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165 Program Highlights »
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Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #1
Streamlining Variational Inference for Constraint Satisfaction Problems
Aditya Grover · Tudor Achim · Stefano Ermon
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #2
Robust Hypothesis Testing Using Wasserstein Uncertainty Sets
RUI GAO · Liyan Xie · Yao Xie · Huan Xu
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #3
Smoothed analysis of the low-rank approach for smooth semidefinite programs
Thomas Pumir · Samy Jelassi · Nicolas Boumal
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #4
Convergence of Cubic Regularization for Nonconvex Optimization under KL Property
Yi Zhou · Zhe Wang · Yingbin Liang
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #5
A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization
Zhize Li · Jian Li
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #6
Stochastic Chebyshev Gradient Descent for Spectral Optimization
Insu Han · Haim Avron · Jinwoo Shin
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #7
Proximal SCOPE for Distributed Sparse Learning
Shenyi Zhao · Gong-Duo Zhang · Ming-Wei Li · Wu-Jun Li
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #8
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning
Tianyi Chen · Georgios Giannakis · Tao Sun · Wotao Yin
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #9
Direct Runge-Kutta Discretization Achieves Acceleration
Jingzhao Zhang · Aryan Mokhtari · Suvrit Sra · Ali Jadbabaie
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #10
Optimal Algorithms for Non-Smooth Distributed Optimization in Networks
Kevin Scaman · Francis Bach · Sebastien Bubeck · Laurent Massoulié · Yin Tat Lee
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #11
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization
Blake Woodworth · Jialei Wang · Adam Smith · Brendan McMahan · Nati Srebro
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #12
(Probably) Concave Graph Matching
Haggai Maron · Yaron Lipman
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #13
Solving Non-smooth Constrained Programs with Lower Complexity than $\mathcal{O}(1/\varepsilon)$: A Primal-Dual Homotopy Smoothing Approach
Xiaohan Wei · Hao Yu · Qing Ling · Michael Neely
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #14
Wasserstein Distributionally Robust Kalman Filtering
Soroosh Shafieezadeh Abadeh · Viet Anh Nguyen · Daniel Kuhn · Peyman Mohajerin Esfahani
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #15
Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters
Pavel Dvurechenskii · Darina Dvinskikh · Alexander Gasnikov · Cesar Uribe · Angelia Nedich
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #16
Limited Memory Kelley's Method Converges for Composite Convex and Submodular Objectives
Song Zhou · Swati Gupta · Madeleine Udell
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #17
Stochastic Spectral and Conjugate Descent Methods
Dmitry Kovalev · Peter Richtarik · Eduard Gorbunov · Elnur Gasanov
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #18
Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima
Yaodong Yu · Pan Xu · Quanquan Gu
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #19
First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time
Yi Xu · Jing Rong · Tianbao Yang
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #20
Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation
Kush Bhatia · Aldo Pacchiano · Nicolas Flammarion · Peter Bartlett · Michael Jordan
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #21
Sparse DNNs with Improved Adversarial Robustness
Yiwen Guo · Chao Zhang · Changshui Zhang · Yurong Chen
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #22
Constructing Fast Network through Deconstruction of Convolution
Yunho Jeon · Junmo Kim
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #23
Learning Loop Invariants for Program Verification
Xujie Si · Hanjun Dai · Mukund Raghothaman · Mayur Naik · Le Song
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #24
Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction
Kevin Ellis · Lucas Morales · Mathias Sablé-Meyer · Armando Solar-Lezama · Josh Tenenbaum
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #25
Learning to Infer Graphics Programs from Hand-Drawn Images
Kevin Ellis · Daniel Ritchie · Armando Solar-Lezama · Josh Tenenbaum
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #26
Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation
Liwei Wang · Lunjia Hu · Jiayuan Gu · Zhiqiang Hu · Yue Wu · Kun He · John Hopcroft
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #27
Norm matters: efficient and accurate normalization schemes in deep networks
Elad Hoffer · Ron Banner · Itay Golan · Daniel Soudry
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #28
ResNet with one-neuron hidden layers is a Universal Approximator
Hongzhou Lin · Stefanie Jegelka
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #29
Hyperbolic Neural Networks
Octavian Ganea · Gary Becigneul · Thomas Hofmann
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #30
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
Kimin Lee · Kibok Lee · Honglak Lee · Jinwoo Shin
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #31
Improving Neural Program Synthesis with Inferred Execution Traces
Richard Shin · Illia Polosukhin · Dawn Song
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #32
Scaling the Poisson GLM to massive neural datasets through polynomial approximations
David Zoltowski · Jonathan W Pillow
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #33
Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning
Supasorn Suwajanakorn · Noah Snavely · Jonathan Tompson · Mohammad Norouzi
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #34
Learning to Reconstruct Shapes from Unseen Classes
Xiuming Zhang · Zhoutong Zhang · Chengkai Zhang · Josh Tenenbaum · Bill Freeman · Jiajun Wu
