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Oral
Thu Jun 13 09:00 AM -- 09:20 AM (PDT) @ Room 104
Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning
In Non-convex Optimization
Dong Yin · Yudong Chen · Kannan Ramchandran · Peter Bartlett
[ Oral] 
Oral
Thu Jun 13 09:00 AM -- 09:20 AM (PDT) @ Room 103
Geometric Losses for Distributional Learning
In Supervised and Transfer Learning
Arthur Mensch · Mathieu Blondel · Gabriel Peyré
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:00 AM -- 09:20 AM (PDT) @ Hall B
Batch Policy Learning under Constraints
In Reinforcement Learning
Hoang Le · Cameron Voloshin · Yisong Yue
[ Slides] 
Oral
Thu Jun 13 09:00 AM -- 09:20 AM (PDT) @ Hall A
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations
In Deep Generative Models
Alex Lamb · Jonathan Binas · Anirudh Goyal · Sandeep Subramanian · Ioannis Mitliagkas · Yoshua Bengio · Michael Mozer
[ Slides] 
Oral
Thu Jun 13 09:00 AM -- 09:20 AM (PDT) @ Grand Ballroom
Neural Network Attributions: A Causal Perspective
In Interpretability
Aditya Chattopadhyay · Piyushi Manupriya · Anirban Sarkar · Vineeth N Balasubramanian
Oral
Thu Jun 13 09:00 AM -- 09:20 AM (PDT) @ Room 201
Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random
In Applications
Xiaojie Wang · Rui Zhang · Yu Sun · Jianzhong Qi
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:00 AM -- 09:20 AM (PDT) @ Room 102
Matrix-Free Preconditioning in Online Learning
In Online Learning
Ashok Cutkosky · Tamas Sarlos
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:00 AM -- 09:20 AM (PDT) @ Room 101
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions
In Monte Carlo Methods
Antoine Liutkus · Umut Simsekli · Szymon Majewski · Alain Durmus · Fabian-Robert Stöter
[ Oral] 
Oral
Thu Jun 13 09:00 AM -- 09:20 AM (PDT) @ Seaside Ballroom
On Sparse Linear Regression in the Local Differential Privacy Model
In Privacy and Fairness
Di Wang · Jinhui Xu
[ Oral] 
Oral
Thu Jun 13 09:20 AM -- 09:25 AM (PDT) @ Seaside Ballroom
Differentially Private Empirical Risk Minimization with Non-convex Loss Functions
In Privacy and Fairness
Di Wang · Changyou Chen · Jinhui Xu
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:20 AM -- 09:25 AM (PDT) @ Room 102
Online Convex Optimization in Adversarial Markov Decision Processes
In Online Learning
Aviv Rosenberg · Yishay Mansour
Oral
Thu Jun 13 09:20 AM -- 09:25 AM (PDT) @ Room 104
Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization
In Non-convex Optimization
Baojian Zhou · Feng Chen · Yiming Ying
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:20 AM -- 09:25 AM (PDT) @ Hall B
Quantifying Generalization in Reinforcement Learning
In Reinforcement Learning
Karl Cobbe · Oleg Klimov · Chris Hesse · Taehoon Kim · John Schulman
[ Slides] 
Oral
Thu Jun 13 09:20 AM -- 09:25 AM (PDT) @ Room 103
Classification from Positive, Unlabeled and Biased Negative Data
In Supervised and Transfer Learning
Yu-Guan Hsieh · Gang Niu · Masashi Sugiyama
Oral
Thu Jun 13 09:20 AM -- 09:25 AM (PDT) @ Grand Ballroom
Towards a Deep and Unified Understanding of Deep Neural Models in NLP
In Interpretability
Chaoyu Guan · Xiting Wang · Quanshi Zhang · Runjin Chen · Di He · Xing Xie
[ Slides] 
Oral
Thu Jun 13 09:20 AM -- 09:25 AM (PDT) @ Room 201
Linear-Complexity Data-Parallel Earth Mover's Distance Approximations
In Applications
Kubilay Atasu · Thomas Mittelholzer
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:20 AM -- 09:25 AM (PDT) @ Room 101
Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization
In Monte Carlo Methods
Thanh Huy Nguyen · Umut Simsekli · Gaël RICHARD
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:20 AM -- 09:25 AM (PDT) @ Hall A
Variational Laplace Autoencoders
In Deep Generative Models
Yookoon Park · Chris Kim · Gunhee Kim
[ Slides] 
Oral
Thu Jun 13 09:25 AM -- 09:30 AM (PDT) @ Hall B
Learning Latent Dynamics for Planning from Pixels
In Reinforcement Learning
Danijar Hafner · Timothy Lillicrap · Ian Fischer · Ruben Villegas · David Ha · Honglak Lee · James Davidson
[ Slides] 
Oral
Thu Jun 13 09:25 AM -- 09:30 AM (PDT) @ Room 103
Complementary-Label Learning for Arbitrary Losses and Models
In Supervised and Transfer Learning
Takashi Ishida · Gang Niu · Aditya Menon · Masashi Sugiyama
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:25 AM -- 09:30 AM (PDT) @ Room 104
Neuron birth-death dynamics accelerates gradient descent and converges asymptotically
In Non-convex Optimization
Grant Rotskoff · Samy Jelassi · Joan Bruna · Eric Vanden-Eijnden
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:25 AM -- 09:30 AM (PDT) @ Hall A
Latent Normalizing Flows for Discrete Sequences
In Deep Generative Models
Zachary Ziegler · Alexander Rush
[ Slides] 
Oral
Thu Jun 13 09:25 AM -- 09:30 AM (PDT) @ Room 101
Unifying Orthogonal Monte Carlo Methods
In Monte Carlo Methods
Krzysztof Choromanski · Mark Rowland · Wenyu Chen · Adrian Weller
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:25 AM -- 09:30 AM (PDT) @ Grand Ballroom
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation
In Interpretability
Marco Ancona · Cengiz Oztireli · Markus Gross
[ Slides] 
Oral
Thu Jun 13 09:25 AM -- 09:30 AM (PDT) @ Room 201
Model Comparison for Semantic Grouping
In Applications
Francisco Vargas · Kamen Brestnichki · Nils Hammerla
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:25 AM -- 09:30 AM (PDT) @ Room 102
Competing Against Nash Equilibria in Adversarially Changing Zero-Sum Games
In Online Learning
Adrian Rivera Cardoso · Jacob Abernethy · He Wang · Huan Xu
[ Slides] 
Oral
Thu Jun 13 09:25 AM -- 09:30 AM (PDT) @ Seaside Ballroom
Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy
In Privacy and Fairness
Kareem Amin · Alex Kulesza · andres munoz · Sergei Vassilvitskii
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:30 AM -- 09:35 AM (PDT) @ Room 103
Learning to Infer Program Sketches
In Supervised and Transfer Learning
Maxwell Nye · Luke Hewitt · Josh Tenenbaum · Armando Solar-Lezama
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:30 AM -- 09:35 AM (PDT) @ Seaside Ballroom
Differentially Private Learning of Geometric Concepts
In Privacy and Fairness
Haim Kaplan · Yishay Mansour · Yossi Matias · Uri Stemmer
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:30 AM -- 09:35 AM (PDT) @ Hall B
Projections for Approximate Policy Iteration Algorithms
In Reinforcement Learning
Riad Akrour · Joni Pajarinen · Jan Peters · Gerhard Neumann
[ Slides] 
Oral
Thu Jun 13 09:30 AM -- 09:35 AM (PDT) @ Hall A
Multi-objective training of Generative Adversarial Networks with multiple discriminators
In Deep Generative Models
Isabela Albuquerque · Joao Monteiro · Thang Doan · Breandan Considine · Tiago Falk · Ioannis Mitliagkas
[ Slides] 
Oral
Thu Jun 13 09:30 AM -- 09:35 AM (PDT) @ Room 102
Online Learning with Sleeping Experts and Feedback Graphs
In Online Learning
Corinna Cortes · Giulia DeSalvo · Claudio Gentile · Mehryar Mohri · Scott Yang
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:30 AM -- 09:35 AM (PDT) @ Grand Ballroom
Functional Transparency for Structured Data: a Game-Theoretic Approach
In Interpretability
Guang-He Lee · Wengong Jin · David Alvarez-Melis · Tommi Jaakkola
[ Slides] 
Oral
Thu Jun 13 09:30 AM -- 09:35 AM (PDT) @ Room 101
Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits
In Monte Carlo Methods
Martin Zhang · James Zou · David Tse
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:30 AM -- 09:35 AM (PDT) @ Room 104
Width Provably Matters in Optimization for Deep Linear Neural Networks
In Non-convex Optimization
Simon Du · Wei Hu
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:30 AM -- 09:35 AM (PDT) @ Room 201
RaFM: Rank-Aware Factorization Machines
In Applications
Xiaoshuang Chen · Yin Zheng · Jiaxing Wang · Wenye Ma · Junzhou Huang
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:35 AM -- 09:40 AM (PDT) @ Room 102
Incremental Randomized Sketching for Online Kernel Learning
In Online Learning
Xiao Zhang · Shizhong Liao
[ Slides] 
Oral
Thu Jun 13 09:35 AM -- 09:40 AM (PDT) @ Seaside Ballroom
Toward Controlling Discrimination in Online Ad Auctions
In Privacy and Fairness
L. Elisa Celis · Anay Mehrotra · Nisheeth Vishnoi
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:35 AM -- 09:40 AM (PDT) @ Hall A
Learning Discrete and Continuous Factors of Data via Alternating Disentanglement
In Deep Generative Models
Yeonwoo Jeong · Hyun Oh Song
[ Slides] 
Oral
Thu Jun 13 09:35 AM -- 09:40 AM (PDT) @ Hall B
Learning Structured Decision Problems with Unawareness
In Reinforcement Learning
Craig Innes · Alex Lascarides
[ Slides] 
Oral
Thu Jun 13 09:35 AM -- 09:40 AM (PDT) @ Room 103
Hierarchically Structured Meta-learning
In Supervised and Transfer Learning
Huaxiu Yao · Ying WEI · Junzhou Huang · Zhenhui (Jessie) Li
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:35 AM -- 09:40 AM (PDT) @ Grand Ballroom
Exploring interpretable LSTM neural networks over multi-variable data
In Interpretability
Tian Guo · Tao Lin · Nino Antulov-Fantulin
[ Slides] 
Oral
Thu Jun 13 09:35 AM -- 09:40 AM (PDT) @ Room 104
Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path?
