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Timezone: Europe/Vienna
Full Schedule Mon 7/13 Tue 7/14 Wed 7/15 Thu 7/16 Fri 7/17 Sat 7/18
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Poster
12:00 AM - 12:45 AM
9 Events in this session
Deep Streaming Label Learning
Zhen Wang ⋅ Liu Liu ⋅ Dacheng Tao
Stochastic Optimization for Regularized Wasserstein Estimators
Marin Ballu ⋅ Quentin Berthet ⋅ Francis Bach
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness
Aounon Kumar ⋅ Alexander Levine ⋅ Tom Goldstein ⋅ Soheil Feizi
Learning Factorized Weight Matrix for Joint Filtering
Xiangyu Xu ⋅ Yongrui Ma ⋅ Wenxiu Sun
Neural Network Control Policy Verification With Persistent Adversarial Perturbation
Yuh-Shyang Wang ⋅ Tsui-Wei Weng ⋅ Luca Daniel
Off-Policy Actor-Critic with Shared Experience Replay
Simon Schmitt ⋅ Matteo Hessel ⋅ Karen Simonyan
Randomization matters How to defend against strong adversarial attacks
Rafael Pinot ⋅ Raphael Ettedgui ⋅ Geovani Rizk ⋅ Yann Chevaleyre ⋅ Jamal Atif
Reliable Fidelity and Diversity Metrics for Generative Models
Muhammad Ferjad Naeem ⋅ Seong Joon Oh ⋅ Yunjey Choi ⋅ Youngjung Uh ⋅ Jaejun Yoo
Spectral Subsampling MCMC for Stationary Time Series
Robert Salomone ⋅ Matias Quiroz ⋅ Robert kohn ⋅ Mattias Villani ⋅ Minh-Ngoc Tran
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Poster
2:00 AM - 2:45 AM
82 Events in this session
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation
Florence Regol ⋅ Soumyasundar Pal ⋅ Yingxue Zhang ⋅ Mark Coates
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE
Juntang Zhuang ⋅ Nicha Dvornek ⋅ Xiaoxiao Li ⋅ Sekhar Tatikonda ⋅ Xenophon Papademetris ⋅ James Duncan
Born-again Tree Ensembles
Thibaut Vidal ⋅ Maximilian Schiffer
Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models
Rares-Darius Buhai ⋅ Yoni Halpern ⋅ Yoon Kim ⋅ Andrej Risteski ⋅ David Sontag
Enhancing Simple Models by Exploiting What They Already Know
Amit Dhurandhar ⋅ Karthikeyan Shanmugam ⋅ Ronny Luss
Error-Bounded Correction of Noisy Labels
Songzhu Zheng ⋅ Pengxiang Wu ⋅ Aman Goswami ⋅ Mayank Goswami ⋅ Dimitris Metaxas ⋅ Chao Chen
FACT: A Diagnostic for Group Fairness Trade-offs
Joon Kim ⋅ Jiahao Chen ⋅ Ameet Talwalkar
Improving Robustness of Deep-Learning-Based Image Reconstruction
Ankit Raj ⋅ Yoram Bresler ⋅ Bo Li
Learning Selection Strategies in Buchberger’s Algorithm
Dylan Peifer ⋅ Michael Stillman ⋅ Daniel Halpern-Leistner
Learning the Valuations of a $k$-demand Agent
Hanrui Zhang ⋅ Vincent Conitzer
Linear Convergence of Randomized Primal-Dual Coordinate Method for Large-scale Linear Constrained Convex Programming
Daoli Zhu ⋅ Lei Zhao
New Oracle-Efficient Algorithms for Private Synthetic Data Release
Giuseppe Vietri ⋅ Grace Tian ⋅ Mark Bun ⋅ Thomas Steinke ⋅ Steven Wu
p-Norm Flow Diffusion for Local Graph Clustering
Kimon Fountoulakis ⋅ Di Wang ⋅ Shenghao Yang
Privately detecting changes in unknown distributions
Rachel Cummings ⋅ Sara Krehbiel ⋅ Yuliia Lut ⋅ Wanrong Zhang
Robust Pricing in Dynamic Mechanism Design
Yuan Deng ⋅ Sébastien Lahaie ⋅ Vahab Mirrokni
Stochastic Flows and Geometric Optimization on the Orthogonal Group
Krzysztof Choromanski ⋅ David Cheikhi ⋅ Jared Quincy Davis ⋅ Valerii Likhosherstov ⋅ Achille Nazaret ⋅ Achraf Bahamou ⋅ Xingyou Song ⋅ Mrugank Akarte ⋅ Jack Parker-Holder ⋅ Jacob Bergquist ⋅ Yuan Gao ⋅ Aldo Pacchiano ⋅ Tamas Sarlos ⋅ Adrian Weller ⋅ Vikas Sindhwani
A Flexible Framework for Nonparametric Graphical Modeling that Accommodates Machine Learning
Yunhua Xiang ⋅ Noah Simon
Almost Tune-Free Variance Reduction
Bingcong Li ⋅ Lingda Wang ⋅ Georgios B. Giannakis
A Pairwise Fair and Community-preserving Approach to k-Center Clustering
Brian Brubach ⋅ Darshan Chakrabarti ⋅ John P Dickerson ⋅ Samir Khuller ⋅ Aravind Srinivasan ⋅ Leonidas Tsepenekas
BINOCULARS for efficient, nonmyopic sequential experimental design
Shali Jiang ⋅ Henry Chai ⋅ Javier Gonzalez ⋅ Roman Garnett
Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics
Matthew Hoffman ⋅ Yian Ma
Calibration, Entropy Rates, and Memory in Language Models
Mark Braverman ⋅ Xinyi Chen ⋅ Sham Kakade ⋅ Karthik Narasimhan ⋅ Cyril Zhang ⋅ Yi Zhang
CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information
Pengyu Cheng ⋅ Weituo Hao ⋅ Shuyang Dai ⋅ Jiachang Liu ⋅ Zhe Gan ⋅ Lawrence Carin
Concise Explanations of Neural Networks using Adversarial Training
Prasad Chalasani ⋅ Jiefeng Chen ⋅ Amrita Roy Chowdhury ⋅ Xi Wu ⋅ Somesh Jha
Constrained Markov Decision Processes via Backward Value Functions
Harsh Satija ⋅ Philip Amortila ⋅ Joelle Pineau
Continuous Graph Neural Networks
Louis-Pascal Xhonneux ⋅ Meng Qu ⋅ Jian Tang
Estimating Q(s,s') with Deep Deterministic Dynamics Gradients
Ashley Edwards ⋅ Himanshu Sahni ⋅ Rosanne Liu ⋅ Jane Hung ⋅ Ankit Jain ⋅ Rui Wang ⋅ Adrien Ecoffet ⋅ Thomas Miconi ⋅ Charles Isbell ⋅ Jason Yosinski
Estimation of Bounds on Potential Outcomes For Decision Making
Maggie Makar ⋅ Fredrik Johansson ⋅ John Guttag ⋅ David Sontag
Fiedler Regularization: Learning Neural Networks with Graph Sparsity
Edric Tam ⋅ David Dunson
From ImageNet to Image Classification: Contextualizing Progress on Benchmarks
Dimitris Tsipras ⋅ Shibani Santurkar ⋅ Logan Engstrom ⋅ Andrew Ilyas ⋅ Aleksander Madry
Graph Homomorphism Convolution
Hoang NT ⋅ Takanori Maehara
GraphOpt: Learning Optimization Models of Graph Formation
Rakshit Trivedi ⋅ Jiachen Yang ⋅ Hongyuan Zha
History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms
Kaiyi Ji ⋅ Zhe Wang ⋅ Bowen Weng ⋅ Yi Zhou ⋅ Wei Zhang ⋅ Yingbin LIANG
How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization
Chris Finlay ⋅ Joern-Henrik Jacobsen ⋅ Levon Nurbekyan ⋅ Adam Oberman
Improved Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance
Blair Bilodeau ⋅ Dylan Foster ⋅ Daniel Roy
Interpreting Robust Optimization via Adversarial Influence Functions
Zhun Deng ⋅ Cynthia Dwork ⋅ Jialiang Wang ⋅ Linjun Zhang
Invariant Rationalization
Shiyu Chang ⋅ Yang Zhang ⋅ Mo Yu ⋅ Tommi Jaakkola
Latent Variable Modelling with Hyperbolic Normalizing Flows
Joey Bose ⋅ Ariella Smofsky ⋅ Renjie Liao ⋅ Prakash Panangaden ⋅ Will Hamilton
Learning Robot Skills with Temporal Variational Inference
Tanmay Shankar ⋅ Abhinav Gupta
Learning with Bounded Instance- and Label-dependent Label Noise
Jiacheng Cheng ⋅ Tongliang Liu ⋅ Kotagiri Ramamohanarao ⋅ Dacheng Tao
Low-Rank Bottleneck in Multi-head Attention Models
Srinadh Bhojanapalli ⋅ Chulhee Yun ⋅ Ankit Singh Rawat ⋅ Sashank Jakkam Reddi ⋅ Sanjiv Kumar
Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
Silviu Pitis ⋅ Harris Chan ⋅ Stephen Zhao ⋅ Bradly Stadie ⋅ Jimmy Ba
Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation
Yaqi Duan ⋅ Zeyu Jia ⋅ Mengdi Wang
Multi-Agent Routing Value Iteration Network
Quinlan Sykora ⋅ Mengye Ren ⋅ Raquel Urtasun
Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification
Hongyuan Mei ⋅ Guanghui Qin ⋅ Minjie Xu ⋅ Jason Eisner