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #35
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
Dan Hendrycks · Mantas Mazeika · Duncan Wilson · Kevin Gimpel
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #37
A Reduction for Efficient LDA Topic Reconstruction
Matteo Almanza · Flavio Chierichetti · Alessandro Panconesi · Andrea Vattani
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #38
A Unified View of Piecewise Linear Neural Network Verification
Rudy Bunel · Ilker Turkaslan · Philip Torr · Pushmeet Kohli · Pawan K Mudigonda
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #39
Optimization over Continuous and Multi-dimensional Decisions with Observational Data
Dimitris Bertsimas · Christopher McCord
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #40
The Convergence of Sparsified Gradient Methods
Dan Alistarh · Torsten Hoefler · Mikael Johansson · Nikola Konstantinov · Sarit Khirirat · Cedric Renggli
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #41
Estimating Learnability in the Sublinear Data Regime
Weihao Kong · Gregory Valiant
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #42
Learning convex polytopes with margin
Lee-Ad Gottlieb · Eran Kaufman · Aryeh Kontorovich · Gabriel Nivasch
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #43
Ridge Regression and Provable Deterministic Ridge Leverage Score Sampling
Shannon McCurdy
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #44
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization
Shusen Wang · Farbod Roosta-Khorasani · Peng Xu · Michael W Mahoney
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #45
Wavelet regression and additive models for irregularly spaced data
Asad Haris · Ali Shojaie · Noah Simon
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #46
New Insight into Hybrid Stochastic Gradient Descent: Beyond With-Replacement Sampling and Convexity
Pan Zhou · Xiaotong Yuan · Jiashi Feng
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #47
Sample Efficient Stochastic Gradient Iterative Hard Thresholding Method for Stochastic Sparse Linear Regression with Limited Attribute Observation
Tomoya Murata · Taiji Suzuki
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #48
Robust Subspace Approximation in a Stream
Roie Levin · Anish Prasad Sevekari · David Woodruff
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #50
A Practical Algorithm for Distributed Clustering and Outlier Detection
Jiecao Chen · Erfan Sadeqi Azer · Qin Zhang
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #51
Compact Representation of Uncertainty in Clustering
Craig Greenberg · Nicholas Monath · Ari Kobren · Patrick Flaherty · Andrew McGregor · Andrew McCallum
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #52
Bipartite Stochastic Block Models with Tiny Clusters
Stefan Neumann
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #53
Clustering Redemption–Beyond the Impossibility of Kleinberg’s Axioms
Vincent Cohen-Addad · Varun Kanade · Frederik Mallmann-Trenn
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #54
Understanding Regularized Spectral Clustering via Graph Conductance
Yilin Zhang · Karl Rohe
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #55
Query K-means Clustering and the Double Dixie Cup Problem
I Chien · Chao Pan · Olgica Milenkovic
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #56
How to tell when a clustering is (approximately) correct using convex relaxations
Marina Meila
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #57
Derivative Estimation in Random Design
Yu Liu · Kris De Brabanter
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #58
Exploiting Numerical Sparsity for Efficient Learning : Faster Eigenvector Computation and Regression
Neha Gupta · Aaron Sidford
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #59
Boosted Sparse and Low-Rank Tensor Regression
Lifang He · Kun Chen · Wanwan Xu · Jiayu Zhou · Fei Wang
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #60
An Efficient Pruning Algorithm for Robust Isotonic Regression
Cong Han Lim
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #61
A convex program for bilinear inversion of sparse vectors
Alireza Aghasi · Ali Ahmed · Paul Hand · Babhru Joshi
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #62
Efficient Convex Completion of Coupled Tensors using Coupled Nuclear Norms
Kishan Wimalawarne · Hiroshi Mamitsuka
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #63
Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds
Raghav Somani · Chirag Gupta · Prateek Jain · Praneeth Netrapalli
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #64
Learning without the Phase: Regularized PhaseMax Achieves Optimal Sample Complexity
Fariborz Salehi · Ehsan Abbasi · Babak Hassibi
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #65
On Controllable Sparse Alternatives to Softmax
Anirban Laha · Saneem Ahmed Chemmengath · Priyanka Agrawal · Mitesh Khapra · Karthik Sankaranarayanan · Harish Ramaswamy
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #66
Sparse PCA from Sparse Linear Regression
Guy Bresler · Sung Min Park · Madalina Persu
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #67
Efficient Anomaly Detection via Matrix Sketching
Vatsal Sharan · Parikshit Gopalan · Udi Wieder
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #69
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization
Minshuo Chen · Lin Yang · Mengdi Wang · Tuo Zhao
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #70
Contrastive Learning from Pairwise Measurements
Yi Chen · Zhuoran Yang · Yuchen Xie · Princeton Zhaoran Wang
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #71
Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions
Minhyuk Sung · Hao Su · Ronald Yu · Leonidas J Guibas
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #72
Large Scale computation of Means and Clusters for Persistence Diagrams using Optimal Transport
Theo Lacombe · Marco Cuturi · Steve OUDOT