In Non-convex Optimization
Samet Oymak · Mahdi Soltanolkotabi
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:35 AM -- 09:40 AM (PDT) @ Room 101
Metropolis-Hastings Generative Adversarial Networks
In Monte Carlo Methods
Ryan Turner · Jane Hung · Eric Frank · Yunus Saatchi · Jason Yosinski
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:35 AM -- 09:40 AM (PDT) @ Room 201
CAB: Continuous Adaptive Blending for Policy Evaluation and Learning
In Applications
Yi Su · Luke Lequn Wang · Michele Santacatterina · Thorsten Joachims
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:40 AM -- 10:00 AM (PDT) @ Hall A
Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables
In Deep Generative Models
Friso Kingma · Pieter Abbeel · Jonathan Ho
Oral
Thu Jun 13 09:40 AM -- 10:00 AM (PDT) @ Room 104
Power k-Means Clustering
In Non-convex Optimization
Jason Xu · Kenneth Lange
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:40 AM -- 10:00 AM (PDT) @ Seaside Ballroom
Learning Optimal Fair Policies
In Privacy and Fairness
Razieh Nabi · Daniel Malinsky · Ilya Shpitser
[ Oral] 
Oral
Thu Jun 13 09:40 AM -- 10:00 AM (PDT) @ Room 102
Adaptive Scale-Invariant Online Algorithms for Learning Linear Models
In Online Learning
Michal Kempka · Wojciech Kotlowski · Manfred K. Warmuth
[ Oral] 
Oral
Thu Jun 13 09:40 AM -- 10:00 AM (PDT) @ Room 201
MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement
In Applications
Szu-Wei Fu · Chien-Feng Liao · Yu Tsao · Shou-De Lin
[ Oral] 
Oral
Thu Jun 13 09:40 AM -- 10:00 AM (PDT) @ Grand Ballroom
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
In Interpretability
Augustus Odena · Catherine Olsson · David Andersen · Ian Goodfellow
Oral
Thu Jun 13 09:40 AM -- 10:00 AM (PDT) @ Room 103
Bridging Theory and Algorithm for Domain Adaptation
In Supervised and Transfer Learning
Yuchen Zhang · Tianle Liu · Mingsheng Long · Michael Jordan
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:40 AM -- 10:00 AM (PDT) @ Room 101
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets
In Monte Carlo Methods
Rob Cornish · Paul Vanetti · Alexandre Bouchard-Côté · George Deligiannidis · Arnaud Doucet
[ Slides]  [ Oral] 
Oral
Thu Jun 13 09:40 AM -- 10:00 AM (PDT) @ Hall B
Calibrated Model-Based Deep Reinforcement Learning
In Reinforcement Learning
Ali Malik · Volodymyr Kuleshov · Jiaming Song · Danny Nemer · Harlan Seymour · Stefano Ermon
[ Slides] 
Oral
Thu Jun 13 10:00 AM -- 10:05 AM (PDT) @ Seaside Ballroom
Fairness-Aware Learning for Continuous Attributes and Treatments
In Privacy and Fairness
Jeremie Mary · Clément Calauzènes · Noureddine El Karoui
[ Slides]  [ Oral] 
Oral
Thu Jun 13 10:00 AM -- 10:05 AM (PDT) @ Room 102
Online Control with Adversarial Disturbances
In Online Learning
Naman Agarwal · Brian Bullins · Elad Hazan · Sham Kakade · Karan Singh
[ Slides]  [ Oral] 
Oral
Thu Jun 13 10:00 AM -- 10:05 AM (PDT) @ Room 103
Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation
In Supervised and Transfer Learning
Shani Gamrian · Yoav Goldberg
[ Slides]  [ Oral] 
Oral
Thu Jun 13 10:00 AM -- 10:05 AM (PDT) @ Hall A
Graphite: Iterative Generative Modeling of Graphs
In Deep Generative Models
Aditya Grover · Aaron Zweig · Stefano Ermon
[ Slides] 
Oral
Thu Jun 13 10:00 AM -- 10:05 AM (PDT) @ Room 104
Distributed Learning over Unreliable Networks
In Non-convex Optimization
Chen Yu · Hanlin Tang · Cedric Renggli · Simon Kassing · Ankit Singla · Dan Alistarh · Ce Zhang · Ji Liu
[ Slides]  [ Oral] 
Oral
Thu Jun 13 10:00 AM -- 10:05 AM (PDT) @ Room 101
Replica Conditional Sequential Monte Carlo
In Monte Carlo Methods
Alex Shestopaloff · Arnaud Doucet
[ Slides]  [ Oral] 
Oral
Thu Jun 13 10:00 AM -- 10:05 AM (PDT) @ Hall B
Reinforcement Learning in Configurable Continuous Environments
In Reinforcement Learning
Alberto Maria Metelli · Emanuele Ghelfi · Marcello Restelli
[ Slides] 
Oral
Thu Jun 13 10:00 AM -- 10:05 AM (PDT) @ Room 201
Neural Separation of Observed and Unobserved Distributions
In Applications
Tavi Halperin · Ariel Ephrat · Yedid Hoshen
[ Slides]  [ Oral] 
Oral
Thu Jun 13 10:00 AM -- 10:05 AM (PDT) @ Grand Ballroom
Gaining Free or Low-Cost Interpretability with Interpretable Partial Substitute
In Interpretability
Tong Wang
[ Slides] 
Oral
Thu Jun 13 10:05 AM -- 10:10 AM (PDT) @ Room 201
Almost Unsupervised Text to Speech and Automatic Speech Recognition
In Applications
Yi Ren · Xu Tan · Tao Qin · Sheng Zhao · Zhou Zhao · Tie-Yan Liu
[ Slides]  [ Oral] 
Oral
Thu Jun 13 10:05 AM -- 10:10 AM (PDT) @ Hall B
Target-Based Temporal-Difference Learning
In Reinforcement Learning
Donghwan Lee · Niao He
[ Slides] 
Oral
Thu Jun 13 10:05 AM -- 10:10 AM (PDT) @ Hall A
Hybrid Models with Deep and Invertible Features
In Deep Generative Models
Eric Nalisnick · Akihiro Matsukawa · Yee-Whye Teh · Dilan Gorur · Balaji Lakshminarayanan
[ Slides] 
Oral
Thu Jun 13 10:05 AM -- 10:10 AM (PDT) @ Grand Ballroom
State-Regularized Recurrent Neural Networks
In Interpretability
Cheng Wang · Mathias Niepert
[ Slides] 
Oral
Thu Jun 13 10:05 AM -- 10:10 AM (PDT) @ Room 104
Escaping Saddle Points with Adaptive Gradient Methods
In Non-convex Optimization
Matthew Staib · Sashank Jakkam Reddi · Satyen Kale · Sanjiv Kumar · Suvrit Sra
[ Slides]  [ Oral] 
Oral
Thu Jun 13 10:05 AM -- 10:10 AM (PDT) @ Seaside Ballroom
Fairness risk measures
In Privacy and Fairness
Robert C Williamson · Aditya Menon
[ Slides]  [ Oral] 
Oral
Thu Jun 13 10:05 AM -- 10:10 AM (PDT) @ Room 103
Learning What and Where to Transfer
In Supervised and Transfer Learning
Yunhun Jang · Hankook Lee · Sung Ju Hwang · Jinwoo Shin
[ Slides]  [ Oral] 
Oral
Thu Jun 13 10:05 AM -- 10:10 AM (PDT) @ Room 101
A Polynomial Time MCMC Method for Sampling from Continuous Determinantal Point Processes
In Monte Carlo Methods
Alireza Rezaei · Shayan Oveis Gharan
[ Slides]  [ Oral] 
Oral
Thu Jun 13 10:05 AM -- 10:10 AM (PDT) @ Room 102
Adversarial Online Learning with noise
In Online Learning
Alon Resler · Yishay Mansour
[ Slides]  [ Oral] 
Oral
Thu Jun 13 10:10 AM -- 10:15 AM (PDT) @ Hall B
Iterative Linearized Control: Stable Algorithms and Complexity Guarantees
In Reinforcement Learning
Vincent Roulet · Dmitriy Drusvyatskiy · Siddhartha Srinivasa · Zaid Harchaoui
[ Slides] 
Oral
Thu Jun 13 10:10 AM -- 10:15 AM (PDT) @ Room 104
$\texttt{DoubleSqueeze}$: Parallel Stochastic Gradient Descent with Double-pass Error-Compensated Compression
In Non-convex Optimization
Hanlin Tang · Chen Yu · Xiangru Lian · Tong Zhang · Ji Liu
[ Slides]  [ Oral] 
Oral
Thu Jun 13 10:10 AM -- 10:15 AM (PDT) @ Grand Ballroom
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation
In Interpretability
Sahil Singla · Eric Wallace · Shi Feng · Soheil Feizi
[ Slides] 
Oral
Thu Jun 13 10:10 AM -- 10:15 AM (PDT) @ Room 101
Adaptive Antithetic Sampling for Variance Reduction
In Monte Carlo Methods
Hongyu Ren · Shengjia Zhao · Stefano Ermon
[ Slides]  [ Oral] 
Oral
Thu Jun 13 10:10 AM -- 10:15 AM (PDT) @ Room 201
AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
In Applications
Kaizhi Qian · Yang Zhang · Shiyu Chang · Xuesong Yang · Mark Hasegawa-Johnson
[ Slides]  [ Oral] 
Oral
Thu Jun 13 10:10 AM -- 10:15 AM (PDT) @ Hall A
MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets
In Deep Generative Models
Pierre-Alexandre Mattei · Jes Frellsen
[ Slides] 
Oral
Thu Jun 13 10:10 AM -- 10:15 AM (PDT) @ Room 102
Online Variance Reduction with Mixtures
In Online Learning
Zalán Borsos · Sebastian Curi · Yehuda Levy · Andreas Krause
[ Slides]  [ Oral] 
Oral
Thu Jun 13 10:15 AM -- 10:20 AM (PDT) @ Room 101
Accelerated Flow for Probability Distributions
In Monte Carlo Methods
Amirhossein Taghvaei · Prashant Mehta
[ Slides]  [ Oral] 
Oral
Thu Jun 13 10:15 AM -- 10:20 AM (PDT) @ Hall B
Finding Options that Minimize Planning Time
In Reinforcement Learning
Yuu Jinnai · David Abel · David Hershkowitz · Michael L. Littman · George Konidaris
[ Slides] 
Oral
Thu Jun 13 10:15 AM -- 10:20 AM (PDT) @ Room 104
Model Function Based Conditional Gradient Method with Armijo-like Line Search
In Non-convex Optimization
Peter Ochs · Yura Malitsky
[ Slides]  [ Oral] 
Oral
Thu Jun 13 10:15 AM -- 10:20 AM (PDT) @ Room 102
Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case
In Online Learning
Alina Beygelzimer · David Pal · Balazs Szorenyi · Devanathan Thiruvenkatachari · Chen-Yu Wei · Chicheng Zhang
[ Slides]  [ Oral] 
Oral
Thu Jun 13 10:15 AM -- 10:20 AM (PDT) @ Hall A
On Scalable and Efficient Computation of Large Scale Optimal Transport
In Deep Generative Models
Yujia Xie · Minshuo Chen · Haoming Jiang · Tuo Zhao · Hongyuan Zha
[ Slides] 
Oral
Thu Jun 13 10:15 AM -- 10:20 AM (PDT) @ Grand Ballroom
On the Connection Between Adversarial Robustness and Saliency Map Interpretability
In Interpretability
Christian Etmann · Sebastian Lunz · Peter Maass · Carola-Bibiane Schönlieb
[ Slides] 
Oral
Thu Jun 13 10:15 AM -- 10:20 AM (PDT) @ Room 201
A fully differentiable beam search decoder
In Applications
Ronan Collobert · Awni Hannun · Gabriel Synnaeve
[ Slides]  [ Oral] 
Break
Thu Jun 13 10:30 AM -- 11:00 AM (PDT)
Coffee Break
Oral
Thu Jun 13 11:00 AM -- 11:20 AM (PDT) @ Room 103
DBSCAN++: Towards fast and scalable density clustering
In Unsupervised Learning
Jennifer Jang · Heinrich Jiang
[ Oral] 
Oral
Thu Jun 13 11:00 AM -- 11:20 AM (PDT) @ Grand Ballroom
Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR Problem
In Deep Learning Theory
Alon Brutzkus · Amir Globerson
[ Oral] 
Oral
Thu Jun 13 11:00 AM -- 11:20 AM (PDT) @ Room 201
Scaling Up Ordinal Embedding: A Landmark Approach
In Ranking and Preference Learning
Jesse Anderton · Javed Aslam
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:00 AM -- 11:20 AM (PDT) @ Room 104
Analogies Explained: Towards Understanding Word Embeddings
In Applications: Natural Language Processing
Carl Allen · Timothy Hospedales
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:00 AM -- 11:20 AM (PDT) @ Room 101
Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel $k$-means Clustering
In Optimization and Graphical Models
Taisuke Yasuda · David Woodruff · Manuel Fernandez
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:00 AM -- 11:20 AM (PDT) @ Seaside Ballroom
Proportionally Fair Clustering
In Fairness
Xingyu Chen · Brandon Fain · Liang Lyu · Kamesh Munagala
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:00 AM -- 11:20 AM (PDT) @ Room 102
Learning Linear-Quadratic Regulators Efficiently with only $\sqrt{T}$ Regret
In Online Learning
Alon Cohen · Tomer Koren · Yishay Mansour
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:00 AM -- 11:20 AM (PDT) @ Hall A
Understanding and correcting pathologies in the training of learned optimizers
In Deep Learning
Luke Metz · Niru Maheswaranathan · Jeremy Nixon · Daniel Freeman · Jascha Sohl-Dickstein
Oral
Thu Jun 13 11:00 AM -- 11:20 AM (PDT) @ Hall B
Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement
In Deep Sequence Models
Wouter Kool · Herke van Hoof · Max Welling
[ Slides] 
Oral
Thu Jun 13 11:20 AM -- 11:25 AM (PDT) @ Room 103
Concrete Autoencoders: Differentiable Feature Selection and Reconstruction
In Unsupervised Learning
Muhammed Fatih Balın · Abubakar Abid · James Zou
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:20 AM -- 11:25 AM (PDT) @ Room 102
Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems
In Online Learning
Timothy Mann · Sven Gowal · András György · Huiyi Hu · Ray Jiang · Balaji Lakshminarayanan · Prav Srinivasan
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:20 AM -- 11:25 AM (PDT) @ Room 101
Dimensionality Reduction for Tukey Regression
In Optimization and Graphical Models
Kenneth Clarkson · Ruosong Wang · David Woodruff
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:20 AM -- 11:25 AM (PDT) @ Hall B
Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs
In Deep Sequence Models
Lingbing Guo · Zequn Sun · Wei Hu
[ Slides] 
Oral
Thu Jun 13 11:20 AM -- 11:25 AM (PDT) @ Grand Ballroom
On the Spectral Bias of Neural Networks
In Deep Learning Theory
Nasim Rahaman · Aristide Baratin · Devansh Arpit · Felix Draxler · Min Lin · Fred Hamprecht · Yoshua Bengio · Aaron Courville
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:20 AM -- 11:25 AM (PDT) @ Seaside Ballroom
Stable and Fair Classification
In Fairness
Lingxiao Huang · Nisheeth Vishnoi
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:20 AM -- 11:25 AM (PDT) @ Room 104
Parameter-Efficient Transfer Learning for NLP
In Applications: Natural Language Processing
Neil Houlsby · Andrei Giurgiu · Stanislaw Jastrzebski · Bruna Morrone · Quentin de Laroussilhe · Andrea Gesmundo · Mona Attariyan · Sylvain Gelly
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:20 AM -- 11:25 AM (PDT) @ Hall A
Demystifying Dropout
In Deep Learning
Hongchang Gao · Jian Pei · Heng Huang
[ Slides] 
Oral
Thu Jun 13 11:20 AM -- 11:25 AM (PDT) @ Room 201
Learning to select for a predefined ranking
In Ranking and Preference Learning
Aleksei Ustimenko · Aleksandr Vorobev · Gleb Gusev · Pavel Serdyukov
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:25 AM -- 11:30 AM (PDT) @ Seaside Ballroom
Flexibly Fair Representation Learning by Disentanglement
In Fairness
Elliot Creager · David Madras · Joern-Henrik Jacobsen · Marissa Weis · Kevin Swersky · Toniann Pitassi · Richard Zemel
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:25 AM -- 11:30 AM (PDT) @ Room 201
Mallows ranking models: maximum likelihood estimate and regeneration
In Ranking and Preference Learning
Wenpin Tang
Oral
Thu Jun 13 11:25 AM -- 11:30 AM (PDT) @ Room 104
Efficient On-Device Models using Neural Projections
In Applications: Natural Language Processing
Sujith Ravi
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:25 AM -- 11:30 AM (PDT) @ Room 103
Gromov-Wasserstein Learning for Graph Matching and Node Embedding
In Unsupervised Learning
Hongteng Xu · Dixin Luo · Hongyuan Zha · Lawrence Carin
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:25 AM -- 11:30 AM (PDT) @ Room 101
Efficient Full-Matrix Adaptive Regularization
In Optimization and Graphical Models
Naman Agarwal · Brian Bullins · Xinyi Chen · Elad Hazan · Karan Singh · Cyril Zhang · Yi Zhang
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:25 AM -- 11:30 AM (PDT) @ Room 102
Adaptive Regret of Convex and Smooth Functions
In Online Learning
Lijun Zhang · Tie-Yan Liu · Zhi-Hua Zhou
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:25 AM -- 11:30 AM (PDT) @ Hall A
Ladder Capsule Network
In Deep Learning
Taewon Jeong · Youngmin Lee · Heeyoung Kim
[ Slides] 
Oral
Thu Jun 13 11:25 AM -- 11:30 AM (PDT) @ Grand Ballroom
Recursive Sketches for Modular Deep Learning
In Deep Learning Theory
Badih Ghazi · Rina Panigrahy · Joshua R. Wang
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:25 AM -- 11:30 AM (PDT) @ Hall B
Meta-Learning Neural Bloom Filters
In Deep Sequence Models
Jack Rae · Sergey Bartunov · Timothy Lillicrap
[ Slides] 
Oral
Thu Jun 13 11:30 AM -- 11:35 AM (PDT) @ Seaside Ballroom
Fair Regression: Quantitative Definitions and Reduction-Based Algorithms
In Fairness
Alekh Agarwal · Miroslav Dudik · Steven Wu
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:30 AM -- 11:35 AM (PDT) @ Room 104
Deep Residual Output Layers for Neural Language Generation
In Applications: Natural Language Processing
Nikolaos Pappas · James Henderson
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:30 AM -- 11:35 AM (PDT) @ Hall A
Unreproducible Research is Reproducible
In Deep Learning
Xavier Bouthillier · César Laurent · Pascal Vincent
[ Slides] 
Oral
Thu Jun 13 11:30 AM -- 11:35 AM (PDT) @ Room 101
Breaking the gridlock in Mixture-of-Experts: Consistent and Efficient Algorithms
In Optimization and Graphical Models
Ashok Vardhan Makkuva · Pramod Viswanath · Sreeram Kannan · Sewoong Oh
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:30 AM -- 11:35 AM (PDT) @ Room 201
Fast and Stable Maximum Likelihood Estimation for Incomplete Multinomial Models
In Ranking and Preference Learning
Chenyang ZHANG · Guosheng Yin
Oral
Thu Jun 13 11:30 AM -- 11:35 AM (PDT) @ Hall B
CoT: Cooperative Training for Generative Modeling of Discrete Data
In Deep Sequence Models
Sidi Lu · Lantao Yu · Siyuan Feng · Yaoming Zhu · Weinan Zhang
[ Slides] 
Oral
Thu Jun 13 11:30 AM -- 11:35 AM (PDT) @ Grand Ballroom
Zero-Shot Knowledge Distillation in Deep Networks
In Deep Learning Theory
Gaurav Kumar Nayak · Konda Reddy Mopuri · Vaisakh Shaj · Venkatesh Babu Radhakrishnan · Anirban Chakraborty
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:30 AM -- 11:35 AM (PDT) @ Room 102
Online Adaptive Principal Component Analysis and Its extensions
In Online Learning
Jianjun Yuan · Andrew Lamperski
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:30 AM -- 11:35 AM (PDT) @ Room 103
Spectral Clustering of Signed Graphs via Matrix Power Means
In Unsupervised Learning
Pedro Mercado · Francesco Tudisco · Matthias Hein
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:35 AM -- 11:40 AM (PDT) @ Hall A
Geometric Scattering for Graph Data Analysis
In Deep Learning
Feng Gao · Guy Wolf · Matthew Hirn
[ Slides] 
Oral
Thu Jun 13 11:35 AM -- 11:40 AM (PDT) @ Hall B
Non-Monotonic Sequential Text Generation
In Deep Sequence Models
Sean Welleck · Kiante Brantley · Hal Daumé III · Kyunghyun Cho
[ Slides] 
Oral
Thu Jun 13 11:35 AM -- 11:40 AM (PDT) @ Room 201
Fast Algorithm for Generalized Multinomial Models with Ranking Data
In Ranking and Preference Learning
Jiaqi Gu · Guosheng Yin
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:35 AM -- 11:40 AM (PDT) @ Grand Ballroom
A Convergence Theory for Deep Learning via Over-Parameterization
In Deep Learning Theory
Zeyuan Allen-Zhu · Yuanzhi Li · Zhao Song
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:35 AM -- 11:40 AM (PDT) @ Seaside Ballroom
Fairness without Harm: Decoupled Classifiers with Preference Guarantees
In Fairness
Berk Ustun · Yang Liu · David Parkes
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:35 AM -- 11:40 AM (PDT) @ Room 101
Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations
In Optimization and Graphical Models
Quanming Yao · James Kwok · Bo Han
[ Slides]  [ Video] 
Oral
Thu Jun 13 11:35 AM -- 11:40 AM (PDT) @ Room 103
Coresets for Ordered Weighted Clustering
In Unsupervised Learning
Vladimir Braverman · Shaofeng Jiang · Robert Krauthgamer · Xuan Wu
[ Slides] 
Oral
Thu Jun 13 11:35 AM -- 11:40 AM (PDT) @ Room 102
POLITEX: Regret Bounds for Policy Iteration using Expert Prediction
In Online Learning
Yasin Abbasi-Yadkori · Peter Bartlett · Kush Bhatia · Nevena Lazic · Csaba Szepesvari · Gellért Weisz
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:35 AM -- 11:40 AM (PDT) @ Room 104
Improving Neural Language Modeling via Adversarial Training
In Applications: Natural Language Processing
Dilin Wang · Chengyue Gong · Qiang Liu
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:40 AM -- 12:00 PM (PDT) @ Room 101
Robust Estimation of Tree Structured Gaussian Graphical Models
In Optimization and Graphical Models
Ashish Katiyar · Jessica Hoffmann · Constantine Caramanis
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:40 AM -- 12:00 PM (PDT) @ Room 201
Graph Resistance and Learning from Pairwise Comparisons
In Ranking and Preference Learning
Julien Hendrickx · Alex Olshevsky · Venkatesh Saligrama
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:40 AM -- 12:00 PM (PDT) @ Room 103
Fair k-Center Clustering for Data Summarization
In Unsupervised Learning
Matthäus Kleindessner · Pranjal Awasthi · Jamie Morgenstern
[ Oral] 
Oral
Thu Jun 13 11:40 AM -- 12:00 PM (PDT) @ Hall A
Robust Inference via Generative Classifiers for Handling Noisy Labels
In Deep Learning
Kimin Lee · Sukmin Yun · Kibok Lee · Honglak Lee · Bo Li · Jinwoo Shin
[ Slides] 
Oral
Thu Jun 13 11:40 AM -- 12:00 PM (PDT) @ Grand Ballroom
A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks
In Deep Learning Theory
Umut Simsekli · Levent Sagun · Mert Gurbuzbalaban
[ Oral] 
Oral
Thu Jun 13 11:40 AM -- 12:00 PM (PDT) @ Room 102
Anytime Online-to-Batch, Optimism and Acceleration
In Online Learning
Ashok Cutkosky
[ Slides]  [ Oral] 
Oral
Thu Jun 13 11:40 AM -- 12:00 PM (PDT) @ Hall B
Insertion Transformer: Flexible Sequence Generation via Insertion Operations
In Deep Sequence Models
Mitchell Stern · William Chan · Jamie Kiros · Jakob Uszkoreit
Oral
Thu Jun 13 11:40 AM -- 12:00 PM (PDT) @ Seaside Ballroom
Differentially Private Fair Learning
In Fairness
Matthew Jagielski · Michael Kearns · Jieming Mao · Alina Oprea · Aaron Roth · Saeed Sharifi-Malvajerdi · Jonathan Ullman
[ Oral] 
Oral
Thu Jun 13 11:40 AM -- 12:00 PM (PDT) @ Room 104
Mixture Models for Diverse Machine Translation: Tricks of the Trade
In Applications: Natural Language Processing
Tianxiao Shen · Myle Ott · Michael Auli · Marc'Aurelio Ranzato
[ Oral] 
Oral
Thu Jun 13 12:00 PM -- 12:05 PM (PDT) @ Room 103
A Better k-means++ Algorithm via Local Search
In Unsupervised Learning
Silvio Lattanzi · Christian Sohler
[ Slides]  [ Oral] 
Oral
Thu Jun 13 12:00 PM -- 12:05 PM (PDT) @ Seaside Ballroom
Obtaining Fairness using Optimal Transport Theory
In Fairness
Paula Gordaliza · Eustasio del Barrio · Gamboa Fabrice · Loubes Jean-Michel
[ Slides]  [ Oral] 
Oral
Thu Jun 13 12:00 PM -- 12:05 PM (PDT) @ Room 104
MASS: Masked Sequence to Sequence Pre-training for Language Generation
In Applications: Natural Language Processing
Kaitao Song · Xu Tan · Tao Qin · Jianfeng Lu · Tie-Yan Liu
[ Slides]  [ Oral] 
Oral
Thu Jun 13 12:00 PM -- 12:05 PM (PDT) @ Hall A
LIT: Learned Intermediate Representation Training for Model Compression
In Deep Learning
Animesh Koratana · Daniel Kang · Peter Bailis · Matei Zaharia
[ Slides] 
Oral
Thu Jun 13 12:00 PM -- 12:05 PM (PDT) @ Hall B
Empirical Analysis of Beam Search Performance Degradation in Neural Sequence Models
In Deep Sequence Models
Eldan Cohen · Christopher Beck
[ Slides] 
Oral
Thu Jun 13 12:00 PM -- 12:05 PM (PDT) @ Grand Ballroom
Approximation and non-parametric estimation of ResNet-type convolutional neural networks
In Deep Learning Theory
Kenta Oono · Taiji Suzuki
Oral
Thu Jun 13 12:00 PM -- 12:05 PM (PDT) @ Room 201
Learning Context-dependent Label Permutations for Multi-label Classification
In Ranking and Preference Learning
Jinseok Nam · Young-Bum Kim · Eneldo Loza Mencia · Sunghyun Park · Ruhi Sarikaya · Johannes Fürnkranz
[ Slides]  [ Oral] 
Oral
Thu Jun 13 12:00 PM -- 12:05 PM (PDT) @ Room 101
Spectral Approximate Inference
In Optimization and Graphical Models
Sejun Park · Eunho Yang · Se-Young Yun · Jinwoo Shin
[ Slides]  [ Oral] 