Online Learning with Dependent Stochastic Feedback Graphs
Corinna Cortes ⋅ Giulia DeSalvo ⋅ Claudio Gentile ⋅ Mehryar Mohri ⋅ Ningshan Zhang
On the Expressivity of Neural Networks for Deep Reinforcement Learning
Kefan Dong ⋅ Yuping Luo ⋅ Tianhe (Kevin) Yu ⋅ Chelsea Finn ⋅ Tengyu Ma
Optimizing Dynamic Structures with Bayesian Generative Search
Minh Hoang ⋅ Carleton Kingsford
Piecewise Linear Regression via a Difference of Convex Functions
Ali Siahkamari ⋅ Aditya Gangrade ⋅ Brian Kulis ⋅ Venkatesh Saligrama
Reward-Free Exploration for Reinforcement Learning
Chi Jin ⋅ Akshay Krishnamurthy ⋅ Max Simchowitz ⋅ Tiancheng Yu
Rigging the Lottery: Making All Tickets Winners
Utku Evci ⋅ Trevor Gale ⋅ Jacob Menick ⋅ Pablo Samuel Castro ⋅ Erich Elsen
Scalable Differentiable Physics for Learning and Control
Yi-Ling Qiao ⋅ Junbang Liang ⋅ Vladlen Koltun ⋅ Ming Lin
Skew-Fit: State-Covering Self-Supervised Reinforcement Learning
Vitchyr Pong ⋅ Murtaza Dalal ⋅ Steven Lin ⋅ Ashvin Nair ⋅ Shikhar Bahl ⋅ Sergey Levine
Sparse Convex Optimization via Adaptively Regularized Hard Thresholding
Kyriakos Axiotis ⋅ Maxim Sviridenko
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks
Blake Bordelon ⋅ Abdulkadir Canatar ⋅ Cengiz Pehlevan
Strategic Classification is Causal Modeling in Disguise
John Miller ⋅ Smitha Milli ⋅ Moritz Hardt
Transparency Promotion with Model-Agnostic Linear Competitors
Hassan Rafique ⋅ Tong Wang ⋅ Qihang Lin ⋅ Arshia Singhani
Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere
Tongzhou Wang ⋅ Phillip Isola
Universal Equivariant Multilayer Perceptrons
Siamak Ravanbakhsh
Upper bounds for Model-Free Row-Sparse Principal Component Analysis
Guanyi Wang ⋅ Santanu Dey
Projection-free Distributed Online Convex Optimization with $O(\sqrt{T})$ Communication Complexity
Yuanyu Wan ⋅ Wei-Wei Tu ⋅ Lijun Zhang
Representation Learning via Adversarially-Contrastive Optimal Transport
Anoop Cherian ⋅ Shuchin Aeron
A Graph to Graphs Framework for Retrosynthesis Prediction
Chence Shi ⋅ Minkai Xu ⋅ Hongyu Guo ⋅ Ming Zhang ⋅ Jian Tang
A Tree-Structured Decoder for Image-to-Markup Generation
Jianshu Zhang ⋅ Jun Du ⋅ Yongxin Yang ⋅ Yi-Zhe Song ⋅ Si Wei ⋅ Lirong Dai
Cost-effectively Identifying Causal Effects When Only Response Variable is Observable
Tian-Zuo Wang ⋅ Xi-Zhu Wu ⋅ Sheng-Jun Huang ⋅ Zhi-Hua Zhou
Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making
Chengchun Shi ⋅ Runzhe Wan ⋅ Rui Song ⋅ Wenbin Lu ⋅ Ling Leng
Do We Need Zero Training Loss After Achieving Zero Training Error?
Takashi Ishida ⋅ Ikko Yamane ⋅ Tomoya Sakai ⋅ Gang Niu ⋅ Masashi Sugiyama
DropNet: Reducing Neural Network Complexity via Iterative Pruning
Chong Min John Tan ⋅ Mehul Motani
Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks
Yonggang Zhang ⋅ Ya Li ⋅ Tongliang Liu ⋅ Xinmei Tian
Learning Autoencoders with Relational Regularization
Hongteng Xu ⋅ Dixin Luo ⋅ Ricardo Henao ⋅ Svati Shah ⋅ Lawrence Carin
Minimax Weight and Q-Function Learning for Off-Policy Evaluation
Masatoshi Uehara ⋅ Jiawei Huang ⋅ Nan Jiang
More Information Supervised Probabilistic Deep Face Embedding Learning
Ying Huang ⋅ Shangfeng Qiu ⋅ Wenwei Zhang ⋅ Xianghui Luo ⋅ Jinzhuo Wang
Multinomial Logit Bandit with Low Switching Cost
Kefan Dong ⋅ Yingkai Li ⋅ Qin Zhang ⋅ Yuan Zhou
Multi-objective Bayesian Optimization using Pareto-frontier Entropy
Shinya Suzuki ⋅ Shion Takeno ⋅ Tomoyuki Tamura ⋅ Kazuki Shitara ⋅ Masayuki Karasuyama
Non-autoregressive Machine Translation with Disentangled Context Transformer
Jungo Kasai ⋅ James Cross ⋅ Marjan Ghazvininejad ⋅ Jiatao Gu
On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes
Naoto Ohsaka ⋅ Tatsuya Matsuoka
On the Power of Compressed Sensing with Generative Models
Akshay Kamath ⋅ Eric Price ⋅ Sushrut Karmalkar
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup
Jang-Hyun Kim ⋅ Wonho Choo ⋅ Hyun Oh Song
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors
Zhaoqiang Liu ⋅ Selwyn Gomes ⋅ Avtansh Tiwari ⋅ Jonathan Scarlett
Self-Attentive Associative Memory
Hung Le ⋅ Truyen Tran ⋅ Svetha Venkatesh
Sparse Subspace Clustering with Entropy-Norm
Liang Bai ⋅ Jiye Liang
Transformer Hawkes Process
Simiao Zuo ⋅ Haoming Jiang ⋅ Zichong Li ⋅ Tuo Zhao ⋅ Hongyuan Zha
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Poster
3:00 AM - 3:45 AM
85 Events in this session
Acceleration through spectral density estimation
Fabian Pedregosa ⋅ Damien Scieur
Adaptive Adversarial Multi-task Representation Learning
YUREN MAO ⋅ Weiwei Liu ⋅ Xuemin Lin
Amortized Finite Element Analysis for Fast PDE-Constrained Optimization
Tianju Xue ⋅ Alex Beatson ⋅ Sigrid Adriaenssens ⋅ Ryan P. Adams
Analytic Marching: An Analytic Meshing Solution from Deep Implicit Surface Networks
Jiabao Lei ⋅ Kui Jia
Differentiable Product Quantization for End-to-End Embedding Compression
Ting Chen ⋅ Lala Li ⋅ Yizhou Sun
Do GANs always have Nash equilibria?
Farzan Farnia ⋅ Asuman Ozdaglar
FR-Train: A Mutual Information-Based Approach to Fair and Robust Training
Yuji Roh ⋅ Kangwook Lee ⋅ Steven Whang ⋅ Changho Suh
Improving Transformer Optimization Through Better Initialization
Xiao Shi Huang ⋅ Felipe Perez ⋅ Jimmy Ba ⋅ Maksims Volkovs
On a projective ensemble approach to two sample test for equality of distributions
Zhimei Li ⋅ Yaowu Zhang
Sharp Statistical Guaratees for Adversarially Robust Gaussian Classification
Chen Dan ⋅ Yuting Wei ⋅ Pradeep Ravikumar
Sparsified Linear Programming for Zero-Sum Equilibrium Finding
Brian Zhang ⋅ Tuomas Sandholm
Task Understanding from Confusing Multi-task Data
Xin Su ⋅ Yizhou Jiang ⋅ Shangqi Guo ⋅ Feng Chen
Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks
Micah Goldblum ⋅ Steven Reich ⋅ Liam Fowl ⋅ Renkun Ni ⋅ Valeriia Cherepanova ⋅ Tom Goldstein
Adaptive Region-Based Active Learning
Corinna Cortes ⋅ Giulia DeSalvo ⋅ Claudio Gentile ⋅ Mehryar Mohri ⋅ Ningshan Zhang
A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton
Risheng Liu ⋅ Pan Mu ⋅ Xiaoming Yuan ⋅ Shangzhi Zeng ⋅ Jin Zhang
Bandits for BMO Functions
Tianyu Wang ⋅ Cynthia Rudin
Black-Box Methods for Restoring Monotonicity
Evangelia Gergatsouli ⋅ Brendan Lucier ⋅ Christos Tzamos
Bridging the Gap Between f-GANs and Wasserstein GANs
Jiaming Song ⋅ Stefano Ermon
Causal Inference using Gaussian Processes with Structured Latent Confounders
Sam Witty ⋅ Kenta Takatsu ⋅ David Jensen ⋅ Vikash Mansinghka
Convex Calibrated Surrogates for the Multi-Label F-Measure
Mingyuan Zhang ⋅ Harish Guruprasad Ramaswamy ⋅ Shivani Agarwal
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space
Yingyi Ma ⋅ Vignesh Ganapathiraman ⋅ Yaoliang Yu ⋅ Xinhua Zhang
Countering Language Drift with Seeded Iterated Learning
Yuchen Lu ⋅ Soumye Singhal ⋅ Florian Strub ⋅ Aaron Courville ⋅ Olivier Pietquin
Deep Reinforcement Learning with Smooth Policy
Qianli Shen ⋅ Yan Li ⋅ Haoming Jiang ⋅ Zhaoran Wang ⋅ Tuo Zhao
Does label smoothing mitigate label noise?