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #73
Representation Learning of Compositional Data
Marta Avalos · Richard Nock · Cheng Soon Ong · Julien Rouar · Ke Sun
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #74
How To Make the Gradients Small Stochastically: Even Faster Convex and Nonconvex SGD
Zeyuan Allen-Zhu
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #75
Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes
Loucas Pillaud-Vivien · Alessandro Rudi · Francis Bach
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #76
Optimal Subsampling with Influence Functions
Daniel Ting · Eric Brochu
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #77
Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators
Isao Ishikawa · Keisuke Fujii · Masahiro Ikeda · Yuka Hashimoto · Yoshinobu Kawahara
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #78
Random Feature Stein Discrepancies
Jonathan Huggins · Lester Mackey
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #79
Informative Features for Model Comparison
Wittawat Jitkrittum · Heishiro Kanagawa · Patsorn Sangkloy · James Hays · Bernhard Schölkopf · Arthur Gretton
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #80
On Fast Leverage Score Sampling and Optimal Learning
Alessandro Rudi · Daniele Calandriello · Luigi Carratino · Lorenzo Rosasco
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #81
Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams
Tam Le · Makoto Yamada
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #82
Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels
Shahin Shahrampour · Vahid Tarokh
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #83
RetGK: Graph Kernels based on Return Probabilities of Random Walks
Zhen Zhang · Mianzhi Wang · Yijian Xiang · Yan Huang · Arye Nehorai
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #84
Nonparametric Density Estimation under Adversarial Losses
Shashank Singh · Ananya Uppal · Boyue Li · Chun-Liang Li · Manzil Zaheer · Barnabas Poczos
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #85
Deep Homogeneous Mixture Models: Representation, Separation, and Approximation
Priyank Jaini · Pascal Poupart · Yaoliang Yu
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #86
Gaussian Process Conditional Density Estimation
Vincent Dutordoir · Hugh Salimbeni · James Hensman · Marc Deisenroth
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #87
Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames
Geneviève Robin · Hoi-To Wai · Julie Josse · Olga Klopp · Eric Moulines
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #88
Mixture Matrix Completion
Daniel Pimentel-Alarcon
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #89
Multivariate Time Series Imputation with Generative Adversarial Networks
Yonghong Luo · Xiangrui Cai · Ying ZHANG · Jun Xu · Yuan xiaojie
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #90
Fully Understanding The Hashing Trick
Lior Kamma · Casper B. Freksen · Kasper Green Larsen
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #91
Learning semantic similarity in a continuous space
Michel Deudon
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #92
Bilevel learning of the Group Lasso structure
Jordan Frecon · Saverio Salzo · Massimiliano Pontil
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #93
Bayesian Structure Learning by Recursive Bootstrap
Raanan Y. Rohekar · Yaniv Gurwicz · Shami Nisimov · Guy Koren · Gal Novik
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #94
Learning from Group Comparisons: Exploiting Higher Order Interactions
Yao Li · Minhao Cheng · Kevin Fujii · Fushing Hsieh · Cho-Jui Hsieh
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #95
A Structured Prediction Approach for Label Ranking
Anna Korba · Alexandre Garcia · Florence d'Alché-Buc
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #96
The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models
Chen Dan · Liu Leqi · Bryon Aragam · Pradeep Ravikumar · Eric Xing
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #97
Binary Classification from Positive-Confidence Data
Takashi Ishida · Gang Niu · Masashi Sugiyama
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #98
Learning SMaLL Predictors
Vikas Garg · Ofer Dekel · Lin Xiao
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #99
Contour location via entropy reduction leveraging multiple information sources
Alexandre Marques · Remi Lam · Karen Willcox
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #100
Multi-Class Learning: From Theory to Algorithm
Jian Li · Yong Liu · Rong Yin · Hua Zhang · Lizhong Ding · Weiping Wang
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #101
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
Zhilu Zhang · Mert Sabuncu
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #102
A Smoother Way to Train Structured Prediction Models
Venkata Krishna Pillutla · Vincent Roulet · Sham Kakade · Zaid Harchaoui
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #103
Constrained Graph Variational Autoencoders for Molecule Design
Qi Liu · Miltiadis Allamanis · Marc Brockschmidt · Alexander Gaunt
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #104
Learning Beam Search Policies via Imitation Learning
Renato Negrinho · Matthew Gormley · Geoffrey Gordon
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #105
Loss Functions for Multiset Prediction
Sean Welleck · Zixin Yao · Yu Gai · Jialin Mao · Zheng Zhang · Kyunghyun Cho
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #106
Learning Confidence Sets using Support Vector Machines
Wenbo Wang · Xingye Qiao
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #107
Fast Similarity Search via Optimal Sparse Lifting
Wenye Li · Jingwei Mao · Yin Zhang · Shuguang Cui
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #108
The Sparse Manifold Transform
Yubei Chen · Dylan Paiton · Bruno Olshausen
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #109
When do random forests fail?
