin Wainwright · Michael Jordan
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #24
Nonparametric learning from Bayesian models with randomized objective functions
Simon Lyddon · Stephen Walker · Chris C Holmes
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #25
Rectangular Bounding Process
Xuhui Fan · Bin Li · Scott SIsson
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #26
A Bayesian Nonparametric View on Count-Min Sketch
Diana Cai · Michael Mitzenmacher · Ryan Adams
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #27
Communication Efficient Parallel Algorithms for Optimization on Manifolds
Bayan Saparbayeva · Michael Zhang · Lizhen Lin
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #28
Lifted Weighted Mini-Bucket
Nicholas Gallo · Alexander Ihler
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #29
Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
Dominik Linzner · Heinz Koeppl
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #30
Faithful Inversion of Generative Models for Effective Amortized Inference
Stefan Webb · Adam Golinski · Rob Zinkov · Siddharth Narayanaswamy · Tom Rainforth · Yee Whye Teh · Frank Wood
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #31
A Stein variational Newton method
Gianluca Detommaso · Tiangang Cui · Youssef Marzouk · Alessio Spantini · Robert Scheichl
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #32
Reparameterization Gradient for Non-differentiable Models
Wonyeol Lee · Hangyeol Yu · Hongseok Yang
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #33
Implicit Reparameterization Gradients
Mikhail Figurnov · Shakir Mohamed · Andriy Mnih
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #34
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient
Aaron Mishkin · Frederik Kunstner · Didrik Nielsen · Mark Schmidt · Mohammad Emtiyaz Khan
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #35
Wasserstein Variational Inference
Luca Ambrogioni · Umut Güçlü · Yağmur Güçlütürk · Max Hinne · Marcel A. J. van Gerven · Eric Maris
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #36
Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems
Jung-Su Ha · Young-Jin Park · Hyeok-Joo Chae · Soon-Seo Park · Han-Lim Choi
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #37
Variational Inference with Tail-adaptive f-Divergence
Dilin Wang · Hao Liu · Qiang Liu
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #38
Boosting Black Box Variational Inference
Francesco Locatello · Gideon Dresdner · Rajiv Khanna · Isabel Valera · Gunnar Raetsch
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #39
Discretely Relaxing Continuous Variables for tractable Variational Inference
Trefor Evans · Prasanth Nair
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #40
Using Large Ensembles of Control Variates for Variational Inference
Tomas Geffner · Justin Domke
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #41
The promises and pitfalls of Stochastic Gradient Langevin Dynamics
Nicolas Brosse · Alain Durmus · Eric Moulines
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #42
Large-Scale Stochastic Sampling from the Probability Simplex
Jack Baker · Paul Fearnhead · Emily Fox · Christopher Nemeth
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #43
Mirrored Langevin Dynamics
Ya-Ping Hsieh · Ali Kavis · Paul Rolland · Volkan Cevher
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #44
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning
Rui Luo · Jianhong Wang · Yaodong Yang · Jun WANG · Zhanxing Zhu
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #45
Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo
Oren Mangoubi · Nisheeth Vishnoi
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #46
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization
Pan Xu · Jinghui Chen · Difan Zou · Quanquan Gu
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #47
Meta-Learning MCMC Proposals
Tongzhou Wang · YI WU · Dave Moore · Stuart Russell
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #48
Posterior Concentration for Sparse Deep Learning
Veronika Rockova · nicholas polson
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #49
Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net
Tom Michoel
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #50
Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors
Fei Jiang · Guosheng Yin · Francesca Dominici
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #51
Graphical model inference: Sequential Monte Carlo meets deterministic approximations
Fredrik Lindsten · Jouni Helske · Matti Vihola
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #52
Implicit Probabilistic Integrators for ODEs
Onur Teymur · Han Cheng Lie · Tim Sullivan · Ben Calderhead
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #53
A Bayes-Sard Cubature Method
Toni Karvonen · Chris J Oates · Simo Sarkka
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #54
Deep State Space Models for Time Series Forecasting
Syama Sundar Rangapuram · Matthias W Seeger · Jan Gasthaus · Lorenzo Stella · Yuyang Wang · Tim Januschowski
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #55
BRUNO: A Deep Recurrent Model for Exchangeable Data