Michal Lukasik ⋅ Srinadh Bhojanapalli ⋅ Aditya Menon ⋅ Sanjiv Kumar
Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance
Yasutoshi Ida ⋅ Sekitoshi Kanai ⋅ Yasuhiro Fujiwara ⋅ Tomoharu Iwata ⋅ Koh Takeuchi ⋅ Hisashi Kashima
Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters
Wenhui Yu ⋅ Zheng Qin
Input-Sparsity Low Rank Approximation in Schatten Norm
Yi Li ⋅ David Woodruff
Linear Lower Bounds and Conditioning of Differentiable Games
Adam Ibrahim ⋅ Waïss Azizian ⋅ Gauthier Gidel ⋅ Ioannis Mitliagkas
Mapping natural-language problems to formal-language solutions using structured neural representations
Kezhen Chen ⋅ Qiuyuan Huang ⋅ Hamid Palangi ⋅ Paul Smolensky ⋅ Ken Forbus ⋅ Jianfeng Gao
Meta Variance Transfer: Learning to Augment from the Others
Seong-Jin Park ⋅ Seungju Han ⋅ Ji-won Baek ⋅ Insoo Kim ⋅ Juhwan Song ⋅ Hae Beom Lee ⋅ Jae-Joon Han ⋅ Sung Ju Hwang
Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization
Debabrata Mahapatra ⋅ Vaibhav Rajan
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems
Tianyi Lin ⋅ Chi Jin ⋅ Michael Jordan
PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions
Zhengyang Shen ⋅ Lingshen He ⋅ Zhouchen Lin ⋅ Jinwen Ma
Refined bounds for algorithm configuration: The knife-edge of dual class approximability
Nina Balcan ⋅ Tuomas Sandholm ⋅ Ellen Vitercik
Robustifying Sequential Neural Processes
Jaesik Yoon ⋅ Gautam Singh ⋅ Sungjin Ahn
Robustness to Programmable String Transformations via Augmented Abstract Training
Yuhao Zhang ⋅ Aws Albarghouthi ⋅ Loris D'Antoni
Sequence Generation with Mixed Representations
Lijun Wu ⋅ Shufang Xie ⋅ Yingce Xia ⋅ Yang Fan ⋅ Jian-Huang Lai ⋅ Tao Qin ⋅ Tie-Yan Liu
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion
Qinqing Zheng ⋅ Jinshuo Dong ⋅ Qi Long ⋅ Weijie Su
Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization
Sudeep Salgia ⋅ Qing Zhao ⋅ Sattar Vakili
Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization
Quoc Tran-Dinh ⋅ Nhan H Pham ⋅ Lam M. Nguyen
Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle
Shaocong Ma ⋅ Yi Zhou
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies
Shengpu Tang ⋅ Aditya Modi ⋅ Michael Sjoding ⋅ Jenna Wiens
ConQUR: Mitigating Delusional Bias in Deep Q-Learning
DiJia Su ⋅ Jayden Ooi ⋅ Tyler Lu ⋅ Dale Schuurmans ⋅ Craig Boutilier
Educating Text Autoencoders: Latent Representation Guidance via Denoising
Tianxiao Shen ⋅ Jonas Mueller ⋅ Regina Barzilay ⋅ Tommi Jaakkola
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
Xiang Jiang ⋅ Qicheng Lao ⋅ Stan Matwin ⋅ Mohammad Havaei
Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing
Sanghamitra Dutta ⋅ Dennis Wei ⋅ Hazar Yueksel ⋅ Pin-Yu Chen ⋅ Sijia Liu ⋅ Kush Varshney
Multi-Objective Molecule Generation using Interpretable Substructures
Wengong Jin ⋅ Regina Barzilay ⋅ Tommi Jaakkola
On Efficient Constructions of Checkpoints
Yu Chen ⋅ Zhenming LIU ⋅ Bin Ren ⋅ Xin Jin
On Leveraging Pretrained GANs for Generation with Limited Data
Miaoyun Zhao ⋅ Yulai Cong ⋅ Lawrence Carin
Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates
Jeff Calder ⋅ Brendan Cook ⋅ Matthew Thorpe ⋅ Dejan Slepcev
Simultaneous Inference for Massive Data: Distributed Bootstrap
Yang Yu ⋅ Shih-Kang Chao ⋅ Guang Cheng
Spectral Frank-Wolfe Algorithm: Strict Complementarity and Linear Convergence
Lijun Ding ⋅ Yingjie Fei ⋅ Qiantong Xu ⋅ Chengrun Yang
Stochastic Hamiltonian Gradient Methods for Smooth Games
Nicolas Loizou ⋅ Hugo Berard ⋅ Alexia Jolicoeur-Martineau ⋅ Pascal Vincent ⋅ Simon Lacoste-Julien ⋅ Ioannis Mitliagkas
Time-aware Large Kernel Convolutions
Vasileios Lioutas ⋅ Yuhong Guo
ACFlow: Flow Models for Arbitrary Conditional Likelihoods
Yang Li ⋅ Shoaib Akbar ⋅ Junier Oliva
Adaptive Reward-Poisoning Attacks against Reinforcement Learning
Xuezhou Zhang ⋅ Yuzhe Ma ⋅ Adish Singla ⋅ Jerry Zhu
Adversarial Attacks on Copyright Detection Systems
Parsa Saadatpanah ⋅ Ali Shafahi ⋅ Tom Goldstein
Computational and Statistical Tradeoffs in Inferring Combinatorial Structures of Ising Model
Ying Jin ⋅ Zhaoran Wang ⋅ Junwei Lu
Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models
Amrita Roy Chowdhury ⋅ Theodoros Rekatsinas ⋅ Somesh Jha
Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks
Marko Vasic ⋅ Cameron Chalk ⋅ Sarfraz Khurshid ⋅ David Soloveichik
DRWR: A Differentiable Renderer without Rendering for Unsupervised 3D Structure Learning from Silhouette Images
Zhizhong Han ⋅ Chao Chen ⋅ Yushen Liu ⋅ Matthias Zwicker
Efficient Continuous Pareto Exploration in Multi-Task Learning
Pingchuan Ma ⋅ Tao Du ⋅ Wojciech Matusik
Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos
Subhroshekhar Ghosh ⋅ Krishna Balasubramanian ⋅ Xiaochuan Yang
Generalization via Derandomization
Jeffrey Negrea ⋅ Gintare Karolina Dziugaite ⋅ Daniel Roy
Goal-Aware Prediction: Learning to Model What Matters
Suraj Nair ⋅ Silvio Savarese ⋅ Chelsea Finn
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks
Denny Zhou ⋅ Mao Ye ⋅ Chen Chen ⋅ Tianjian Meng ⋅ Mingxing Tan ⋅ Xiaodan Song ⋅ Quoc Le ⋅ Qiang Liu ⋅ Dale Schuurmans
Graph Optimal Transport for Cross-Domain Alignment
Liqun Chen ⋅ Zhe Gan ⋅ Yu Cheng ⋅ Linjie Li ⋅ Lawrence Carin ⋅ Jingjing Liu
Improving generalization by controlling label-noise information in neural network weights
Hrayr Harutyunyan ⋅ Kyle Reing ⋅ Greg Ver Steeg ⋅ Aram Galstyan
Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information
Karl Stratos ⋅ Sam Wiseman
Momentum Improves Normalized SGD
Ashok Cutkosky ⋅ Harsh Mehta
More Data Can Expand The Generalization Gap Between Adversarially Robust and Standard Models
Lin Chen ⋅ Yifei Min ⋅ Mingrui Zhang ⋅ Amin Karbasi
No-Regret and Incentive-Compatible Online Learning
Rupert Freeman ⋅ David Pennock ⋅ Chara Podimata ⋅ Jennifer Wortman Vaughan
On hyperparameter tuning in general clustering problemsm
Xinjie Fan ⋅ Yuguang Yue ⋅ Purnamrita Sarkar ⋅ Y. X. Rachel Wang
Online Learned Continual Compression with Adaptive Quantization Modules
Lucas Caccia ⋅ Eugene Belilovsky ⋅ Massimo Caccia ⋅ Joelle Pineau
Online Learning with Imperfect Hints
Aditya Bhaskara ⋅ Ashok Cutkosky ⋅ Ravi Kumar ⋅ Manish Purohit
Provable Smoothness Guarantees for Black-Box Variational Inference
Justin Domke