Oral
Thu Jun 13 12:00 PM -- 12:05 PM (PDT) @ Room 102
Cautious Regret Minimization: Online Optimization with Long-Term Budget Constraints
In Online Learning
Nikolaos Liakopoulos · Apostolos Destounis · Georgios Paschos · Thrasyvoulos Spyropoulos · Panayotis Mertikopoulos
[ Slides]  [ Oral] 
Oral
Thu Jun 13 12:05 PM -- 12:10 PM (PDT) @ Room 201
Discovering Context Effects from Raw Choice Data
In Ranking and Preference Learning
Arjun Seshadri · Alexander Peysakhovich · Johan Ugander
[ Slides]  [ Oral] 
Oral
Thu Jun 13 12:05 PM -- 12:10 PM (PDT) @ Room 104
Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops
In Applications: Natural Language Processing
Limor Gultchin · Genevieve Patterson · Nancy Baym · Nathaniel Swinger · Adam Kalai
[ Slides]  [ Oral] 
Oral
Thu Jun 13 12:05 PM -- 12:10 PM (PDT) @ Room 102
Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning
In Online Learning
Frederik Benzing · Marcelo Matheus Gauy · Asier Mujika · Anders Martinsson · Angelika Steger
[ Slides]  [ Oral] 
Oral
Thu Jun 13 12:05 PM -- 12:10 PM (PDT) @ Grand Ballroom
Global Convergence of Block Coordinate Descent in Deep Learning
In Deep Learning Theory
Jinshan ZENG · Tim Tsz-Kit Lau · Shaobo Lin · Yuan Yao
[ Slides]  [ Oral] 
Oral
Thu Jun 13 12:05 PM -- 12:10 PM (PDT) @ Room 101
Partially Linear Additive Gaussian Graphical Models
In Optimization and Graphical Models
Sinong Geng · Minhao Yan · Mladen Kolar · Sanmi Koyejo
[ Slides] 
Oral
Thu Jun 13 12:05 PM -- 12:10 PM (PDT) @ Hall B
Trainable Decoding of Sets of Sequences for Neural Sequence Models
In Deep Sequence Models
Ashwin Kalyan · Peter Anderson · Stefan Lee · Dhruv Batra
[ Slides] 
Oral
Thu Jun 13 12:05 PM -- 12:10 PM (PDT) @ Room 103
Kernel Normalized Cut: a Theoretical Revisit
In Unsupervised Learning
Yoshikazu Terada · Michio Yamamoto
[ Slides]  [ Oral] 
Oral
Thu Jun 13 12:05 PM -- 12:10 PM (PDT) @ Seaside Ballroom
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions
In Fairness
Hao Wang · Berk Ustun · Flavio Calmon
[ Slides]  [ Oral] 
Oral
Thu Jun 13 12:05 PM -- 12:10 PM (PDT) @ Hall A
Analyzing and Improving Representations with the Soft Nearest Neighbor Loss
In Deep Learning
Nicholas Frosst · Nicolas Papernot · Geoffrey Hinton
[ Slides] 
Oral
Thu Jun 13 12:10 PM -- 12:15 PM (PDT) @ Room 103
Guarantees for Spectral Clustering with Fairness Constraints
In Unsupervised Learning
Matthäus Kleindessner · Samira Samadi · Pranjal Awasthi · Jamie Morgenstern
[ Slides]  [ Oral] 
Oral
Thu Jun 13 12:10 PM -- 12:15 PM (PDT) @ Grand Ballroom
Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians
In Deep Learning Theory
Vardan Papyan
[ Slides]  [ Oral] 
Oral
Thu Jun 13 12:10 PM -- 12:15 PM (PDT) @ Seaside Ballroom
On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning
In Fairness
Hoda Heidari · Vedant Nanda · Krishna Gummadi
[ Slides]  [ Oral] 
Oral
Thu Jun 13 12:10 PM -- 12:15 PM (PDT) @ Hall B
Learning to Generalize from Sparse and Underspecified Rewards
In Deep Sequence Models
Rishabh Agarwal · Chen Liang · Dale Schuurmans · Mohammad Norouzi
[ Slides] 
Oral
Thu Jun 13 12:10 PM -- 12:15 PM (PDT) @ Room 104
MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization
In Applications: Natural Language Processing
Eric Chu · Peter Liu
[ Slides]  [ Oral] 
Oral
Thu Jun 13 12:10 PM -- 12:15 PM (PDT) @ Hall A
What is the Effect of Importance Weighting in Deep Learning?
In Deep Learning
Jonathon Byrd · Zachary Lipton
[ Slides] 
Oral
Thu Jun 13 12:10 PM -- 12:15 PM (PDT) @ Room 201
On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference
In Ranking and Preference Learning
Rohin Shah · Noah Gundotra · Pieter Abbeel · Anca Dragan
[ Slides]  [ Oral] 
Oral
Thu Jun 13 12:10 PM -- 12:15 PM (PDT) @ Room 101
DAG-GNN: DAG Structure Learning with Graph Neural Networks
In Optimization and Graphical Models
Yue Yu · Jie Chen · Tian Gao · Mo Yu
[ Slides]  [ Oral] 
Oral
Thu Jun 13 12:10 PM -- 12:15 PM (PDT) @ Room 102
Adaptive Sensor Placement for Continuous Spaces
In Online Learning
James A. Grant · Alexis Boukouvalas · Ryan-Rhys Griffiths · David Leslie · Sattar Vakili · Enrique Munoz De Cote
[ Slides]  [ Oral] 
Oral
Thu Jun 13 12:15 PM -- 12:20 PM (PDT) @ Room 101
Random Walks on Hypergraphs with Edge-Dependent Vertex Weights
In Optimization and Graphical Models
Uthsav Chitra · Benjamin Raphael
[ Slides]  [ Oral] 
Oral
Thu Jun 13 12:15 PM -- 12:20 PM (PDT) @ Room 102
Scale-free adaptive planning for deterministic dynamics & discounted rewards
In Online Learning
Peter Bartlett · Victor Gabillon · Jennifer Healey · Michal Valko
[ Slides]  [ Oral] 
Oral
Thu Jun 13 12:15 PM -- 12:20 PM (PDT) @ Room 103
Supervised Hierarchical Clustering with Exponential Linkage
In Unsupervised Learning
Nishant Yadav · Ari Kobren · Nicholas Monath · Andrew McCallum
[ Slides]  [ Oral] 
Oral
Thu Jun 13 12:15 PM -- 12:20 PM (PDT) @ Room 104
CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network
In Applications: Natural Language Processing
Tom Kenter · Vincent Wan · Chun-an Chan · Robert Clark · Jakub Vit
[ Slides]  [ Oral] 
Oral
Thu Jun 13 12:15 PM -- 12:20 PM (PDT) @ Seaside Ballroom
Making Decisions that Reduce Discriminatory Impacts
In Fairness
Matt J. Kusner · Chris Russell · Joshua Loftus · Ricardo Silva
[ Slides] 
Oral
Thu Jun 13 12:15 PM -- 12:20 PM (PDT) @ Hall A
Similarity of Neural Network Representations Revisited
In Deep Learning
Simon Kornblith · Mohammad Norouzi · Honglak Lee · Geoffrey Hinton
[ Slides] 
Oral
Thu Jun 13 12:15 PM -- 12:20 PM (PDT) @ Grand Ballroom
On the Limitations of Representing Functions on Sets
In Deep Learning Theory
Edward Wagstaff · Fabian Fuchs · Martin Engelcke · Ingmar Posner · Michael A Osborne
[ Slides]  [ Oral] 
Oral
Thu Jun 13 12:15 PM -- 12:20 PM (PDT) @ Hall B
Efficient Training of BERT by Progressively Stacking
In Deep Sequence Models
Linyuan Gong · Di He · Zhuohan Li · Tao Qin · Liwei Wang · Tie-Yan Liu
[ Slides] 
Oral
Thu Jun 13 12:15 PM -- 12:20 PM (PDT) @ Room 201
Learning Distance for Sequences by Learning a Ground Metric
In Ranking and Preference Learning
Bing Su · Ying Wu
[ Slides]  [ Oral] 
Break
Thu Jun 13 12:30 PM -- 02:00 PM (PDT)
Lunch - on your own
Invited Talk
Thu Jun 13 02:00 PM -- 03:00 PM (PDT) @ Hall A
What 4 year olds can do and AI can’t (yet)
Alison Gopnik
[ Video] 
Invited Talk
Thu Jun 13 03:00 PM -- 03:30 PM (PDT) @ Hall A
Best Paper
[ Video] 
Oral
Thu Jun 13 03:00 PM -- 03:20 PM (PDT) @ Hall A #0
Rates of Convergence for Sparse Variational Gaussian Process Regression
David Burt · Carl E Rasmussen · Mark van der Wilk
Break
Thu Jun 13 03:30 PM (PDT)
Coffee Break
Oral
Thu Jun 13 04:00 PM -- 04:20 PM (PDT) @ Hall A
Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations
In Representation Learning
Tri Dao · Albert Gu · Matthew Eichhorn · Atri Rudra · Christopher Re
Oral
Thu Jun 13 04:00 PM -- 04:20 PM (PDT) @ Room 104
COMIC: Multi-view Clustering Without Parameter Selection
In Unsupervised Learning
Xi Peng · Zhenyu Huang · Jiancheng Lv · Hongyuan Zhu · Joey Tianyi Zhou
[ Oral] 
Oral
Thu Jun 13 04:00 PM -- 04:20 PM (PDT) @ Hall B
Decentralized Exploration in Multi-Armed Bandits
In Bandits and Multiagent Learning
Raphaël Féraud · REDA ALAMI · Romain Laroche
[ Slides] 
Oral
Thu Jun 13 04:00 PM -- 04:20 PM (PDT) @ Seaside Ballroom
Submodular Maximization beyond Non-negativity: Guarantees, Fast Algorithms, and Applications
In Optimization
Christopher Harshaw · Moran Feldman · Justin Ward · Amin Karbasi
[ Oral] 
Oral
Thu Jun 13 04:00 PM -- 04:20 PM (PDT) @ Room 102
Communication-Constrained Inference and the Role of Shared Randomness
In Learning Theory
Jayadev Acharya · Clément Canonne · Himanshu Tyagi
[ Slides] 
Oral
Thu Jun 13 04:00 PM -- 04:20 PM (PDT) @ Grand Ballroom
Probabilistic Neural Symbolic Models for Interpretable Visual Question Answering
In Bayesian Deep Learning
Shanmukha Ramakrishna Vedantam · Karan Desai · Stefan Lee · Marcus Rohrbach · Dhruv Batra · Devi Parikh
Oral
Thu Jun 13 04:20 PM -- 04:25 PM (PDT) @ Room 104
The Wasserstein Transform
In Unsupervised Learning
Facundo Memoli · Zane Smith · Zhengchao Wan
[ Slides]  [ Oral] 
Oral
Thu Jun 13 04:20 PM -- 04:25 PM (PDT) @ Hall A
Distributed, Egocentric Representations of Graphs for Detecting Critical Structures
In Representation Learning
Ruo-Chun Tzeng · Shan-Hung (Brandon) Wu
[ Slides] 
Oral
Thu Jun 13 04:20 PM -- 04:25 PM (PDT) @ Grand Ballroom
Nonparametric Bayesian Deep Networks with Local Competition
In Bayesian Deep Learning
Konstantinos Panousis · Sotirios Chatzis · Sergios Theodoridis
Oral
Thu Jun 13 04:20 PM -- 04:25 PM (PDT) @ Seaside Ballroom
Online Algorithms for Rent-Or-Buy with Expert Advice
In Optimization
Sreenivas Gollapudi · Debmalya Panigrahi
[ Slides]  [ Oral] 
Oral
Thu Jun 13 04:20 PM -- 04:25 PM (PDT) @ Hall B
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback
In Bandits and Multiagent Learning
Chicheng Zhang · Alekh Agarwal · Hal Daumé III · John Langford · Sahand Negahban
[ Slides] 
Oral
Thu Jun 13 04:20 PM -- 04:25 PM (PDT) @ Room 102
Learning and Data Selection in Big Datasets
In Learning Theory
Hossein Shokri Ghadikolaei · Hadi Ghauch · Inst. of Technology Carlo Fischione · Mikael Skoglund
[ Slides] 
Oral
Thu Jun 13 04:25 PM -- 04:30 PM (PDT) @ Hall B
Exploiting structure of uncertainty for efficient matroid semi-bandits
In Bandits and Multiagent Learning
Pierre Perrault · Vianney Perchet · Michal Valko
[ Slides] 
Oral
Thu Jun 13 04:25 PM -- 04:30 PM (PDT) @ Room 104
Sequential Facility Location: Approximate Submodularity and Greedy Algorithm
In Unsupervised Learning
Ehsan Elhamifar
[ Slides]  [ Oral] 
Oral
Thu Jun 13 04:25 PM -- 04:30 PM (PDT) @ Grand Ballroom
Good Initializations of Variational Bayes for Deep Models
In Bayesian Deep Learning
Simone Rossi · Pietro Michiardi · Maurizio Filippone
[ Slides] 
Oral
Thu Jun 13 04:25 PM -- 04:30 PM (PDT) @ Hall A
Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities
In Representation Learning
Octavian-Eugen Ganea · Sylvain Gelly · Gary Becigneul · Aliaksei Severyn
[ Slides] 
Oral
Thu Jun 13 04:25 PM -- 04:30 PM (PDT) @ Room 102
Sublinear quantum algorithms for training linear and kernel-based classifiers
In Learning Theory
Tongyang Li · Shouvanik Chakrabarti · Xiaodi Wu
[ Slides] 
Oral
Thu Jun 13 04:25 PM -- 04:30 PM (PDT) @ Seaside Ballroom
Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity
In Optimization
Matthew Fahrbach · Vahab Mirrokni · Morteza Zadimoghaddam
[ Slides]  [ Oral] 
Oral
Thu Jun 13 04:30 PM -- 04:35 PM (PDT) @ Grand Ballroom
Dropout as a Structured Shrinkage Prior
In Bayesian Deep Learning
Eric Nalisnick · Jose Miguel Hernandez-Lobato · Padhraic Smyth
[ Slides] 
Oral
Thu Jun 13 04:30 PM -- 04:35 PM (PDT) @ Room 102
Agnostic Federated Learning
In Learning Theory
Mehryar Mohri · Gary Sivek · Ananda Suresh
[ Slides] 
Oral
Thu Jun 13 04:30 PM -- 04:35 PM (PDT) @ Hall A
Multi-Object Representation Learning with Iterative Variational Inference
In Representation Learning
Klaus Greff · Raphael Lopez Kaufman · Rishabh Kabra · Nicholas Watters · Christopher Burgess · Daniel Zoran · Loic Matthey · Matthew Botvinick · Alexander Lerchner
[ Slides] 
Oral
Thu Jun 13 04:30 PM -- 04:35 PM (PDT) @ Seaside Ballroom
Categorical Feature Compression via Submodular Optimization
In Optimization