Iryna Korshunova · Jonas Degrave · Ferenc Huszar · Yarin Gal · Arthur Gretton · Joni Dambre
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #56
Scaling Gaussian Process Regression with Derivatives
David Eriksson · Kun Dong · Eric Lee · David Bindel · Andrew Wilson
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #57
Algebraic tests of general Gaussian latent tree models
Dennis Leung · Mathias Drton
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #58
Differentially Private Bayesian Inference for Exponential Families
Garrett Bernstein · Daniel Sheldon
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #59
Semi-crowdsourced Clustering with Deep Generative Models
Yucen Luo · TIAN TIAN · Jiaxin Shi · Jun Zhu · Bo Zhang
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #60
Deep Poisson gamma dynamical systems
Dandan Guo · Bo Chen · Hao Zhang · Mingyuan Zhou
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #61
Deep State Space Models for Unconditional Word Generation
Florian Schmidt · Thomas Hofmann
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #62
Modular Networks: Learning to Decompose Neural Computation
Louis Kirsch · Julius Kunze · David Barber
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #63
Gaussian Process Prior Variational Autoencoders
Francesco Paolo Casale · Adrian Dalca · Luca Saglietti · Jennifer Listgarten · Nicolo Fusi
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #64
Bayesian Semi-supervised Learning with Graph Gaussian Processes
Yin Cheng Ng · Nicolò Colombo · Ricardo Silva
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #65
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo
Marton Havasi · José Miguel Hernández-Lobato · Juan José Murillo-Fuentes
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #66
Variational Bayesian Monte Carlo
Luigi Acerbi
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #67
Bayesian Alignments of Warped Multi-Output Gaussian Processes
Markus Kaiser · Clemens Otte · Thomas Runkler · Carl Henrik Ek
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #68
Automating Bayesian optimization with Bayesian optimization
Gustavo Malkomes · Roman Garnett
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #69
Infinite-Horizon Gaussian Processes
Arno Solin · James Hensman · Richard E Turner
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #70
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds
David Reeb · Andreas Doerr · Sebastian Gerwinn · Barbara Rakitsch
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #71
Algorithmic Linearly Constrained Gaussian Processes
Markus Lange-Hegermann
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #72
Efficient Projection onto the Perfect Phylogeny Model
Bei Jia · Surjyendu Ray · Sam Safavi · José Bento
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #73
Distributed $k$-Clustering for Data with Heavy Noise
Shi Li · Xiangyu Guo
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #74
Communication Compression for Decentralized Training
Hanlin Tang · Shaoduo Gan · Ce Zhang · Tong Zhang · Ji Liu
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #75
Do Less, Get More: Streaming Submodular Maximization with Subsampling
Moran Feldman · Amin Karbasi · Ehsan Kazemi
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #76
Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization
Rad Niazadeh · Tim Roughgarden · Joshua Wang
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #77
Provable Variational Inference for Constrained Log-Submodular Models
Josip Djolonga · Stefanie Jegelka · Andreas Krause
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #78
Fast greedy algorithms for dictionary selection with generalized sparsity constraints
Kaito Fujii · Tasuku Soma
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #79
Boolean Decision Rules via Column Generation
Sanjeeb Dash · Oktay Gunluk · Dennis Wei
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #80
Computing Kantorovich-Wasserstein Distances on $d$-dimensional histograms using $(d+1)$-partite graphs
Gennaro Auricchio · Federico Bassetti · Stefano Gualandi · Marco Veneroni
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #81
Adaptive Negative Curvature Descent with Applications in Non-convex Optimization
Mingrui Liu · Zhe Li · Xiaoyu Wang · Jinfeng Yi · Tianbao Yang
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #82
Implicit Bias of Gradient Descent on Linear Convolutional Networks
Suriya Gunasekar · Jason Lee · Daniel Soudry · Nati Srebro
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #83
Deep Generative Models for Distribution-Preserving Lossy Compression
Michael Tschannen · Eirikur Agustsson · Mario Lucic
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #84
Visual Object Networks: Image Generation with Disentangled 3D Representations
Jun-Yan Zhu · Zhoutong Zhang · Chengkai Zhang · Jiajun Wu · Antonio Torralba · Josh Tenenbaum · Bill Freeman
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #85
Nonlocal Neural Networks, Nonlocal Diffusion and Nonlocal Modeling
Yunzhe Tao · Qi Sun · Qiang Du · Wei Liu
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #86
Can We Gain More from Orthogonality Regularizations in Training Deep Networks?