Recht-Re Noncommutative Arithmetic-Geometric Mean Conjecture is False
Zehua Lai ⋅ Lek-Heng Lim
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound
Lin Yang ⋅ Mengdi Wang
The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation
Zhe Feng ⋅ David Parkes ⋅ Haifeng Xu
Uniform Convergence of Rank-weighted Learning
Justin Khim ⋅ Liu Leqi ⋅ Adarsh Prasad ⋅ Pradeep Ravikumar
Variance Reduction and Quasi-Newton for Particle-Based Variational Inference
Michael Zhu ⋅ Chang Liu ⋅ Jun Zhu
Few-shot Domain Adaptation by Causal Mechanism Transfer
Takeshi Teshima ⋅ Issei Sato ⋅ Masashi Sugiyama
Learning Efficient Multi-agent Communication: An Information Bottleneck Approach
Rundong Wang ⋅ Xu He ⋅ Runsheng Yu ⋅ Wei Qiu ⋅ Bo An ⋅ Zinovi Rabinovich
On Layer Normalization in the Transformer Architecture
Ruibin Xiong ⋅ Yunchang Yang ⋅ Di He ⋅ Kai Zheng ⋅ Shuxin Zheng ⋅ Chen Xing ⋅ Huishuai Zhang ⋅ Yanyan Lan ⋅ Liwei Wang ⋅ Tie-Yan Liu
Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training
Xuxi Chen ⋅ Wuyang Chen ⋅ Tianlong Chen ⋅ Ye Yuan ⋅ Chen Gong ⋅ Kewei Chen ⋅ Zhangyang “Atlas” Wang
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Poster
4:00 AM - 4:45 AM
51 Events in this session
Deep Graph Random Process for Relational-Thinking-Based Speech Recognition
Huang Hengguan ⋅ Fuzhao Xue ⋅ Hao Wang ⋅ Ye Wang
Reducing Sampling Error in Batch Temporal Difference Learning
Brahma Pavse ⋅ Ishan Durugkar ⋅ Josiah Hanna ⋅ Peter Stone
Towards Accurate Post-training Network Quantization via Bit-Split and Stitching
Peisong Wang ⋅ Qiang Chen ⋅ Xiangyu He ⋅ Jian Cheng
Universal Asymptotic Optimality of Polyak Momentum
Damien Scieur ⋅ Fabian Pedregosa
Do RNN and LSTM have Long Memory?
Jingyu Zhao ⋅ Feiqing Huang ⋅ Jia Lv ⋅ Yanjie Duan ⋅ Zhen Qin ⋅ Guodong Li ⋅ Guangjian Tian
Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards
Aadirupa Saha ⋅ Pierre Gaillard ⋅ Michal Valko
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data
Tamara Fernandez ⋅ Arthur Gretton ⋅ Nicolas Rivera ⋅ Wenkai Xu
Learning Optimal Tree Models under Beam Search
Jingwei Zhuo ⋅ Ziru Xu ⋅ Wei Dai ⋅ Han Zhu ⋅ HAN LI ⋅ Jian Xu ⋅ Kun Gai
Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems
Guangzeng Xie ⋅ Luo Luo ⋅ yijiang lian ⋅ Zhihua Zhang
On Breaking Deep Generative Model-based Defenses and Beyond
Yanzhi Chen ⋅ Renjie Xie ⋅ Zhanxing Zhu
Performative Prediction
Juan Perdomo ⋅ Tijana Zrnic ⋅ Celestine Mendler-Dünner ⋅ Moritz Hardt
Provably Efficient Model-based Policy Adaptation
Yuda Song ⋅ Aditi Mavalankar ⋅ Wen Sun ⋅ Sicun Gao
Tails of Lipschitz Triangular Flows
Priyank Jaini ⋅ Ivan Kobyzev ⋅ Yaoliang Yu ⋅ Marcus Brubaker
T-GD: Transferable GAN-generated Images Detection Framework
Hyeonseong Jeon ⋅ Young Oh Bang ⋅ Junyaup Kim ⋅ Simon Woo
Towards Understanding the Regularization of Adversarial Robustness on Neural Networks
Yuxin Wen ⋅ Shuai Li ⋅ Kui Jia
A Nearly-Linear Time Algorithm for Exact Community Recovery in Stochastic Block Model
Peng Wang ⋅ Zirui Zhou ⋅ Anthony Man-Cho So
Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion
Nghia Hoang ⋅ Thanh Lam ⋅ Bryan Kian Hsiang Low ⋅ Patrick Jaillet
Model-Based Reinforcement Learning with Value-Targeted Regression
Alex Ayoub ⋅ Zeyu Jia ⋅ Csaba Szepesvari ⋅ Mengdi Wang ⋅ Lin Yang
R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games
Zhongxiang Dai ⋅ Yizhou Chen ⋅ Bryan Kian Hsiang Low ⋅ Patrick Jaillet ⋅ Teck-Hua Ho
Distribution Augmentation for Generative Modeling
Heewoo Jun ⋅ Rewon Child ⋅ Mark Chen ⋅ John Schulman ⋅ Aditya Ramesh ⋅ Alec Radford ⋅ Ilya Sutskever
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data
Di Wang ⋅ Hanshen Xiao ⋅ Srinivas Devadas ⋅ Jinhui Xu
An Imitation Learning Approach for Cache Replacement
Evan Liu ⋅ Milad Hashemi ⋅ Kevin Swersky ⋅ Parthasarathy Ranganathan ⋅ Junwhan Ahn
Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels
Lu Jiang ⋅ Di Huang ⋅ Mason Liu ⋅ Weilong Yang
Coresets for Data-efficient Training of Machine Learning Models
Baharan Mirzasoleiman ⋅ Jeff Bilmes ⋅ Jure Leskovec
Minimax Pareto Fairness: A Multi Objective Perspective
Natalia Martinez Gil ⋅ Martin Bertran ⋅ Guillermo Sapiro
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC
Wei Deng ⋅ Qi Feng ⋅ Liyao Gao ⋅ Faming Liang ⋅ Guang Lin
Principled learning method for Wasserstein distributionally robust optimization with local perturbations
Yongchan Kwon ⋅ Wonyoung Kim ⋅ Joong-Ho (Johann) Won ⋅ Myunghee Cho Paik
UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training
Hangbo Bao ⋅ Li Dong ⋅ Furu Wei ⋅ Wenhui Wang ⋅ Nan Yang ⋅ Xiaodong Liu ⋅ Yu Wang ⋅ Jianfeng Gao ⋅ Songhao Piao ⋅ Ming Zhou ⋅ Hsiao-Wuen Hon
Visual Grounding of Learned Physical Models
Yunzhu Li ⋅ Toru Lin ⋅ Kexin Yi ⋅ Daniel Bear ⋅ Daniel Yamins ⋅ Jiajun Wu ⋅ Josh Tenenbaum ⋅ Antonio Torralba
AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation
Jae Hyun Lim ⋅ Aaron Courville ⋅ Christopher Pal ⋅ Chin-Wei Huang
Certified Data Removal from Machine Learning Models
Chuan Guo ⋅ Tom Goldstein ⋅ Awni Hannun ⋅ Laurens van der Maaten
Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems
Zhe Dong ⋅ Bryan Seybold ⋅ Kevin Murphy ⋅ Hung Bui
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks
Zhishuai Guo ⋅ Mingrui Liu ⋅ Zhuoning Yuan ⋅ Li Shen ⋅ Wei Liu ⋅ Tianbao Yang
Cost-Effective Interactive Attention Learning with Neural Attention Processes
Jay Heo ⋅ Junhyeon Park ⋅ Hyewon Jeong ⋅ Kwang Joon Kim ⋅ Juho Lee ⋅ Eunho Yang ⋅ Sung Ju Hwang
Defense Through Diverse Directions
Christopher Bender ⋅ Yang Li ⋅ Yifeng Shi ⋅ Michael K. Reiter ⋅ Junier Oliva
Domain Aggregation Networks for Multi-Source Domain Adaptation
Junfeng Wen ⋅ Russell Greiner ⋅ Dale Schuurmans
Emergence of Separable Manifolds in Deep Language Representations
Jonathan Mamou ⋅ Hang Le ⋅ Miguel A del Rio Fernandez ⋅ Cory Stephenson ⋅ Hanlin Tang ⋅ Yoon Kim ⋅ SueYeon Chung
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs
Meng Qu ⋅ Tianyu Gao ⋅ Louis-Pascal Xhonneux ⋅ Jian Tang
Generalization and Representational Limits of Graph Neural Networks
Vikas Garg ⋅ Stefanie Jegelka ⋅ Tommi Jaakkola
Generalized and Scalable Optimal Sparse Decision Trees
Jimmy Lin ⋅ Chudi Zhong ⋅ Diane Hu ⋅ Cynthia Rudin ⋅ Margo Seltzer
Gradient Temporal-Difference Learning with Regularized Corrections