Mohammad Hossein Bateni · Lin Chen · Hossein Esfandiari · Thomas Fu · Vahab Mirrokni · Afshin Rostamizadeh
[ Slides]  [ Oral] 
Oral
Thu Jun 13 04:30 PM -- 04:35 PM (PDT) @ Hall B
PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits
In Bandits and Multiagent Learning
Arghya Roy Chaudhuri · Shivaram Kalyanakrishnan
[ Slides] 
Oral
Thu Jun 13 04:30 PM -- 04:35 PM (PDT) @ Room 104
Neural Collaborative Subspace Clustering
In Unsupervised Learning
Tong Zhang · Pan Ji · Mehrtash Harandi · Wenbing Huang · HONGDONG LI
[ Slides]  [ Oral] 
Oral
Thu Jun 13 04:35 PM -- 04:40 PM (PDT) @ Hall B
Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model
In Bandits and Multiagent Learning
Gi-Soo Kim · Myunghee Cho Paik
[ Slides] 
Oral
Thu Jun 13 04:35 PM -- 04:40 PM (PDT) @ Seaside Ballroom
Multi-Frequency Phase Synchronization
In Optimization
Tingran Gao · Zhizhen Zhao
[ Slides]  [ Oral] 
Oral
Thu Jun 13 04:35 PM -- 04:40 PM (PDT) @ Room 102
Discovering Conditionally Salient Features with Statistical Guarantees
In Learning Theory
Jaime Roquero Gimenez · James Zou
[ Slides] 
Oral
Thu Jun 13 04:35 PM -- 04:40 PM (PDT) @ Room 104
Unsupervised Deep Learning by Neighbourhood Discovery
In Unsupervised Learning
Jiabo Huang · Qi Dong · Shaogang Gong · Xiatian Zhu
[ Slides]  [ Oral] 
Oral
Thu Jun 13 04:35 PM -- 04:40 PM (PDT) @ Grand Ballroom
ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables
In Bayesian Deep Learning
Mingzhang Yin · Yuguang Yue · Mingyuan Zhou
[ Slides] 
Oral
Thu Jun 13 04:35 PM -- 04:40 PM (PDT) @ Hall A
Cross-Domain 3D Equivariant Image Embeddings
In Representation Learning
Carlos Esteves · Avneesh Sud · Zhengyi Luo · Kostas Daniilidis · Ameesh Makadia
[ Slides] 
Oral
Thu Jun 13 04:40 PM -- 05:00 PM (PDT) @ Room 102
A Theoretical Analysis of Contrastive Unsupervised Representation Learning
In Learning Theory
Nikunj Umesh Saunshi · Orestis Plevrakis · Sanjeev Arora · Mikhail Khodak · Hrishikesh Khandeparkar
Oral
Thu Jun 13 04:40 PM -- 05:00 PM (PDT) @ Grand Ballroom
On Variational Bounds of Mutual Information
In Bayesian Deep Learning
Ben Poole · Sherjil Ozair · Aäron van den Oord · Alexander Alemi · George Tucker
Oral
Thu Jun 13 04:40 PM -- 05:00 PM (PDT) @ Hall A
Loss Landscapes of Regularized Linear Autoencoders
In Representation Learning
Daniel Kunin · Jonathan Bloom · Aleksandrina Goeva · Cotton Seed
[ Slides] 
Oral
Thu Jun 13 04:40 PM -- 05:00 PM (PDT) @ Room 104
Autoregressive Energy Machines
In Unsupervised Learning
Conor Durkan · Charlie Nash
[ Oral] 
Oral
Thu Jun 13 04:40 PM -- 05:00 PM (PDT) @ Hall B
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning
In Bandits and Multiagent Learning
Jakob Foerster · Francis Song · Edward Hughes · Neil Burch · Iain Dunning · Shimon Whiteson · Matthew Botvinick · Michael Bowling
Oral
Thu Jun 13 04:40 PM -- 05:00 PM (PDT) @ Seaside Ballroom
Faster Algorithms for Binary Matrix Factorization
In Optimization
Ravi Kumar · Rina Panigrahy · Ali Rahimi · David Woodruff
[ Oral] 
Oral
Thu Jun 13 05:00 PM -- 05:05 PM (PDT) @ Grand Ballroom
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation
In Bayesian Deep Learning
Samuel Wiqvist · Pierre-Alexandre Mattei · Umberto Picchini · Jes Frellsen
[ Slides] 
Oral
Thu Jun 13 05:00 PM -- 05:05 PM (PDT) @ Room 104
Greedy Orthogonal Pivoting Algorithm for Non-Negative Matrix Factorization
In Unsupervised Learning
Kai Zhang · Sheng Zhang · Jun Liu · Jun Wang · Jie Zhang
[ Slides]  [ Oral] 
Oral
Thu Jun 13 05:00 PM -- 05:05 PM (PDT) @ Hall A
Hyperbolic Disk Embeddings for Directed Acyclic Graphs
In Representation Learning
Ryota Suzuki · Ryusuke Takahama · Shun Onoda
[ Slides] 
Oral
Thu Jun 13 05:00 PM -- 05:05 PM (PDT) @ Hall B
TarMAC: Targeted Multi-Agent Communication
In Bandits and Multiagent Learning
Abhishek Das · Theophile Gervet · Joshua Romoff · Dhruv Batra · Devi Parikh · Michael Rabbat · Joelle Pineau
[ Slides] 
Oral
Thu Jun 13 05:00 PM -- 05:05 PM (PDT) @ Room 102
The information-theoretic value of unlabeled data in semi-supervised learning
In Learning Theory
Alexander Golovnev · David Pal · Balazs Szorenyi
[ Slides] 
Oral
Thu Jun 13 05:05 PM -- 05:10 PM (PDT) @ Seaside Ballroom
Guided evolutionary strategies: augmenting random search with surrogate gradients
In Optimization
Niru Maheswaranathan · Luke Metz · George Tucker · Dami Choi · Jascha Sohl-Dickstein
[ Slides]  [ Oral] 
Oral
Thu Jun 13 05:05 PM -- 05:10 PM (PDT) @ Room 102
Unsupervised Label Noise Modeling and Loss Correction
In Learning Theory
Eric Arazo · Diego Ortego · Paul Albert · Noel O'Connor · Kevin McGuinness
[ Slides] 
Oral
Thu Jun 13 05:05 PM -- 05:10 PM (PDT) @ Room 104
Noise2Self: Blind Denoising by Self-Supervision
In Unsupervised Learning
Joshua Batson · Loic Royer
[ Slides]  [ Oral] 
Oral
Thu Jun 13 05:05 PM -- 05:10 PM (PDT) @ Hall A
LatentGNN: Learning Efficient Non-local Relations for Visual Recognition
In Representation Learning
Songyang Zhang · Xuming He · Shipeng Yan
[ Slides] 
Oral
Thu Jun 13 05:05 PM -- 05:10 PM (PDT) @ Grand Ballroom
Hierarchical Importance Weighted Autoencoders
In Bayesian Deep Learning
Chin-Wei Huang · Kris Sankaran · Eeshan Dhekane · Alexandre Lacoste · Aaron Courville
[ Slides] 
Oral
Thu Jun 13 05:05 PM -- 05:10 PM (PDT) @ Hall B
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
In Bandits and Multiagent Learning
Kyunghwan Son · Daewoo Kim · Wan Ju Kang · David Earl Hostallero · Yung Yi
[ Slides] 
Oral
Thu Jun 13 05:10 PM -- 05:15 PM (PDT) @ Grand Ballroom
Faster Attend-Infer-Repeat with Tractable Probabilistic Models
In Bayesian Deep Learning
Karl Stelzner · Robert Peharz · Kristian Kersting
[ Slides] 
Oral
Thu Jun 13 05:10 PM -- 05:15 PM (PDT) @ Hall A
Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness
In Representation Learning
Raphael Suter · Djordje Miladinovic · Bernhard Schölkopf · Stefan Bauer
[ Slides] 
Oral
Thu Jun 13 05:10 PM -- 05:15 PM (PDT) @ Room 102
Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment
In Learning Theory
Yifan Wu · Ezra Winston · Divyansh Kaushik · Zachary Lipton
[ Slides] 
Oral
Thu Jun 13 05:10 PM -- 05:15 PM (PDT) @ Seaside Ballroom
Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces
In Optimization
Johannes Kirschner · Mojmir Mutny · Nicole Hiller · Rasmus Ischebeck · Andreas Krause
[ Slides]  [ Oral] 
Oral
Thu Jun 13 05:10 PM -- 05:15 PM (PDT) @ Hall B
Actor-Attention-Critic for Multi-Agent Reinforcement Learning
In Bandits and Multiagent Learning
Shariq Iqbal · Fei Sha
[ Slides] 
Oral
Thu Jun 13 05:10 PM -- 05:15 PM (PDT) @ Room 104
Learning Dependency Structures for Weak Supervision Models
In Unsupervised Learning
Paroma Varma · Frederic Sala · Ann He · Alexander J Ratner · Christopher Re
[ Slides]  [ Oral] 
Oral
Thu Jun 13 05:15 PM -- 05:20 PM (PDT) @ Grand Ballroom
Understanding Priors in Bayesian Neural Networks at the Unit Level
In Bayesian Deep Learning
Mariia Vladimirova · Jakob Verbeek · Pablo Mesejo · Julyan Arbel
[ Slides] 
Oral
Thu Jun 13 05:15 PM -- 05:20 PM (PDT) @ Seaside Ballroom
Semi-Cyclic Stochastic Gradient Descent
In Optimization
Hubert Eichner · Tomer Koren · Brendan McMahan · Nati Srebro · Kunal Talwar
[ Slides]  [ Oral] 
Oral
Thu Jun 13 05:15 PM -- 05:20 PM (PDT) @ Hall B
Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation on Multi-Agent Reinforcement Learning
In Bandits and Multiagent Learning
Thinh Doan · Siva Maguluri · Justin Romberg
[ Slides] 
Oral
Thu Jun 13 05:15 PM -- 05:20 PM (PDT) @ Hall A
Lorentzian Distance Learning for Hyperbolic Representations
In Representation Learning
Marc Law · Renjie Liao · Jake Snell · Richard Zemel
[ Slides] 
Oral
Thu Jun 13 05:15 PM -- 05:20 PM (PDT) @ Room 104
Geometry and Symmetry in Short-and-Sparse Deconvolution
In Unsupervised Learning
Han-Wen Kuo · Yenson Lau · Yuqian Zhang · John Wright
[ Slides]  [ Oral] 
Oral
Thu Jun 13 05:15 PM -- 05:20 PM (PDT) @ Room 102
Pareto Optimal Streaming Unsupervised Classification
In Learning Theory
Soumya Basu · Steven Gutstein · Brent Lance · Sanjay Shakkottai
[ Slides] 
Break
Thu Jun 13 05:30 PM -- 06:00 PM (PDT)
Light Evening Snack
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #1
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations
In Posters Thu
Alex Lamb · Jonathan Binas · Anirudh Goyal · Sandeep Subramanian · Ioannis Mitliagkas · Yoshua Bengio · Michael Mozer
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #2
Variational Laplace Autoencoders
In Posters Thu
Yookoon Park · Chris Kim · Gunhee Kim
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #3
Latent Normalizing Flows for Discrete Sequences
In Posters Thu
Zachary Ziegler · Alexander Rush
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #4
Multi-objective training of Generative Adversarial Networks with multiple discriminators
In Posters Thu
Isabela Albuquerque · Joao Monteiro · Thang Doan · Breandan Considine · Tiago Falk · Ioannis Mitliagkas
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #5
Learning Discrete and Continuous Factors of Data via Alternating Disentanglement
In Posters Thu
Yeonwoo Jeong · Hyun Oh Song
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #6
Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables
In Posters Thu
Friso Kingma · Pieter Abbeel · Jonathan Ho
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #7
Graphite: Iterative Generative Modeling of Graphs
In Posters Thu
Aditya Grover · Aaron Zweig · Stefano Ermon
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #9
MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets
In Posters Thu
Pierre-Alexandre Mattei · Jes Frellsen
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #10
On Scalable and Efficient Computation of Large Scale Optimal Transport
In Posters Thu
Yujia Xie · Minshuo Chen · Haoming Jiang · Tuo Zhao · Hongyuan Zha
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #11
Understanding and correcting pathologies in the training of learned optimizers
In Posters Thu
Luke Metz · Niru Maheswaranathan · Jeremy Nixon · Daniel Freeman · Jascha Sohl-Dickstein
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #12
Demystifying Dropout
In Posters Thu
Hongchang Gao · Jian Pei · Heng Huang
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #13
Ladder Capsule Network
In Posters Thu
Taewon Jeong · Youngmin Lee · Heeyoung Kim
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #14
Unreproducible Research is Reproducible
In Posters Thu
Xavier Bouthillier · César Laurent · Pascal Vincent
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #15
Geometric Scattering for Graph Data Analysis
In Posters Thu
Feng Gao · Guy Wolf · Matthew Hirn
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #16
Robust Inference via Generative Classifiers for Handling Noisy Labels
In Posters Thu
Kimin Lee · Sukmin Yun · Kibok Lee · Honglak Lee · Bo Li · Jinwoo Shin
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #17
LIT: Learned Intermediate Representation Training for Model Compression
In Posters Thu
Animesh Koratana · Daniel Kang · Peter Bailis · Matei Zaharia
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #18
Analyzing and Improving Representations with the Soft Nearest Neighbor Loss
In Posters Thu
Nicholas Frosst · Nicolas Papernot · Geoffrey Hinton
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #19
What is the Effect of Importance Weighting in Deep Learning?