Nitin Bansal · Xiaohan Chen · Zhangyang Wang
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #87
Discrimination-aware Channel Pruning for Deep Neural Networks
Zhuangwei Zhuang · Mingkui Tan · Bohan Zhuang · Jing Liu · Yong Guo · Qingyao Wu · Junzhou Huang · Jinhui Zhu
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #88
Probabilistic Model-Agnostic Meta-Learning
Chelsea Finn · Kelvin Xu · Sergey Levine
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #89
FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network
Aditya Kusupati · Manish Singh · Kush Bhatia · Ashish Kumar · Prateek Jain · Manik Varma
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #90
Understanding Batch Normalization
Nils Bjorck · Carla P Gomes · Bart Selman · Kilian Weinberger
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #91
How Many Samples are Needed to Estimate a Convolutional Neural Network?
Simon Du · Yining Wang · Xiyu Zhai · Sivaraman Balakrishnan · Ruslan Salakhutdinov · Aarti Singh
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #92
Robust Detection of Adversarial Attacks by Modeling the Intrinsic Properties of Deep Neural Networks
Zhihao Zheng · Pengyu Hong
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #93
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search
Zhuwen Li · Qifeng Chen · Vladlen Koltun
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #94
Automatic differentiation in ML: Where we are and where we should be going
Bart van Merrienboer · Olivier Breuleux · Arnaud Bergeron · Pascal Lamblin
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #95
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
Avital Oliver · Augustus Odena · Colin A Raffel · Ekin Dogus Cubuk · Ian Goodfellow
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #96
Toddler-Inspired Visual Object Learning
Sven Bambach · David Crandall · Linda Smith · Chen Yu
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #97
Generalisation in humans and deep neural networks
Robert Geirhos · Carlos R. M. Temme · Jonas Rauber · Heiko H. Schütt · Matthias Bethge · Felix A. Wichmann
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #98
Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures
Sergey Bartunov · Adam Santoro · Blake Richards · Luke Marris · Geoffrey E Hinton · Timothy Lillicrap
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #99
Incorporating Context into Language Encoding Models for fMRI
Shailee Jain · Alexander Huth
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #101
Mental Sampling in Multimodal Representations
Jianqiao Zhu · Adam Sanborn · Nick Chater
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #102
Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models
Amir Dezfouli · Richard Morris · Fabio Ramos · Peter Dayan · Bernard Balleine
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #103
Efficient inference for time-varying behavior during learning
Nicholas A Roy · Ji Hyun Bak · Athena Akrami · Carlos Brody · Jonathan W Pillow
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #104
Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals
Tom Dupré la Tour · Thomas Moreau · Mainak Jas · Alexandre Gramfort
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #105
Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks
Anirvan Sengupta · Cengiz Pehlevan · Mariano Tepper · Alexander Genkin · Dmitri Chklovskii
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #106
Connectionist Temporal Classification with Maximum Entropy Regularization
Hu Liu · Sheng Jin · Changshui Zhang
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #107
Removing the Feature Correlation Effect of Multiplicative Noise
Zijun Zhang · Yining Zhang · Zongpeng Li
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #108
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate
Mikhail Belkin · Daniel Hsu · Partha Mitra
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #109
Smoothed Analysis of Discrete Tensor Decomposition and Assemblies of Neurons
Nima Anari · Constantinos Daskalakis · Wolfgang Maass · Christos Papadimitriou · Amin Saberi · Santosh Vempala
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #110
Entropy and mutual information in models of deep neural networks
Marylou Gabrié · Andre Manoel · Clément Luneau · jean barbier · Nicolas Macris · Florent Krzakala · Lenka Zdeborová
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #111
The committee machine: Computational to statistical gaps in learning a two-layers neural network
Benjamin Aubin · Antoine Maillard · jean barbier · Florent Krzakala · Nicolas Macris · Lenka Zdeborová
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #112
A Unified Framework for Extensive-Form Game Abstraction with Bounds