Cheng Tang · Damien Garreau · Ulrike von Luxburg
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #110
Diverse Ensemble Evolution: Curriculum Data-Model Marriage
Tianyi Zhou · Shengjie Wang · Jeff Bilmes
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #111
The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning
Jesse Krijthe · Marco Loog
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #112
Semi-Supervised Learning with Declaratively Specified Entropy Constraints
Haitian Sun · William Cohen · Lidong Bing
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #113
Learning to Multitask
Yu Zhang · Ying Wei · Qiang Yang
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #114
CatBoost: unbiased boosting with categorical features
Liudmila Prokhorenkova · Gleb Gusev · Aleksandr Vorobev · Anna Veronika Dorogush · Andrey Gulin
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #115
Supervised autoencoders: Improving generalization performance with unsupervised regularizers
Lei Le · Andrew Patterson · Martha White
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #116
Representation Learning for Treatment Effect Estimation from Observational Data
Liuyi Yao · Sheng Li · Yaliang Li · Mengdi Huai · Jing Gao · Aidong Zhang
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #117
SimplE Embedding for Link Prediction in Knowledge Graphs
Seyed Mehran Kazemi · David Poole
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #118
DeepProbLog: Neural Probabilistic Logic Programming
Robin Manhaeve · Sebastijan Dumancic · Angelika Kimmig · Thomas Demeester · Luc De Raedt
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #119
Watch Your Step: Learning Node Embeddings via Graph Attention
Sami Abu-El-Haija · Bryan Perozzi · Rami Al-Rfou · Alexander Alemi
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #120
Invariant Representations without Adversarial Training
Daniel Moyer · Shuyang Gao · Rob Brekelmans · Aram Galstyan · Greg Ver Steeg
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #121
Domain-Invariant Projection Learning for Zero-Shot Recognition
An Zhao · Mingyu Ding · Jiechao Guan · Zhiwu Lu · Tao Xiang · Ji-Rong Wen
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #122
Unsupervised Learning of View-invariant Action Representations
Junnan Li · Yongkang Wong · Qi Zhao · Mohan Kankanhalli
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #123
Neural Architecture Optimization
Renqian Luo · Fei Tian · Tao Qin · Enhong Chen · Tie-Yan Liu
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #124
Scalable Hyperparameter Transfer Learning
Valerio Perrone · Rodolphe Jenatton · Matthias W Seeger · Cedric Archambeau
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #125
Learning To Learn Around A Common Mean
Giulia Denevi · Carlo Ciliberto · Dimitris Stamos · Massimiliano Pontil
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #126
Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation
JING LI · Rafal Mantiuk · Junle Wang · Suiyi Ling · Patrick Le Callet
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #127
Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation
Shivapratap Gopakumar · Sunil Gupta · Santu Rana · Vu Nguyen · Svetha Venkatesh
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #128
Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners
Yuxin Chen · Adish Singla · Oisin Mac Aodha · Pietro Perona · Yisong Yue
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #129
Active Learning for Non-Parametric Regression Using Purely Random Trees
Jack Goetz · Ambuj Tewari · Paul Zimmerman
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #130
Interactive Structure Learning with Structural Query-by-Committee
Christopher Tosh · Sanjoy Dasgupta
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #131
Efficient nonmyopic batch active search
Shali Jiang · Gustavo Malkomes · Matthew Abbott · Benjamin Moseley · Roman Garnett
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #132
Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss
Stephen Mussmann · Percy Liang
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #133
Online Adaptive Methods, Universality and Acceleration
Yehuda Kfir Levy · Alp Yurtsever · Volkan Cevher
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #134
Online Improper Learning with an Approximation Oracle
Elad Hazan · Wei Hu · Yuanzhi Li · zhiyuan li
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #135
Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting
Hippolyt Ritter · Aleksandar Botev · David Barber
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #136
Approximating Real-Time Recurrent Learning with Random Kronecker Factors
Asier Mujika · Florian Meier · Angelika Steger
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #137
Online Reciprocal Recommendation with Theoretical Performance Guarantees
Claudio Gentile · Nikos Parotsidis · Fabio Vitale
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #138