In Posters Thu
Jonathon Byrd · Zachary Lipton
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #20
Similarity of Neural Network Representations Revisited
In Posters Thu
Simon Kornblith · Mohammad Norouzi · Honglak Lee · Geoffrey Hinton
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #21
Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations
In Posters Thu
Tri Dao · Albert Gu · Matthew Eichhorn · Atri Rudra · Christopher Re
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #22
Distributed, Egocentric Representations of Graphs for Detecting Critical Structures
In Posters Thu
Ruo-Chun Tzeng · Shan-Hung (Brandon) Wu
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #23
Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities
In Posters Thu
Octavian-Eugen Ganea · Sylvain Gelly · Gary Becigneul · Aliaksei Severyn
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #24
Multi-Object Representation Learning with Iterative Variational Inference
In Posters Thu
Klaus Greff · Raphael Lopez Kaufman · Rishabh Kabra · Nicholas Watters · Christopher Burgess · Daniel Zoran · Loic Matthey · Matthew Botvinick · Alexander Lerchner
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #25
Cross-Domain 3D Equivariant Image Embeddings
In Posters Thu
Carlos Esteves · Avneesh Sud · Zhengyi Luo · Kostas Daniilidis · Ameesh Makadia
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #26
Loss Landscapes of Regularized Linear Autoencoders
In Posters Thu
Daniel Kunin · Jonathan Bloom · Aleksandrina Goeva · Cotton Seed
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #27
Hyperbolic Disk Embeddings for Directed Acyclic Graphs
In Posters Thu
Ryota Suzuki · Ryusuke Takahama · Shun Onoda
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #28
LatentGNN: Learning Efficient Non-local Relations for Visual Recognition
In Posters Thu
Songyang Zhang · Xuming He · Shipeng Yan
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #29
Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness
In Posters Thu
Raphael Suter · Djordje Miladinovic · Bernhard Schölkopf · Stefan Bauer
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #30
Lorentzian Distance Learning for Hyperbolic Representations
In Posters Thu
Marc Law · Renjie Liao · Jake Snell · Richard Zemel
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #31
Batch Policy Learning under Constraints
In Posters Thu
Hoang Le · Cameron Voloshin · Yisong Yue
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #32
Quantifying Generalization in Reinforcement Learning
In Posters Thu
Karl Cobbe · Oleg Klimov · Chris Hesse · Taehoon Kim · John Schulman
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #33
Learning Latent Dynamics for Planning from Pixels
In Posters Thu
Danijar Hafner · Timothy Lillicrap · Ian Fischer · Ruben Villegas · David Ha · Honglak Lee · James Davidson
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #34
Projections for Approximate Policy Iteration Algorithms
In Posters Thu
Riad Akrour · Joni Pajarinen · Jan Peters · Gerhard Neumann
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #35
Learning Structured Decision Problems with Unawareness
In Posters Thu
Craig Innes · Alex Lascarides
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #36
Calibrated Model-Based Deep Reinforcement Learning
In Posters Thu
Ali Malik · Volodymyr Kuleshov · Jiaming Song · Danny Nemer · Harlan Seymour · Stefano Ermon
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #37
Reinforcement Learning in Configurable Continuous Environments
In Posters Thu
Alberto Maria Metelli · Emanuele Ghelfi · Marcello Restelli
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #38
Target-Based Temporal-Difference Learning
In Posters Thu
Donghwan Lee · Niao He
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #39
Iterative Linearized Control: Stable Algorithms and Complexity Guarantees
In Posters Thu
Vincent Roulet · Dmitriy Drusvyatskiy · Siddhartha Srinivasa · Zaid Harchaoui
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #40
Finding Options that Minimize Planning Time
In Posters Thu
Yuu Jinnai · David Abel · David Hershkowitz · Michael L. Littman · George Konidaris
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #41
Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement
In Posters Thu
Wouter Kool · Herke van Hoof · Max Welling
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #42
Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs
In Posters Thu
Lingbing Guo · Zequn Sun · Wei Hu
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #43
Meta-Learning Neural Bloom Filters
In Posters Thu
Jack Rae · Sergey Bartunov · Timothy Lillicrap
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #44
CoT: Cooperative Training for Generative Modeling of Discrete Data
In Posters Thu
Sidi Lu · Lantao Yu · Siyuan Feng · Yaoming Zhu · Weinan Zhang
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #45
Non-Monotonic Sequential Text Generation
In Posters Thu
Sean Welleck · Kiante Brantley · Hal Daumé III · Kyunghyun Cho
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #46
Insertion Transformer: Flexible Sequence Generation via Insertion Operations
In Posters Thu
Mitchell Stern · William Chan · Jamie Kiros · Jakob Uszkoreit
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #47
Empirical Analysis of Beam Search Performance Degradation in Neural Sequence Models
In Posters Thu
Eldan Cohen · Christopher Beck
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #48
Trainable Decoding of Sets of Sequences for Neural Sequence Models
In Posters Thu
Ashwin Kalyan · Peter Anderson · Stefan Lee · Dhruv Batra
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #49
Learning to Generalize from Sparse and Underspecified Rewards
In Posters Thu
Rishabh Agarwal · Chen Liang · Dale Schuurmans · Mohammad Norouzi
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #50
Efficient Training of BERT by Progressively Stacking
In Posters Thu
Linyuan Gong · Di He · Zhuohan Li · Tao Qin · Liwei Wang · Tie-Yan Liu
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #51
Decentralized Exploration in Multi-Armed Bandits
In Posters Thu
Raphaël Féraud · REDA ALAMI · Romain Laroche
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #52
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback
In Posters Thu
Chicheng Zhang · Alekh Agarwal · Hal Daumé III · John Langford · Sahand Negahban
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #53
Exploiting structure of uncertainty for efficient matroid semi-bandits
In Posters Thu
Pierre Perrault · Vianney Perchet · Michal Valko
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #54
PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits
In Posters Thu
Arghya Roy Chaudhuri · Shivaram Kalyanakrishnan
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #55
Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model
In Posters Thu
Gi-Soo Kim · Myunghee Cho Paik
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #56
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning
In Posters Thu
Jakob Foerster · Francis Song · Edward Hughes · Neil Burch · Iain Dunning · Shimon Whiteson · Matthew Botvinick · Michael Bowling
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #57
TarMAC: Targeted Multi-Agent Communication
In Posters Thu
Abhishek Das · Theophile Gervet · Joshua Romoff · Dhruv Batra · Devi Parikh · Michael Rabbat · Joelle Pineau
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #58
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
In Posters Thu
Kyunghwan Son · Daewoo Kim · Wan Ju Kang · David Earl Hostallero · Yung Yi
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #59
Actor-Attention-Critic for Multi-Agent Reinforcement Learning
In Posters Thu
Shariq Iqbal · Fei Sha
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #60
Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation on Multi-Agent Reinforcement Learning
In Posters Thu
Thinh Doan · Siva Maguluri · Justin Romberg
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #61
Neural Network Attributions: A Causal Perspective
In Posters Thu
Aditya Chattopadhyay · Piyushi Manupriya · Anirban Sarkar · Vineeth N Balasubramanian
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #62
Towards a Deep and Unified Understanding of Deep Neural Models in NLP
In Posters Thu
Chaoyu Guan · Xiting Wang · Quanshi Zhang · Runjin Chen · Di He · Xing Xie
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #63
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation
In Posters Thu
Marco Ancona · Cengiz Oztireli · Markus Gross
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #64
Functional Transparency for Structured Data: a Game-Theoretic Approach
In Posters Thu
Guang-He Lee · Wengong Jin · David Alvarez-Melis · Tommi Jaakkola
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #65
Exploring interpretable LSTM neural networks over multi-variable data
In Posters Thu
Tian Guo · Tao Lin · Nino Antulov-Fantulin
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #66
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
In Posters Thu
Augustus Odena · Catherine Olsson · David Andersen · Ian Goodfellow
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #67
Gaining Free or Low-Cost Interpretability with Interpretable Partial Substitute
In Posters Thu
Tong Wang
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #68
State-Regularized Recurrent Neural Networks
In Posters Thu
Cheng Wang · Mathias Niepert
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #69
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation
In Posters Thu
Sahil Singla · Eric Wallace · Shi Feng · Soheil Feizi
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #70
On the Connection Between Adversarial Robustness and Saliency Map Interpretability
In Posters Thu
Christian Etmann · Sebastian Lunz · Peter Maass · Carola-Bibiane Schönlieb
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #71
Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR Problem
In Posters Thu
Alon Brutzkus · Amir Globerson
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #72
On the Spectral Bias of Neural Networks
In Posters Thu
Nasim Rahaman · Aristide Baratin · Devansh Arpit · Felix Draxler · Min Lin · Fred Hamprecht · Yoshua Bengio · Aaron Courville
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #73
Recursive Sketches for Modular Deep Learning
In Posters Thu
Badih Ghazi · Rina Panigrahy · Joshua R. Wang
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #74
Zero-Shot Knowledge Distillation in Deep Networks
In Posters Thu
Gaurav Kumar Nayak · Konda Reddy Mopuri · Vaisakh Shaj · Venkatesh Babu Radhakrishnan · Anirban Chakraborty
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #75
A Convergence Theory for Deep Learning via Over-Parameterization
In Posters Thu
Zeyuan Allen-Zhu · Yuanzhi Li · Zhao Song
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #76
A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks
In Posters Thu
Umut Simsekli · Levent Sagun · Mert Gurbuzbalaban
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #77
Approximation and non-parametric estimation of ResNet-type convolutional neural networks
In Posters Thu
Kenta Oono · Taiji Suzuki
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #78
Global Convergence of Block Coordinate Descent in Deep Learning
In Posters Thu
Jinshan ZENG · Tim Tsz-Kit Lau · Shaobo Lin · Yuan Yao
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #79
Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians
In Posters Thu
Vardan Papyan
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #80
On the Limitations of Representing Functions on Sets
In Posters Thu
Edward Wagstaff · Fabian Fuchs · Martin Engelcke · Ingmar Posner · Michael A Osborne
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #81
Probabilistic Neural Symbolic Models for Interpretable Visual Question Answering
In Posters Thu
Shanmukha Ramakrishna Vedantam · Karan Desai · Stefan Lee · Marcus Rohrbach · Dhruv Batra · Devi Parikh
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #82
Nonparametric Bayesian Deep Networks with Local Competition
In Posters Thu
Konstantinos Panousis · Sotirios Chatzis · Sergios Theodoridis
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #83
Good Initializations of Variational Bayes for Deep Models
In Posters Thu
Simone Rossi · Pietro Michiardi · Maurizio Filippone
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #84
Dropout as a Structured Shrinkage Prior
In Posters Thu
Eric Nalisnick · Jose Miguel Hernandez-Lobato · Padhraic Smyth
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #85
ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables
In Posters Thu
Mingzhang Yin · Yuguang Yue · Mingyuan Zhou
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #86
On Variational Bounds of Mutual Information
In Posters Thu
Ben Poole · Sherjil Ozair · Aäron van den Oord · Alexander Alemi · George Tucker
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #87
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation
In Posters Thu
Samuel Wiqvist · Pierre-Alexandre Mattei · Umberto Picchini · Jes Frellsen
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #88
Hierarchical Importance Weighted Autoencoders
In Posters Thu
Chin-Wei Huang · Kris Sankaran · Eeshan Dhekane · Alexandre Lacoste · Aaron Courville
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #89
Faster Attend-Infer-Repeat with Tractable Probabilistic Models
In Posters Thu
Karl Stelzner · Robert Peharz · Kristian Kersting
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #90
Understanding Priors in Bayesian Neural Networks at the Unit Level
In Posters Thu
Mariia Vladimirova · Jakob Verbeek · Pablo Mesejo · Julyan Arbel
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #91
Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning
In Posters Thu
Dong Yin · Yudong Chen · Kannan Ramchandran · Peter Bartlett
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #92
Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization
In Posters Thu
Baojian Zhou · Feng Chen · Yiming Ying
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #93
Neuron birth-death dynamics accelerates gradient descent and converges asymptotically
In Posters Thu
Grant Rotskoff · Samy Jelassi · Joan Bruna · Eric Vanden-Eijnden
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #94
Width Provably Matters in Optimization for Deep Linear Neural Networks
In Posters Thu
Simon Du · Wei Hu
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #95
Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path?