Christian Kroer · Tuomas Sandholm
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #113
Connecting Optimization and Regularization Paths
Arun Suggala · Adarsh Prasad · Pradeep Ravikumar
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #114
Overlapping Clustering Models, and One (class) SVM to Bind Them All
Xueyu Mao · Purnamrita Sarkar · Deepayan Chakrabarti
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #115
Learning latent variable structured prediction models with Gaussian perturbations
Kevin Bello · Jean Honorio
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #116
Self-Supervised Generation of Spatial Audio for 360° Video
Pedro Morgado · Nuno Nvasconcelos · Timothy Langlois · Oliver Wang
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #117
Symbolic Graph Reasoning Meets Convolutions
Xiaodan Liang · Zhiting Hu · Hao Zhang · Liang Lin · Eric Xing
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #118
Towards Deep Conversational Recommendations
Raymond Li · Samira Ebrahimi Kahou · Hannes Schulz · Vincent Michalski · Laurent Charlin · Chris Pal
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #119
Human-in-the-Loop Interpretability Prior
Isaac Lage · Andrew Ross · Samuel J Gershman · Been Kim · Finale Doshi-Velez
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #120
Why Is My Classifier Discriminatory?
Irene Y Chen · Fredrik Johansson · David Sontag
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #121
Link Prediction Based on Graph Neural Networks
Muhan Zhang · Yixin Chen
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #122
KONG: Kernels for ordered-neighborhood graphs
Moez Draief · Konstantin Kutzkov · Kevin Scaman · Milan Vojnovic
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #123
Efficient Stochastic Gradient Hard Thresholding
Pan Zhou · Xiaotong Yuan · Jiashi Feng
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #124
Measures of distortion for machine learning
Leena Chennuru Vankadara · Ulrike von Luxburg
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #125
Relating Leverage Scores and Density using Regularized Christoffel Functions
Edouard Pauwels · Francis Bach · Jean-Philippe Vert
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #126
Streaming Kernel PCA with $\tilde{O}(\sqrt{n})$ Random Features
Md Enayat Ullah · Poorya Mianjy · Teodor Vanislavov Marinov · Raman Arora
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #127
Learning with SGD and Random Features
Luigi Carratino · Alessandro Rudi · Lorenzo Rosasco
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #128
But How Does It Work in Theory? Linear SVM with Random Features
Yitong Sun · Anna Gilbert · Ambuj Tewari
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #129
Statistical and Computational Trade-Offs in Kernel K-Means
Daniele Calandriello · Lorenzo Rosasco
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #130
Quadrature-based features for kernel approximation
Marina Munkhoeva · Yermek Kapushev · Evgeny Burnaev · Ivan Oseledets
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #131
Processing of missing data by neural networks
Marek Śmieja · Łukasz Struski · Jacek Tabor · Bartosz Zieliński · Przemysław Spurek
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #132
Constructing Deep Neural Networks by Bayesian Network Structure Learning
Raanan Y. Rohekar · Shami Nisimov · Yaniv Gurwicz · Guy Koren · Gal Novik
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #133
Mallows Models for Top-k Lists
Flavio Chierichetti · Anirban Dasgupta · Shahrzad Haddadan · Ravi Kumar · Silvio Lattanzi
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #134
Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification
Harsh Shrivastava · Eugene Bart · Bob Price · Hanjun Dai · Bo Dai · Srinivas Aluru
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #135
Maximum-Entropy Fine Grained Classification
Abhimanyu Dubey · Otkrist Gupta · Ramesh Raskar · Nikhil Naik
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #136
Efficient Loss-Based Decoding on Graphs for Extreme Classification
Itay Evron · Edward Moroshko · Koby Crammer
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #137
A no-regret generalization of hierarchical softmax to extreme multi-label classification
Marek Wydmuch · Kalina Jasinska · Mikhail Kuznetsov · Róbert Busa-Fekete · Krzysztof Dembczynski
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #138
Efficient Gradient Computation for Structured Output Learning with Rational and Tropical Losses
Corinna Cortes · Vitaly Kuznetsov · Mehryar Mohri · Dmitry Storcheus · Scott Yang
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #139
Deep Structured Prediction with Nonlinear Output Transformations
Colin Graber · Ofer Meshi · Alexander Schwing