Generalized Inverse Optimization through Online Learning
Chaosheng Dong · Yiran Chen · Bo Zeng
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #139
Adaptive Online Learning in Dynamic Environments
Lijun Zhang · Shiyin Lu · Zhi-Hua Zhou
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #140
Online convex optimization for cumulative constraints
Jianjun Yuan · Andrew Lamperski
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #141
Efficient online algorithms for fast-rate regret bounds under sparsity
Pierre Gaillard · Olivier Wintenberger
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #142
Regret Bounds for Online Portfolio Selection with a Cardinality Constraint
Shinji Ito · Daisuke Hatano · Sumita Hanna · Akihiro Yabe · Takuro Fukunaga · Naonori Kakimura · Ken-Ichi Kawarabayashi
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #144
Policy Regret in Repeated Games
Raman Arora · Michael Dinitz · Teodor Vanislavov Marinov · Mehryar Mohri
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #145
Query Complexity of Bayesian Private Learning
Kuang Xu
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #146
The Limits of Post-Selection Generalization
Jonathan Ullman · Adam Smith · Kobbi Nissim · Uri Stemmer · Thomas Steinke
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #147
Sequential Test for the Lowest Mean: From Thompson to Murphy Sampling
Emilie Kaufmann · Wouter Koolen · Aurélien Garivier
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #148
Contextual Combinatorial Multi-armed Bandits with Volatile Arms and Submodular Reward
Lixing Chen · Jie Xu · Zhuo Lu
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #149
TopRank: A practical algorithm for online stochastic ranking
Tor Lattimore · Branislav Kveton · Shuai Li · Csaba Szepesvari
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #150
A Bandit Approach to Sequential Experimental Design with False Discovery Control
Kevin Jamieson · Lalit Jain
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #151
Adaptation to Easy Data in Prediction with Limited Advice
Tobias Thune · Yevgeny Seldin
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #152
Differentially Private Contextual Linear Bandits
Roshan Shariff · Or Sheffet
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #153
Community Exploration: From Offline Optimization to Online Learning
Xiaowei Chen · Weiran Huang · Wei Chen · John C. S. Lui
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #154
Adaptive Learning with Unknown Information Flows
Yonatan Gur · Ahmadreza Momeni
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #155
Multi-armed Bandits with Compensation
Siwei Wang · Longbo Huang
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #156
Bandit Learning with Implicit Feedback
Yi Qi · Qingyun Wu · Hongning Wang · Jie Tang · Maosong Sun
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #157
Optimistic optimization of a Brownian
Jean-Bastien Grill · Michal Valko · Remi Munos
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #158
Bandit Learning with Positive Externalities
Virag Shah · Jose Blanchet · Ramesh Johari
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #159
An Information-Theoretic Analysis for Thompson Sampling with Many Actions
Shi Dong · Benjamin Van Roy
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #160
Distributed Multi-Player Bandits - a Game of Thrones Approach
Ilai Bistritz · Amir Leshem
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #161
PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits
Bianca Dumitrascu · Karen Feng · Barbara Engelhardt
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #162
Non-delusional Q-learning and value-iteration
Tyler Lu · Dale Schuurmans · Craig Boutilier
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #163
Differentiable MPC for End-to-end Planning and Control
Brandon Amos · Ivan Jimenez · Jacob I Sacks · Byron Boots · J. Zico Kolter
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #164
Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning
Yonathan Efroni · Gal Dalal · Bruno Scherrer · Shie Mannor
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #165
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
Kurtland Chua · Roberto Calandra · Rowan McAllister · Sergey Levine
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #166
Learning convex bounds for linear quadratic control policy synthesis
Jack Umenberger · Thomas Schön
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #167
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
Jacob Buckman · Danijar Hafner · George Tucker · Eugene Brevdo · Honglak Lee
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB #168
Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes
Andrea Tirinzoni · Marek Petrik · Xiangli Chen · Brian Ziebart
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 517 AB
Why so gloomy? A Bayesian explanation of human pessimism bias in the multi-armed bandit task
Dalin Guo · Angela J Yu