Sina Ghiassian ⋅ Andrew Patterson ⋅ Shivam Garg ⋅ Dhawal Gupta ⋅ Adam White ⋅ Martha White
High-dimensional Robust Mean Estimation via Gradient Descent
Yu Cheng ⋅ Ilias Diakonikolas ⋅ Rong Ge ⋅ Mahdi Soltanolkotabi
Learning Human Objectives by Evaluating Hypothetical Behavior
Siddharth Reddy ⋅ Anca Dragan ⋅ Sergey Levine ⋅ Shane Legg ⋅ Jan Leike
Learning Mixtures of Graphs from Epidemic Cascades
Jessica Hoffmann ⋅ Soumya Basu ⋅ Surbhi Goel ⋅ Constantine Caramanis
Momentum-Based Policy Gradient Methods
Feihu Huang ⋅ Shangqian Gao ⋅ Jian Pei ⋅ Heng Huang
One-shot Distributed Ridge Regression in High Dimensions
Yue Sheng ⋅ Edgar Dobriban
Optimizing Data Usage via Differentiable Rewards
Xinyi Wang ⋅ Hieu Pham ⋅ Paul Michel ⋅ Antonios Anastasopoulos ⋅ Jaime Carbonell ⋅ Graham Neubig
Progressive Identification of True Labels for Partial-Label Learning
Jiaqi Lv ⋅ Miao Xu ⋅ Lei Feng ⋅ Gang Niu ⋅ Xin Geng ⋅ Masashi Sugiyama
SGD Learns One-Layer Networks in WGANs
Qi Lei ⋅ Jason Lee ⋅ Alexandros Dimakis ⋅ Constantinos Daskalakis
Sparse Sinkhorn Attention
Yi Tay ⋅ Dara Bahri ⋅ Liu Yang ⋅ Don Metzler ⋅ Da-Cheng Juan
The Performance Analysis of Generalized Margin Maximizers on Separable Data
Fariborz Salehi ⋅ Ehsan Abbasi ⋅ Babak Hassibi
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Poster
5:00 AM - 5:45 AM
39 Events in this session
Better depth-width trade-offs for neural networks through the lens of dynamical systems
Evangelos Chatziafratis ⋅ Sai Ganesh Nagarajan ⋅ Ioannis Panageas
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning
Lingxiao Wang ⋅ Zhuoran Yang ⋅ Zhaoran Wang
Converging to Team-Maxmin Equilibria in Zero-Sum Multiplayer Games
Youzhi Zhang ⋅ Bo An
Doubly robust off-policy evaluation with shrinkage
Yi Su ⋅ Maria Dimakopoulou ⋅ Akshay Krishnamurthy ⋅ Miroslav Dudik
Goodness-of-Fit Tests for Inhomogeneous Random Graphs
Soham Dan ⋅ Bhaswar B. Bhattacharya
Graph Structure of Neural Networks
Jiaxuan You ⋅ Jure Leskovec ⋅ Kaiming He ⋅ Saining Xie
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
Dipendra Kumar Misra ⋅ Mikael Henaff ⋅ Akshay Krishnamurthy ⋅ John Langford
Lorentz Group Equivariant Neural Network for Particle Physics
Alexander Bogatskiy ⋅ Brandon Anderson ⋅ Jan T Offermann ⋅ Marwah Roussi ⋅ David Miller ⋅ Risi Kondor
Normalized Loss Functions for Deep Learning with Noisy Labels
Xingjun Ma ⋅ Hanxun Huang ⋅ Yisen Wang ⋅ Simone Romano ⋅ Sarah Erfani ⋅ James Bailey
Optimal Differential Privacy Composition for Exponential Mechanisms
Jinshuo Dong ⋅ David Durfee ⋅ Ryan Rogers
Perceptual Generative Autoencoders
Zijun Zhang ⋅ Ruixiang ZHANG ⋅ Zongpeng Li ⋅ Yoshua Bengio ⋅ Liam Paull
Semi-Supervised Learning with Normalizing Flows
Pavel Izmailov ⋅ Polina Kirichenko ⋅ Marc Finzi ⋅ Andrew Wilson
Semi-Supervised StyleGAN for Disentanglement Learning
Weili Nie ⋅ Tero Karras ⋅ Animesh Garg ⋅ Shoubhik Debnath ⋅ Anjul Patney ⋅ Ankit Patel ⋅ Anima Anandkumar
Understanding and Stabilizing GANs' Training Dynamics Using Control Theory
Kun Xu ⋅ Chongxuan Li ⋅ Jun Zhu ⋅ Bo Zhang
Variational Inference for Sequential Data with Future Likelihood Estimates
Geon-Hyeong Kim ⋅ Youngsoo Jang ⋅ Hongseok Yang ⋅ Kee-Eung Kim
When Demands Evolve Larger and Noisier: Learning and Earning in a Growing Environment
Feng Zhu ⋅ Zeyu Zheng
One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control
Wenlong Huang ⋅ Igor Mordatch ⋅ Deepak Pathak
Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation
Shangtong Zhang ⋅ Bo Liu ⋅ Hengshuai Yao ⋅ Shimon Whiteson
Model Fusion with Kullback--Leibler Divergence
Sebastian Claici ⋅ Mikhail Yurochkin ⋅ Soumya Ghosh ⋅ Justin Solomon
Parameter-free, Dynamic, and Strongly-Adaptive Online Learning
Ashok Cutkosky
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions
Michael Chang ⋅ Sid Kaushik ⋅ S. Matthew Weinberg ⋅ Thomas Griffiths ⋅ Sergey Levine
On the Global Convergence Rates of Softmax Policy Gradient Methods
Jincheng Mei ⋅ Chenjun Xiao ⋅ Csaba Szepesvari ⋅ Dale Schuurmans
ROMA: Multi-Agent Reinforcement Learning with Emergent Roles
Tonghan Wang ⋅ Heng Dong ⋅ Victor Lesser ⋅ Chongjie Zhang
The Implicit Regularization of Stochastic Gradient Flow for Least Squares
Alnur Ali ⋅ Edgar Dobriban ⋅ Ryan Tibshirani
Accelerated Message Passing for Entropy-Regularized MAP Inference
Jonathan Lee ⋅ Aldo Pacchiano ⋅ Peter Bartlett ⋅ Michael Jordan
Aligned Cross Entropy for Non-Autoregressive Machine Translation
Marjan Ghazvininejad ⋅ Vladimir Karpukhin ⋅ Luke Zettlemoyer ⋅ Omer Levy
Bio-Inspired Hashing for Unsupervised Similarity Search
Chaitanya Ryali ⋅ John Hopfield ⋅ Leopold Grinberg ⋅ Dmitry Krotov
Concept Bottleneck Models
Pang Wei Koh ⋅ Thao Nguyen ⋅ Yew Siang Tang ⋅ Stephen Mussmann ⋅ Emma Pierson ⋅ Been Kim ⋅ Percy Liang
CURL: Contrastive Unsupervised Representations for Reinforcement Learning
Michael Laskin ⋅ Aravind Srinivas ⋅ Pieter Abbeel
Decision Trees for Decision-Making under the Predict-then-Optimize Framework
Adam Elmachtoub ⋅ Jason Cheuk Nam Liang ⋅ Ryan McNellis
Evaluating Machine Accuracy on ImageNet
Vaishaal Shankar ⋅ Rebecca Roelofs ⋅ Horia Mania ⋅ Alex Fang ⋅ Benjamin Recht ⋅ Ludwig Schmidt
Generalization Error of Generalized Linear Models in High Dimensions
Melikasadat Emami ⋅ Mojtaba Sahraee-Ardakan ⋅ Parthe Pandit ⋅ Sundeep Rangan ⋅ Alyson Fletcher
Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection
Mao Ye ⋅ Chengyue Gong ⋅ Lizhen Nie ⋅ Denny Zhou ⋅ Adam Klivans ⋅ Qiang Liu
Implicit competitive regularization in GANs
Florian Schäfer ⋅ Hongkai Zheng ⋅ Anima Anandkumar
Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: Joint Gradient Estimation and Tracking
Haoran Sun ⋅ Songtao Lu ⋅ Mingyi Hong
Inferring DQN structure for high-dimensional continuous control
Andrey Sakryukin ⋅ Chedy Raissi ⋅ Mohan Kankanhalli
Learning to Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning
Sai Krishna Gottipati ⋅ Boris Sattarov ⋅ Sufeng Niu ⋅ Yashaswi Pathak ⋅ Haoran Wei ⋅ Shengchao Liu ⋅ Shengchao Liu ⋅ Simon Blackburn ⋅ Karam Thomas ⋅ Connor Coley ⋅ Jian Tang ⋅ Sarath Chandar ⋅ Yoshua Bengio
Video Prediction via Example Guidance
Jingwei Xu ⋅ Huazhe Xu ⋅ Bingbing Ni ⋅ Xiaokang Yang ⋅ Trevor Darrell
When Does Self-Supervision Help Graph Convolutional Networks?