In Posters Thu
Samet Oymak · Mahdi Soltanolkotabi
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #96
Power k-Means Clustering
In Posters Thu
Jason Xu · Kenneth Lange
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #97
Distributed Learning over Unreliable Networks
In Posters Thu
Chen Yu · Hanlin Tang · Cedric Renggli · Simon Kassing · Ankit Singla · Dan Alistarh · Ce Zhang · Ji Liu
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #98
Escaping Saddle Points with Adaptive Gradient Methods
In Posters Thu
Matthew Staib · Sashank Jakkam Reddi · Satyen Kale · Sanjiv Kumar · Suvrit Sra
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #99
$\texttt{DoubleSqueeze}$: Parallel Stochastic Gradient Descent with Double-pass Error-Compensated Compression
In Posters Thu
Hanlin Tang · Chen Yu · Xiangru Lian · Tong Zhang · Ji Liu
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #100
Model Function Based Conditional Gradient Method with Armijo-like Line Search
In Posters Thu
Peter Ochs · Yura Malitsky
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #101
Analogies Explained: Towards Understanding Word Embeddings
In Posters Thu
Carl Allen · Timothy Hospedales
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #102
Parameter-Efficient Transfer Learning for NLP
In Posters Thu
Neil Houlsby · Andrei Giurgiu · Stanislaw Jastrzebski · Bruna Morrone · Quentin de Laroussilhe · Andrea Gesmundo · Mona Attariyan · Sylvain Gelly
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #103
Efficient On-Device Models using Neural Projections
In Posters Thu
Sujith Ravi
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #104
Deep Residual Output Layers for Neural Language Generation
In Posters Thu
Nikolaos Pappas · James Henderson
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #105
Improving Neural Language Modeling via Adversarial Training
In Posters Thu
Dilin Wang · Chengyue Gong · Qiang Liu
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #106
Mixture Models for Diverse Machine Translation: Tricks of the Trade
In Posters Thu
Tianxiao Shen · Myle Ott · Michael Auli · Marc'Aurelio Ranzato
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #107
MASS: Masked Sequence to Sequence Pre-training for Language Generation
In Posters Thu
Kaitao Song · Xu Tan · Tao Qin · Jianfeng Lu · Tie-Yan Liu
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #108
Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops
In Posters Thu
Limor Gultchin · Genevieve Patterson · Nancy Baym · Nathaniel Swinger · Adam Kalai
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #109
MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization
In Posters Thu
Eric Chu · Peter Liu
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #110
CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network
In Posters Thu
Tom Kenter · Vincent Wan · Chun-an Chan · Robert Clark · Jakub Vit
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #111
COMIC: Multi-view Clustering Without Parameter Selection
In Posters Thu
Xi Peng · Zhenyu Huang · Jiancheng Lv · Hongyuan Zhu · Joey Tianyi Zhou
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #112
The Wasserstein Transform
In Posters Thu
Facundo Memoli · Zane Smith · Zhengchao Wan
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #113
Sequential Facility Location: Approximate Submodularity and Greedy Algorithm
In Posters Thu
Ehsan Elhamifar
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #114
Neural Collaborative Subspace Clustering
In Posters Thu
Tong Zhang · Pan Ji · Mehrtash Harandi · Wenbing Huang · HONGDONG LI
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #115
Unsupervised Deep Learning by Neighbourhood Discovery
In Posters Thu
Jiabo Huang · Qi Dong · Shaogang Gong · Xiatian Zhu
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #116
Autoregressive Energy Machines
In Posters Thu
Conor Durkan · Charlie Nash
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #117
Greedy Orthogonal Pivoting Algorithm for Non-Negative Matrix Factorization
In Posters Thu
Kai Zhang · Sheng Zhang · Jun Liu · Jun Wang · Jie Zhang
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #118
Noise2Self: Blind Denoising by Self-Supervision
In Posters Thu
Joshua Batson · Loic Royer
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #119
Learning Dependency Structures for Weak Supervision Models
In Posters Thu
Paroma Varma · Frederic Sala · Ann He · Alexander J Ratner · Christopher Re
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #120
Geometry and Symmetry in Short-and-Sparse Deconvolution
In Posters Thu
Han-Wen Kuo · Yenson Lau · Yuqian Zhang · John Wright
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #121
On Sparse Linear Regression in the Local Differential Privacy Model
In Posters Thu
Di Wang · Jinhui Xu
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #122
Differentially Private Empirical Risk Minimization with Non-convex Loss Functions
In Posters Thu
Di Wang · Changyou Chen · Jinhui Xu
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #123
Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy
In Posters Thu
Kareem Amin · Alex Kulesza · andres munoz · Sergei Vassilvitskii
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #124
Differentially Private Learning of Geometric Concepts
In Posters Thu
Haim Kaplan · Yishay Mansour · Yossi Matias · Uri Stemmer
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #125
Toward Controlling Discrimination in Online Ad Auctions
In Posters Thu
L. Elisa Celis · Anay Mehrotra · Nisheeth Vishnoi
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #126
Learning Optimal Fair Policies
In Posters Thu
Razieh Nabi · Daniel Malinsky · Ilya Shpitser
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #127
Fairness-Aware Learning for Continuous Attributes and Treatments
In Posters Thu
Jeremie Mary · Clément Calauzènes · Noureddine El Karoui
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #128
Fairness risk measures
In Posters Thu
Robert C Williamson · Aditya Menon
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #129
Proportionally Fair Clustering
In Posters Thu
Xingyu Chen · Brandon Fain · Liang Lyu · Kamesh Munagala
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #130
Stable and Fair Classification
In Posters Thu
Lingxiao Huang · Nisheeth Vishnoi
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #131
Flexibly Fair Representation Learning by Disentanglement
In Posters Thu
Elliot Creager · David Madras · Joern-Henrik Jacobsen · Marissa Weis · Kevin Swersky · Toniann Pitassi · Richard Zemel
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #132
Fair Regression: Quantitative Definitions and Reduction-Based Algorithms
In Posters Thu
Alekh Agarwal · Miroslav Dudik · Steven Wu
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #133
Fairness without Harm: Decoupled Classifiers with Preference Guarantees
In Posters Thu
Berk Ustun · Yang Liu · David Parkes
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #134
Differentially Private Fair Learning
In Posters Thu
Matthew Jagielski · Michael Kearns · Jieming Mao · Alina Oprea · Aaron Roth · Saeed Sharifi-Malvajerdi · Jonathan Ullman
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #135
Obtaining Fairness using Optimal Transport Theory
In Posters Thu
Paula Gordaliza · Eustasio del Barrio · Gamboa Fabrice · Loubes Jean-Michel
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #136
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions
In Posters Thu
Hao Wang · Berk Ustun · Flavio Calmon
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #137
On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning
In Posters Thu
Hoda Heidari · Vedant Nanda · Krishna Gummadi
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #138
Making Decisions that Reduce Discriminatory Impacts
In Posters Thu
Matt J. Kusner · Chris Russell · Joshua Loftus · Ricardo Silva
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #139
Submodular Maximization beyond Non-negativity: Guarantees, Fast Algorithms, and Applications
In Posters Thu
Christopher Harshaw · Moran Feldman · Justin Ward · Amin Karbasi
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #140
Online Algorithms for Rent-Or-Buy with Expert Advice
In Posters Thu
Sreenivas Gollapudi · Debmalya Panigrahi
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #141
Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity
In Posters Thu
Matthew Fahrbach · Vahab Mirrokni · Morteza Zadimoghaddam
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #142
Categorical Feature Compression via Submodular Optimization
In Posters Thu
Mohammad Hossein Bateni · Lin Chen · Hossein Esfandiari · Thomas Fu · Vahab Mirrokni · Afshin Rostamizadeh
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #143
Multi-Frequency Phase Synchronization
In Posters Thu
Tingran Gao · Zhizhen Zhao
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #144
Faster Algorithms for Binary Matrix Factorization
In Posters Thu
Ravi Kumar · Rina Panigrahy · Ali Rahimi · David Woodruff
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #146
Guided evolutionary strategies: augmenting random search with surrogate gradients
In Posters Thu
Niru Maheswaranathan · Luke Metz · George Tucker · Dami Choi · Jascha Sohl-Dickstein
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #147
Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces
In Posters Thu
Johannes Kirschner · Mojmir Mutny · Nicole Hiller · Rasmus Ischebeck · Andreas Krause
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #148
Semi-Cyclic Stochastic Gradient Descent
In Posters Thu
Hubert Eichner · Tomer Koren · Brendan McMahan · Nati Srebro · Kunal Talwar
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #149
Matrix-Free Preconditioning in Online Learning
In Posters Thu
Ashok Cutkosky · Tamas Sarlos
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #150
Online Convex Optimization in Adversarial Markov Decision Processes
In Posters Thu
Aviv Rosenberg · Yishay Mansour
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #151
Competing Against Nash Equilibria in Adversarially Changing Zero-Sum Games
In Posters Thu
Adrian Rivera Cardoso · Jacob Abernethy · He Wang · Huan Xu
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #152
Online Learning with Sleeping Experts and Feedback Graphs
In Posters Thu
Corinna Cortes · Giulia DeSalvo · Claudio Gentile · Mehryar Mohri · Scott Yang
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #153
Incremental Randomized Sketching for Online Kernel Learning
In Posters Thu
Xiao Zhang · Shizhong Liao
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #154
Adaptive Scale-Invariant Online Algorithms for Learning Linear Models
In Posters Thu
Michal Kempka · Wojciech Kotlowski · Manfred K. Warmuth
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #155
Online Control with Adversarial Disturbances
In Posters Thu
Naman Agarwal · Brian Bullins · Elad Hazan · Sham Kakade · Karan Singh
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #156
Adversarial Online Learning with noise
In Posters Thu
Alon Resler · Yishay Mansour
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #157
Online Variance Reduction with Mixtures
In Posters Thu
Zalán Borsos · Sebastian Curi · Yehuda Levy · Andreas Krause
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #158
Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case
In Posters Thu
Alina Beygelzimer · David Pal · Balazs Szorenyi · Devanathan Thiruvenkatachari · Chen-Yu Wei · Chicheng Zhang
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #159
Learning Linear-Quadratic Regulators Efficiently with only $\sqrt{T}$ Regret
In Posters Thu
Alon Cohen · Tomer Koren · Yishay Mansour
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #160
Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems
In Posters Thu
Timothy Mann · Sven Gowal · András György · Huiyi Hu · Ray Jiang · Balaji Lakshminarayanan · Prav Srinivasan
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #161
Adaptive Regret of Convex and Smooth Functions
In Posters Thu
Lijun Zhang · Tie-Yan Liu · Zhi-Hua Zhou
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #162
Online Adaptive Principal Component Analysis and Its extensions
In Posters Thu
Jianjun Yuan · Andrew Lamperski
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #163
POLITEX: Regret Bounds for Policy Iteration using Expert Prediction
In Posters Thu
Yasin Abbasi-Yadkori · Peter Bartlett · Kush Bhatia · Nevena Lazic · Csaba Szepesvari · Gellért Weisz
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #164
Anytime Online-to-Batch, Optimism and Acceleration
In Posters Thu
Ashok Cutkosky
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #165
Cautious Regret Minimization: Online Optimization with Long-Term Budget Constraints
In Posters Thu
Nikolaos Liakopoulos · Apostolos Destounis · Georgios Paschos · Thrasyvoulos Spyropoulos · Panayotis Mertikopoulos
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #166
Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning
In Posters Thu
Frederik Benzing · Marcelo Matheus Gauy · Asier Mujika · Anders Martinsson · Angelika Steger
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #167
Adaptive Sensor Placement for Continuous Spaces
In Posters Thu
James A. Grant · Alexis Boukouvalas · Ryan-Rhys Griffiths · David Leslie · Sattar Vakili · Enrique Munoz De Cote
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #168
Scale-free adaptive planning for deterministic dynamics & discounted rewards
In Posters Thu
Peter Bartlett · Victor Gabillon · Jennifer Healey · Michal Valko
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #169
Communication-Constrained Inference and the Role of Shared Randomness
In Posters Thu
Jayadev Acharya · Clément Canonne · Himanshu Tyagi
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #170
Learning and Data Selection in Big Datasets
In Posters Thu
Hossein Shokri Ghadikolaei · Hadi Ghauch · Inst. of Technology Carlo Fischione · Mikael Skoglund
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #171
Sublinear quantum algorithms for training linear and kernel-based classifiers
In Posters Thu
Tongyang Li · Shouvanik Chakrabarti · Xiaodi Wu
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #172
Agnostic Federated Learning
In Posters Thu
Mehryar Mohri · Gary Sivek · Ananda Suresh
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #173
Discovering Conditionally Salient Features with Statistical Guarantees
In Posters Thu
Jaime Roquero Gimenez · James Zou
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #174
A Theoretical Analysis of Contrastive Unsupervised Representation Learning
In Posters Thu
Nikunj Umesh Saunshi · Orestis Plevrakis · Sanjeev Arora · Mikhail Khodak · Hrishikesh Khandeparkar
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #175
The information-theoretic value of unlabeled data in semi-supervised learning
In Posters Thu
Alexander Golovnev · David Pal · Balazs Szorenyi
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #176
Unsupervised Label Noise Modeling and Loss Correction
In Posters Thu
Eric Arazo · Diego Ortego · Paul Albert · Noel O'Connor · Kevin McGuinness
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #177
Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment
In Posters Thu
Yifan Wu · Ezra Winston · Divyansh Kaushik · Zachary Lipton
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #178
Pareto Optimal Streaming Unsupervised Classification
In Posters Thu
Soumya Basu · Steven Gutstein · Brent Lance · Sanjay Shakkottai
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #179
Geometric Losses for Distributional Learning
In Posters Thu
Arthur Mensch · Mathieu Blondel · Gabriel Peyré
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #180
Classification from Positive, Unlabeled and Biased Negative Data
In Posters Thu
Yu-Guan Hsieh · Gang Niu · Masashi Sugiyama
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #181
Complementary-Label Learning for Arbitrary Losses and Models
In Posters Thu
Takashi Ishida · Gang Niu · Aditya Menon · Masashi Sugiyama
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #182
Learning to Infer Program Sketches
In Posters Thu
Maxwell Nye · Luke Hewitt · Josh Tenenbaum · Armando Solar-Lezama
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #183
Hierarchically Structured Meta-learning
In Posters Thu
Huaxiu Yao · Ying WEI · Junzhou Huang · Zhenhui (Jessie) Li
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #184
Bridging Theory and Algorithm for Domain Adaptation
In Posters Thu
Yuchen Zhang · Tianle Liu · Mingsheng Long · Michael Jordan
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #185
Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation
In Posters Thu
Shani Gamrian · Yoav Goldberg
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #186
Learning What and Where to Transfer
In Posters Thu
Yunhun Jang · Hankook Lee · Sung Ju Hwang · Jinwoo Shin
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #187
DBSCAN++: Towards fast and scalable density clustering
In Posters Thu
Jennifer Jang · Heinrich Jiang
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #188
Concrete Autoencoders: Differentiable Feature Selection and Reconstruction
In Posters Thu
Muhammed Fatih Balın · Abubakar Abid · James Zou
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #189
Gromov-Wasserstein Learning for Graph Matching and Node Embedding
In Posters Thu
Hongteng Xu · Dixin Luo · Hongyuan Zha · Lawrence Carin
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #190
Spectral Clustering of Signed Graphs via Matrix Power Means
In Posters Thu
Pedro Mercado · Francesco Tudisco · Matthias Hein
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #191
Coresets for Ordered Weighted Clustering
In Posters Thu
Vladimir Braverman · Shaofeng Jiang · Robert Krauthgamer · Xuan Wu
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #192
Fair k-Center Clustering for Data Summarization
In Posters Thu
Matthäus Kleindessner · Pranjal Awasthi · Jamie Morgenstern
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #193
A Better k-means++ Algorithm via Local Search
In Posters Thu
Silvio Lattanzi · Christian Sohler
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #194
Kernel Normalized Cut: a Theoretical Revisit
In Posters Thu
Yoshikazu Terada · Michio Yamamoto
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #195
Guarantees for Spectral Clustering with Fairness Constraints
In Posters Thu
Matthäus Kleindessner · Samira Samadi · Pranjal Awasthi · Jamie Morgenstern
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #196
Supervised Hierarchical Clustering with Exponential Linkage
In Posters Thu
Nishant Yadav · Ari Kobren · Nicholas Monath · Andrew McCallum
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #197
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions
In Posters Thu
Antoine Liutkus · Umut Simsekli · Szymon Majewski · Alain Durmus · Fabian-Robert Stöter
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #198
Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization
In Posters Thu
Thanh Huy Nguyen · Umut Simsekli · Gaël RICHARD
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #199
Unifying Orthogonal Monte Carlo Methods
In Posters Thu
Krzysztof Choromanski · Mark Rowland · Wenyu Chen · Adrian Weller
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #200
Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits
In Posters Thu
Martin Zhang · James Zou · David Tse
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #201
Metropolis-Hastings Generative Adversarial Networks
In Posters Thu
Ryan Turner · Jane Hung · Eric Frank · Yunus Saatchi · Jason Yosinski
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #202
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets
In Posters Thu
Rob Cornish · Paul Vanetti · Alexandre Bouchard-Côté · George Deligiannidis · Arnaud Doucet
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #203
Replica Conditional Sequential Monte Carlo
In Posters Thu
Alex Shestopaloff · Arnaud Doucet
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #204
A Polynomial Time MCMC Method for Sampling from Continuous Determinantal Point Processes
In Posters Thu
Alireza Rezaei · Shayan Oveis Gharan
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #205
Adaptive Antithetic Sampling for Variance Reduction
In Posters Thu
Hongyu Ren · Shengjia Zhao · Stefano Ermon
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #206
Accelerated Flow for Probability Distributions
In Posters Thu
Amirhossein Taghvaei · Prashant Mehta
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #207
Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel $k$-means Clustering
In Posters Thu
Taisuke Yasuda · David Woodruff · Manuel Fernandez
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #208
Dimensionality Reduction for Tukey Regression
In Posters Thu
Kenneth Clarkson · Ruosong Wang · David Woodruff
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #209
Efficient Full-Matrix Adaptive Regularization
In Posters Thu
Naman Agarwal · Brian Bullins · Xinyi Chen · Elad Hazan · Karan Singh · Cyril Zhang · Yi Zhang
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #210
Breaking the gridlock in Mixture-of-Experts: Consistent and Efficient Algorithms
In Posters Thu
Ashok Vardhan Makkuva · Pramod Viswanath · Sreeram Kannan · Sewoong Oh
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #211
Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations
In Posters Thu
Quanming Yao · James Kwok · Bo Han
[ Video] 
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #212
Robust Estimation of Tree Structured Gaussian Graphical Models
In Posters Thu
Ashish Katiyar · Jessica Hoffmann · Constantine Caramanis
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #213
Spectral Approximate Inference
In Posters Thu
Sejun Park · Eunho Yang · Se-Young Yun · Jinwoo Shin
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #214
Partially Linear Additive Gaussian Graphical Models
In Posters Thu
Sinong Geng · Minhao Yan · Mladen Kolar · Sanmi Koyejo
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #215
DAG-GNN: DAG Structure Learning with Graph Neural Networks
In Posters Thu
Yue Yu · Jie Chen · Tian Gao · Mo Yu
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #216
Random Walks on Hypergraphs with Edge-Dependent Vertex Weights
In Posters Thu
Uthsav Chitra · Benjamin Raphael
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #217
Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random
In Posters Thu
Xiaojie Wang · Rui Zhang · Yu Sun · Jianzhong Qi
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #218
Linear-Complexity Data-Parallel Earth Mover's Distance Approximations
In Posters Thu
Kubilay Atasu · Thomas Mittelholzer
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #219
Model Comparison for Semantic Grouping
In Posters Thu
Francisco Vargas · Kamen Brestnichki · Nils Hammerla
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #220
RaFM: Rank-Aware Factorization Machines
In Posters Thu
Xiaoshuang Chen · Yin Zheng · Jiaxing Wang · Wenye Ma · Junzhou Huang
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #221
CAB: Continuous Adaptive Blending for Policy Evaluation and Learning
In Posters Thu
Yi Su · Luke Lequn Wang · Michele Santacatterina · Thorsten Joachims
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #222
MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement
In Posters Thu
Szu-Wei Fu · Chien-Feng Liao · Yu Tsao · Shou-De Lin
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #223
Neural Separation of Observed and Unobserved Distributions
In Posters Thu
Tavi Halperin · Ariel Ephrat · Yedid Hoshen
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #224
Almost Unsupervised Text to Speech and Automatic Speech Recognition
In Posters Thu
Yi Ren · Xu Tan · Tao Qin · Sheng Zhao · Zhou Zhao · Tie-Yan Liu
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #225
AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
In Posters Thu
Kaizhi Qian · Yang Zhang · Shiyu Chang · Xuesong Yang · Mark Hasegawa-Johnson
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #226
A fully differentiable beam search decoder
In Posters Thu
Ronan Collobert · Awni Hannun · Gabriel Synnaeve
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #227
Scaling Up Ordinal Embedding: A Landmark Approach
In Posters Thu
Jesse Anderton · Javed Aslam
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #228
Learning to select for a predefined ranking
In Posters Thu
Aleksei Ustimenko · Aleksandr Vorobev · Gleb Gusev · Pavel Serdyukov
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #229
Mallows ranking models: maximum likelihood estimate and regeneration
In Posters Thu
Wenpin Tang
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #230
Fast and Stable Maximum Likelihood Estimation for Incomplete Multinomial Models
In Posters Thu
Chenyang ZHANG · Guosheng Yin
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #231
Fast Algorithm for Generalized Multinomial Models with Ranking Data
In Posters Thu
Jiaqi Gu · Guosheng Yin
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #232
Graph Resistance and Learning from Pairwise Comparisons
In Posters Thu
Julien Hendrickx · Alex Olshevsky · Venkatesh Saligrama
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #233
Learning Context-dependent Label Permutations for Multi-label Classification
In Posters Thu
Jinseok Nam · Young-Bum Kim · Eneldo Loza Mencia · Sunghyun Park · Ruhi Sarikaya · Johannes Fürnkranz
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #234
Discovering Context Effects from Raw Choice Data
In Posters Thu
Arjun Seshadri · Alexander Peysakhovich · Johan Ugander
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #235
On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference
In Posters Thu
Rohin Shah · Noah Gundotra · Pieter Abbeel · Anca Dragan
Poster
Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #236
Learning Distance for Sequences by Learning a Ground Metric
In Posters Thu
Bing Su · Ying Wu