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #140
Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction
Roei Herzig · Moshiko Raboh · Gal Chechik · Jonathan Berant · Amir Globerson
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #141
Large Margin Deep Networks for Classification
Gamaleldin Elsayed · Dilip Krishnan · Hossein Mobahi · Kevin Regan · Samy Bengio
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #142
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance
Neal Jean · Sang Michael Xie · Stefano Ermon
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #143
Multitask Boosting for Survival Analysis with Competing Risks
Alexis Bellot · Mihaela van der Schaar
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #144
Multi-Layered Gradient Boosting Decision Trees
Ji Feng · Yang Yu · Zhi-Hua Zhou
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #145
Unsupervised Adversarial Invariance
Ayush Jaiswal · Rex Yue Wu · Wael Abd-Almageed · Prem Natarajan
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #146
Learning Deep Disentangled Embeddings With the F-Statistic Loss
Karl Ridgeway · Michael Mozer
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #147
Learning Latent Subspaces in Variational Autoencoders
Jack Klys · Jake Snell · Richard Zemel
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #148
Dual Swap Disentangling
Zunlei Feng · Xinchao Wang · Chenglong Ke · An-Xiang Zeng · Dacheng Tao · Mingli Song
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #149
Joint Autoregressive and Hierarchical Priors for Learned Image Compression
David Minnen · Johannes Ballé · Johannes Ballé · George D Toderici
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #150
Group Equivariant Capsule Networks
Jan Eric Lenssen · Matthias Fey · Pascal Libuschewski
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #151
Learning Disentangled Joint Continuous and Discrete Representations
Emilien Dupont
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #152
Image-to-image translation for cross-domain disentanglement
Abel Gonzalez-Garcia · Joost van de Weijer · Yoshua Bengio
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB
Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization
Bruno Korbar · Du Tran · Lorenzo Torresani
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #154
Non-Adversarial Mapping with VAEs
Yedid Hoshen
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #155
Learning to Teach with Dynamic Loss Functions
Lijun Wu · Fei Tian · Yingce Xia · Yang Fan · Tao Qin · Lai Jian-Huang · Tie-Yan Liu
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #156
Maximizing acquisition functions for Bayesian optimization
James Wilson · Frank Hutter · Marc Deisenroth
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #157
MetaReg: Towards Domain Generalization using Meta-Regularization
Yogesh Balaji · Swami Sankaranarayanan · Rama Chellappa
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #158
Transfer Learning with Neural AutoML
Catherine Wong · Neil Houlsby · Yifeng Lu · Andrea Gesmundo
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #159
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies
Sungryull Sohn · Junhyuk Oh · Honglak Lee
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #160
Lifelong Inverse Reinforcement Learning
Jorge A Mendez · Shashank Shivkumar · Eric Eaton
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #161
Safe Active Learning for Time-Series Modeling with Gaussian Processes
Christoph Zimmer · Mona Meister · Duy Nguyen-Tuong
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #162
Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks
Agastya Kalra · Abdullah Rashwan · Wei-Shou Hsu · Pascal Poupart · Prashant Doshi · George Trimponias
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #163
Preference Based Adaptation for Learning Objectives
Yao-Xiang Ding · Zhi-Hua Zhou
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #164
Byzantine Stochastic Gradient Descent
Dan Alistarh · Zeyuan Allen-Zhu · Jerry Li
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #165
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms
Dylan Foster · Akshay Krishnamurthy
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #166
Online Learning of Quantum States
Scott Aaronson · Xinyi Chen · Elad Hazan · Satyen Kale · Ashwin Nayak
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #167
Horizon-Independent Minimax Linear Regression
Alan Malek · Peter Bartlett
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #168
Factored Bandits
Julian Zimmert · Yevgeny Seldin
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 517 AB #169
A Model for Learned Bloom Filters and Optimizing by Sandwiching
Michael Mitzenmacher