Yuning You ⋅ Tianlong Chen ⋅ Zhangyang “Atlas” Wang ⋅ Yang Shen
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Poster
8:00 AM - 8:45 AM
22 Events in this session
Bounding the fairness and accuracy of classifiers from population statistics
Sivan Sabato ⋅ Elad Yom-Tov
ECLIPSE: An Extreme-Scale Linear Program Solver for Web-Applications
Kinjal Basu ⋅ Amol Ghoting ⋅ Rahul Mazumder ⋅ Yao Pan
Energy-Based Processes for Exchangeable Data
Sherry Yang ⋅ Bo Dai ⋅ Hanjun Dai ⋅ Dale Schuurmans
Multiclass Neural Network Minimization via Tropical Newton Polytope Approximation
Georgios Smyrnis ⋅ Petros Maragos
Active World Model Learning in Agent-rich Environments with Progress Curiosity
Kuno Kim ⋅ Megumi Sano ⋅ Julian De Freitas ⋅ Nick Haber ⋅ Daniel Yamins
A Distributional Framework For Data Valuation
Amirata Ghorbani ⋅ Michael Kim ⋅ James Zou
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
Jian Liang ⋅ Dapeng Hu ⋅ Jiashi Feng
From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model
Aadirupa Saha ⋅ Aditya Gopalan
Improved Optimistic Algorithms for Logistic Bandits
Louis Faury ⋅ Marc Abeille ⋅ Clément Calauzènes ⋅ Olivier Fercoq
WaveFlow: A Compact Flow-based Model for Raw Audio
Wei Ping ⋅ Kainan Peng ⋅ Kexin Zhao ⋅ Zhao Song
Inter-domain Deep Gaussian Processes
Tim G. J. Rudner ⋅ Dino Sejdinovic ⋅ Yarin Gal
Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks
Alexander Shevchenko ⋅ Marco Mondelli
Learning Flat Latent Manifolds with VAEs
Nutan Chen ⋅ Alexej Klushyn ⋅ Francesco Ferroni ⋅ Justin Bayer ⋅ Patrick van der Smagt
Missing Data Imputation using Optimal Transport
Boris Muzellec ⋅ Julie Josse ⋅ Claire Boyer ⋅ Marco Cuturi
Optimizer Benchmarking Needs to Account for Hyperparameter Tuning
Prabhu Teja Sivaprasad ⋅ Florian Mai ⋅ Thijs Vogels ⋅ Martin Jaggi ⋅ François Fleuret
A Flexible Latent Space Model for Multilayer Networks
Xuefei Zhang ⋅ Songkai Xue ⋅ Ji Zhu
Composable Sketches for Functions of Frequencies: Beyond the Worst Case
Edith Cohen ⋅ Ofir Geri ⋅ Rasmus Pagh
Naive Exploration is Optimal for Online LQR
Max Simchowitz ⋅ Dylan Foster
Online Continual Learning from Imbalanced Data
Aristotelis Chrysakis ⋅ Marie-Francine Moens
Online Multi-Kernel Learning with Graph-Structured Feedback
Pouya M Ghari ⋅ Yanning Shen
On Thompson Sampling with Langevin Algorithms
Eric Mazumdar ⋅ Aldo Pacchiano ⋅ Yian Ma ⋅ Michael Jordan ⋅ Peter Bartlett
Topologically Densified Distributions
Christoph Hofer ⋅ Florian Graf ⋅ Marc Niethammer ⋅ Roland Kwitt
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Poster
9:00 AM - 9:45 AM
18 Events in this session
Symbolic Network: Generalized Neural Policies for Relational MDPs
Sankalp Garg ⋅ Aniket Bajpai ⋅ Mausam
Unsupervised Discovery of Interpretable Directions in the GAN Latent Space
Andrey Voynov ⋅ Artem Babenko
Information Particle Filter Tree: An Online Algorithm for POMDPs with Belief-Based Rewards on Continuous Domains
Johannes Fischer ⋅ Ömer Sahin Tas
Soft Threshold Weight Reparameterization for Learnable Sparsity
Aditya Kusupati ⋅ Vivek Ramanujan ⋅ Raghav Somani ⋅ Mitchell Wortsman ⋅ Prateek Jain ⋅ Sham Kakade ⋅ Ali Farhadi
Double-Loop Unadjusted Langevin Algorithm
Paul Rolland ⋅ Armin Eftekhari ⋅ Ali Kavis ⋅ Volkan Cevher
Gradient-free Online Learning in Continuous Games with Delayed Rewards
Amélie Héliou ⋅ Panayotis Mertikopoulos ⋅ Zhengyuan Zhou
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules
Sarthak Mittal ⋅ Alex Lamb ⋅ Anirudh Goyal ⋅ Vikram Voleti ⋅ Murray Shanahan ⋅ Guillaume Lajoie ⋅ Michael Mozer ⋅ Yoshua Bengio
Low Bias Low Variance Gradient Estimates for Hierarchical Boolean Stochastic Networks
Adeel Pervez ⋅ Taco Cohen ⋅ Efstratios Gavves
Neural Kernels Without Tangents
Vaishaal Shankar ⋅ Alex Fang ⋅ Wenshuo Guo ⋅ Sara Fridovich-Keil ⋅ Jonathan Ragan-Kelley ⋅ Ludwig Schmidt ⋅ Benjamin Recht
PowerNorm: Rethinking Batch Normalization in Transformers
Sheng Shen ⋅ Zhewei Yao ⋅ Amir Gholaminejad ⋅ Michael Mahoney ⋅ Kurt Keutzer
Random extrapolation for primal-dual coordinate descent
Ahmet Alacaoglu ⋅ Olivier Fercoq ⋅ Volkan Cevher
Randomized Block-Diagonal Preconditioning for Parallel Learning
Celestine Mendler-Dünner ⋅ Aurelien Lucchi
Real-Time Optimisation for Online Learning in Auctions
Lorenzo Croissant ⋅ Marc Abeille ⋅ Clément Calauzènes
Efficient Proximal Mapping of the 1-path-norm of Shallow Networks
Fabian Latorre ⋅ Paul Rolland ⋅ Shaul Nadav Hallak ⋅ Volkan Cevher
Learning to Branch for Multi-Task Learning
Pengsheng Guo ⋅ Chen-Yu Lee ⋅ Daniel Ulbricht
Robust Learning with the Hilbert-Schmidt Independence Criterion
Daniel Greenfeld ⋅ Uri Shalit
The Boomerang Sampler
Joris Bierkens ⋅ Sebastiano Grazzi ⋅ Kengo Kamatani ⋅ Gareth Roberts
Topic Modeling via Full Dependence Mixtures
Dan Fisher ⋅ Mark Kozdoba ⋅ Shie Mannor
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Poster
10:00 AM - 10:45 AM
32 Events in this session
Graph-based Nearest Neighbor Search: From Practice to Theory
Liudmila Prokhorenkova ⋅ Aleksandr Shekhovtsov
Aggregation of Multiple Knockoffs
Tuan-Binh Nguyen ⋅ Jerome-Alexis Chevalier ⋅ Thirion Bertrand ⋅ Sylvain Arlot
Bisection-Based Pricing for Repeated Contextual Auctions against Strategic Buyer
Anton Zhiyanov ⋅ Alexey Drutsa
Conditional gradient methods for stochastically constrained convex minimization
Maria-Luiza Vladarean ⋅ Ahmet Alacaoglu ⋅ Ya-Ping Hsieh ⋅ Volkan Cevher
Data-Efficient Image Recognition with Contrastive Predictive Coding
Olivier Henaff
Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions
Ahmed Alaa ⋅ Mihaela van der Schaar
Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More
Aleksandar Bojchevski ⋅ Johannes Gasteiger ⋅ Stephan Günnemann
Extra-gradient with player sampling for faster convergence in n-player games
Samy Jelassi ⋅ Carles Domingo-Enrich ⋅ Damien Scieur ⋅ Arthur Mensch ⋅ Joan Bruna
Frequency Bias in Neural Networks for Input of Non-Uniform Density
Ronen Basri ⋅ Meirav Galun ⋅ Amnon Geifman ⋅ David Jacobs ⋅ Yoni Kasten ⋅ Shira Kritchman
Learning to Rank Learning Curves
Martin Wistuba ⋅ Tejaswini Pedapati
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack
Francesco Croce ⋅ Matthias Hein
Monte-Carlo Tree Search as Regularized Policy Optimization
Jean-Bastien Grill ⋅ Florent Altché ⋅ Yunhao Tang ⋅ Thomas Hubert ⋅ Michal Valko ⋅ Ioannis Antonoglou ⋅ Remi Munos
Near-optimal Regret Bounds for Stochastic Shortest Path
Aviv Rosenberg ⋅ Alon Cohen ⋅ Yishay Mansour ⋅ Haim Kaplan
Optimistic Bounds for Multi-output Learning
Henry Reeve ⋅ Ata Kaban
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Jingqing Zhang ⋅ Yao Zhao ⋅ Mohammad Saleh ⋅ Peter Liu
Predictive Coding for Locally-Linear Control
Rui Shu ⋅ Tung Nguyen ⋅ Yinlam Chow ⋅ Tuan Pham ⋅ Khoat Than ⋅ Mohammad Ghavamzadeh ⋅ Stefano Ermon ⋅ Hung Bui
Revisiting Training Strategies and Generalization Performance in Deep Metric Learning
Karsten Roth ⋅ Timo Milbich ⋅ Samrath Sinha ⋅ Prateek Gupta ⋅ Bjorn Ommer ⋅ Joseph Paul Cohen
Supervised Quantile Normalization for Low Rank Matrix Factorization
Marco Cuturi ⋅ Olivier Teboul ⋅ Jonathan Niles-Weed ⋅ Jean-Philippe Vert
The Complexity of Finding Stationary Points with Stochastic Gradient Descent
Yoel Drori ⋅ Ohad Shamir
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Jakub Swiatkowski ⋅ Kevin Roth ⋅ Bastiaan Veeling ⋅ Linh Tran ⋅ Joshua V Dillon ⋅ Jasper Snoek ⋅ Stephan Mandt ⋅ Tim Salimans ⋅ Rodolphe Jenatton ⋅ Sebastian Nowozin
Stochastic Latent Residual Video Prediction
Jean-Yves Franceschi ⋅ Edouard Delasalles ⋅ Mickael Chen ⋅ Sylvain Lamprier ⋅ Patrick Gallinari
A quantile-based approach for hyperparameter transfer learning
David Salinas ⋅ Huibin Shen ⋅ Valerio Perrone
Graph Filtration Learning
Christoph Hofer ⋅ Florian Graf ⋅ Bastian Rieck ⋅ Marc Niethammer ⋅ Roland Kwitt
Neural Topic Modeling with Continual Lifelong Learning
Pankaj Gupta ⋅ Yatin Chaudhary ⋅ Thomas Runkler ⋅ Hinrich Schuetze
Predicting Choice with Set-Dependent Aggregation
Nir Rosenfeld ⋅ Kojin Oshiba ⋅ Yaron Singer
Spectral Clustering with Graph Neural Networks for Graph Pooling
Filippo Maria Bianchi ⋅ Daniele Grattarola ⋅ Cesare Alippi
Stochastic Differential Equations with Variational Wishart Diffusions
Martin Jørgensen ⋅ Marc Deisenroth ⋅ Hugh Salimbeni
Stochastic Subspace Cubic Newton Method
Filip Hanzely ⋅ Nikita Doikov ⋅ Yurii Nesterov ⋅ Peter Richtarik
Anderson Acceleration of Proximal Gradient Methods
Vien Mai ⋅ Mikael Johansson
Extrapolation for Large-batch Training in Deep Learning
Tao Lin ⋅ Lingjing Kong ⋅ Sebastian Stich ⋅ Martin Jaggi
Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks
Mark Kurtz ⋅ Justin Kopinsky ⋅ Rati Gelashvili ⋅ Alexander Matveev ⋅ John Carr ⋅ Michael Goin ⋅ William Leiserson ⋅ Sage Moore ⋅ Nir Shavit ⋅ Dan Alistarh
Scalable Gaussian Process Separation for Kernels with a Non-Stationary Phase
Jan Graßhoff ⋅ Alexandra Jankowski ⋅ Philipp Rostalski
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Poster
11:00 AM - 11:45 AM
18 Events in this session
Amortised Learning by Wake-Sleep
Li Kevin Wenliang ⋅ Theodore Moskovitz ⋅ Heishiro Kanagawa ⋅ Maneesh Sahani
Convolutional Kernel Networks for Graph-Structured Data
Dexiong Chen ⋅ Laurent Jacob ⋅ Julien Mairal
Latent Bernoulli Autoencoder
Jiri Fajtl ⋅ Vasileios Argyriou ⋅ Dorothy Monekosso ⋅ Paolo Remagnino
On Efficient Low Distortion Ultrametric Embedding
Vincent Cohen-Addad ⋅ Karthik C. S. ⋅ Guillaume Lagarde
Preselection Bandits
Viktor Bengs ⋅ Eyke Hüllermeier
A Generative Model for Molecular Distance Geometry
Gregor Simm ⋅ Jose Miguel Hernandez-Lobato
Influenza Forecasting Framework based on Gaussian Processes
Christoph Zimmer ⋅ Reza Yaesoubi
Kernel interpolation with continuous volume sampling
Ayoub Belhadji ⋅ Rémi Bardenet ⋅ Pierre Chainais
Modulating Surrogates for Bayesian Optimization
Erik Bodin ⋅ Markus Kaiser ⋅ Ieva Kazlauskaite ⋅ Zhenwen Dai ⋅ Neill Campbell ⋅ Carl Henrik Ek
Projective Preferential Bayesian Optimization
Petrus Mikkola ⋅ Milica Todorović ⋅ Jari Järvi ⋅ Patrick Rinke ⋅ Samuel Kaski
Teaching with Limited Information on the Learner's Behaviour
Ferdinando Cicalese ⋅ Francisco Sergio de Freitas Filho ⋅ Eduardo Laber ⋅ Marco Molinaro
The FAST Algorithm for Submodular Maximization
Adam Breuer ⋅ Eric Balkanski ⋅ Yaron Singer
Towards Adaptive Residual Network Training: A Neural-ODE Perspective
Chengyu Dong ⋅ Liyuan Liu ⋅ Zichao Li ⋅ Jingbo Shang
Towards non-parametric drift detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD)
Fabian Hinder ⋅ André Artelt ⋅ CITEC Barbara Hammer
Training Binary Neural Networks using the Bayesian Learning Rule
Xiangming Meng ⋅ Roman Bachmann ⋅ Mohammad Emtiyaz Khan
Optimistic Policy Optimization with Bandit Feedback
Lior Shani ⋅ Yonathan Efroni ⋅ Aviv Rosenberg ⋅ Shie Mannor
Stochastic Optimization for Regularized Wasserstein Estimators
Marin Ballu ⋅ Quentin Berthet ⋅ Francis Bach
Reliable Fidelity and Diversity Metrics for Generative Models
Muhammad Ferjad Naeem ⋅ Seong Joon Oh ⋅ Yunjey Choi ⋅ Youngjung Uh ⋅ Jaejun Yoo
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Workshop
11:00 AM - 5:00 AM
17 Events in this session
Self-supervision in Audio and Speech
Mirco Ravanelli ⋅ Dmitriy Serdyuk ⋅ R Devon Hjelm ⋅ Bhuvana Ramabhadran ⋅ Titouan Parcollet
5th ICML Workshop on Human Interpretability in Machine Learning (WHI)
Adrian Weller ⋅ Alice Xiang ⋅ Amit Dhurandhar ⋅ Been Kim ⋅ Dennis Wei ⋅ Kush Varshney ⋅ Umang Bhatt
Law & Machine Learning
Céline Castets-Renard ⋅ Sylvain Cussat-Blanc ⋅ Laurent Risser
Learning with Missing Values
Julie Josse ⋅ Jes Frellsen ⋅ Pierre-Alexandre Mattei ⋅ Gael Varoquaux
Healthcare Systems, Population Health, and the Role of Health-tech
Creighton Heaukulani ⋅ Konstantina Palla ⋅ Katherine Heller ⋅ Niranjani Prasad ⋅ Marzyeh Ghassemi
Challenges in Deploying and Monitoring Machine Learning Systems
Alessandra Tosi ⋅ Nathan Korda ⋅ Neil Lawrence
Workshop on AI for Autonomous Driving (AIAD)
Wei-Lun (Harry) Chao ⋅ Rowan McAllister ⋅ Adrien Gaidon ⋅ Li Erran Li ⋅ Sven Kreiss
MLRetrospectives: A Venue for Self-Reflection in ML Research
Jessica Forde ⋅ Jesse Dodge ⋅ Mayoore Jaiswal ⋅ Rosanne Liu ⋅ Ryan Lowe ⋅ Rosanne Liu ⋅ Joelle Pineau ⋅ Yoshua Bengio
ICML 2020 Workshop on Computational Biology
Workshop CompBio ⋅ Delasa Aghamirzaie ⋅ Alexander Anderson ⋅ Elham Azizi ⋅ Abdoulaye Baniré Diallo ⋅ Cassandra Burdziak ⋅ Jill Gallaher ⋅ Anshul Kundaje ⋅ Dana Pe'er ⋅ Sandhya Prabhakaran ⋅ Amine Remita ⋅ Mark Robertson-Tessi ⋅ Wesley Tansey ⋅ Julia Vogt ⋅ Yubin Xie
Participatory Approaches to Machine Learning
Angela Zhou ⋅ David Madras ⋅ Deborah Raji ⋅ Smitha Milli ⋅ Bogdan Kulynych ⋅ Richard Zemel
Workshop on Continual Learning
Haytham Fayek ⋅ Arslan Chaudhry ⋅ David Lopez-Paz ⋅ Eugene Belilovsky ⋅ Jonathan Schwarz ⋅ Marc Pickett ⋅ Rahaf Aljundi ⋅ Sayna Ebrahimi ⋅ Razvan Pascanu ⋅ Puneet Dokania
Workshop on eXtreme Classification: Theory and Applications
Anna Choromanska ⋅ John Langford ⋅ Maryam Majzoubi ⋅ Yashoteja Prabhu
Object-Oriented Learning: Perception, Representation, and Reasoning
Sungjin Ahn ⋅ Adam Kosiorek ⋅ Jessica Hamrick ⋅ Sjoerd van Steenkiste ⋅ Yoshua Bengio
Theoretical Foundations of Reinforcement Learning
Emma Brunskill ⋅ Thodoris Lykouris ⋅ Max Simchowitz ⋅ Wen Sun ⋅ Mengdi Wang
ML Interpretability for Scientific Discovery
Subhashini Venugopalan ⋅ Michael Brenner ⋅ Scott Linderman ⋅ Been Kim
Uncertainty and Robustness in Deep Learning Workshop (UDL)
Sharon Yixuan Li ⋅ Balaji Lakshminarayanan ⋅ Dan Hendrycks ⋅ Thomas Dietterich ⋅ Jasper Snoek
Beyond first order methods in machine learning systems
Albert S Berahas ⋅ Amir Gholaminejad ⋅ Anastasios Kyrillidis ⋅ Michael Mahoney ⋅ Fred Roosta
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Poster
12:00 PM - 12:45 PM
13 Events in this session
Probing Emergent Semantics in Predictive Agents via Question Answering
Abhishek Das ⋅ Federico Carnevale ⋅ Hamza Merzic ⋅ Laura Rimell ⋅ Rosalia Schneider ⋅ Josh Abramson ⋅ Alden Hung ⋅ Arun Ahuja ⋅ Stephen Clark ⋅ Greg Wayne ⋅ Feilx Hill
The continuous categorical: a novel simplex-valued exponential family
Elliott Gordon-Rodriguez ⋅ Gabriel Loaiza-Ganem ⋅ John Cunningham
Efficiently sampling functions from Gaussian process posteriors
James Wilson ⋅ Slava Borovitskiy ⋅ Alexander Terenin ⋅ Peter Mostowsky ⋅ Marc Deisenroth
Forecasting Sequential Data Using Consistent Koopman Autoencoders
Omri Azencot ⋅ N. Benjamin Erichson ⋅ Vanessa Lin ⋅ Michael Mahoney
From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models
Aytunc Sahin ⋅ Yatao Bian ⋅ Joachim Buhmann ⋅ Andreas Krause
Linear bandits with Stochastic Delayed Feedback
Claire Vernade ⋅ Alexandra Carpentier ⋅ Tor Lattimore ⋅ Giovanni Zappella ⋅ Beyza Ermis ⋅ Michael Brueckner
Multi-Agent Determinantal Q-Learning
Yaodong Yang ⋅ Ying Wen ⋅ Jun Wang ⋅ Liheng Chen ⋅ Kun Shao ⋅ David Mguni ⋅ Weinan Zhang
Parallel Algorithm for Non-Monotone DR-Submodular Maximization
Alina Ene ⋅ Huy Nguyen
Regularized Optimal Transport is Ground Cost Adversarial
François-Pierre Paty ⋅ Marco Cuturi
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics
Gregor Simm ⋅ Robert Pinsler ⋅ Jose Miguel Hernandez-Lobato
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift
Alexander Chan ⋅ Ahmed Alaa ⋅ Zhaozhi Qian ⋅ Mihaela van der Schaar
Variational Autoencoders with Riemannian Brownian Motion Priors
Dimitris Kalatzis ⋅ David Eklund ⋅ Georgios Arvanitidis ⋅ Søren Hauberg
Spectral Subsampling MCMC for Stationary Time Series
Robert Salomone ⋅ Matias Quiroz ⋅ Robert kohn ⋅ Mattias Villani ⋅ Minh-Ngoc Tran
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Poster
1:00 PM - 1:45 PM
32 Events in this session
Deep Streaming Label Learning
Zhen Wang ⋅ Liu Liu ⋅ Dacheng Tao
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness
Aounon Kumar ⋅ Alexander Levine ⋅ Tom Goldstein ⋅ Soheil Feizi
Learning Factorized Weight Matrix for Joint Filtering
Xiangyu Xu ⋅ Yongrui Ma ⋅ Wenxiu Sun
Neural Network Control Policy Verification With Persistent Adversarial Perturbation
Yuh-Shyang Wang ⋅ Tsui-Wei Weng ⋅ Luca Daniel
Off-Policy Actor-Critic with Shared Experience Replay
Simon Schmitt ⋅ Matteo Hessel ⋅ Karen Simonyan
Randomization matters How to defend against strong adversarial attacks
Rafael Pinot ⋅ Raphael Ettedgui ⋅ Geovani Rizk ⋅ Yann Chevaleyre ⋅ Jamal Atif
Projection-free Distributed Online Convex Optimization with $O(\sqrt{T})$ Communication Complexity
Yuanyu Wan ⋅ Wei-Wei Tu ⋅ Lijun Zhang
Representation Learning via Adversarially-Contrastive Optimal Transport
Anoop Cherian ⋅ Shuchin Aeron
A Graph to Graphs Framework for Retrosynthesis Prediction
Chence Shi ⋅ Minkai Xu ⋅ Hongyu Guo ⋅ Ming Zhang ⋅ Jian Tang
A Tree-Structured Decoder for Image-to-Markup Generation
Jianshu Zhang ⋅ Jun Du ⋅ Yongxin Yang ⋅ Yi-Zhe Song ⋅ Si Wei ⋅ Lirong Dai
Cost-effectively Identifying Causal Effects When Only Response Variable is Observable
Tian-Zuo Wang ⋅ Xi-Zhu Wu ⋅ Sheng-Jun Huang ⋅ Zhi-Hua Zhou
Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making
Chengchun Shi ⋅ Runzhe Wan ⋅ Rui Song ⋅ Wenbin Lu ⋅ Ling Leng
Do We Need Zero Training Loss After Achieving Zero Training Error?
Takashi Ishida ⋅ Ikko Yamane ⋅ Tomoya Sakai ⋅ Gang Niu ⋅ Masashi Sugiyama
DropNet: Reducing Neural Network Complexity via Iterative Pruning
Chong Min John Tan ⋅ Mehul Motani
Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks
Yonggang Zhang ⋅ Ya Li ⋅ Tongliang Liu ⋅ Xinmei Tian
Learning Autoencoders with Relational Regularization
Hongteng Xu ⋅ Dixin Luo ⋅ Ricardo Henao ⋅ Svati Shah ⋅ Lawrence Carin
Minimax Weight and Q-Function Learning for Off-Policy Evaluation
Masatoshi Uehara ⋅ Jiawei Huang ⋅ Nan Jiang
More Information Supervised Probabilistic Deep Face Embedding Learning
Ying Huang ⋅ Shangfeng Qiu ⋅ Wenwei Zhang ⋅ Xianghui Luo ⋅ Jinzhuo Wang
Multinomial Logit Bandit with Low Switching Cost
Kefan Dong ⋅ Yingkai Li ⋅ Qin Zhang ⋅ Yuan Zhou
Multi-objective Bayesian Optimization using Pareto-frontier Entropy
Shinya Suzuki ⋅ Shion Takeno ⋅ Tomoyuki Tamura ⋅ Kazuki Shitara ⋅ Masayuki Karasuyama
Non-autoregressive Machine Translation with Disentangled Context Transformer
Jungo Kasai ⋅ James Cross ⋅ Marjan Ghazvininejad ⋅ Jiatao Gu
On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes
Naoto Ohsaka ⋅ Tatsuya Matsuoka
On the Power of Compressed Sensing with Generative Models
Akshay Kamath ⋅ Eric Price ⋅ Sushrut Karmalkar
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup
Jang-Hyun Kim ⋅ Wonho Choo ⋅ Hyun Oh Song
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors
Zhaoqiang Liu ⋅ Selwyn Gomes ⋅ Avtansh Tiwari ⋅ Jonathan Scarlett
Self-Attentive Associative Memory
Hung Le ⋅ Truyen Tran ⋅ Svetha Venkatesh
Sparse Subspace Clustering with Entropy-Norm
Liang Bai ⋅ Jiye Liang
Transformer Hawkes Process
Simiao Zuo ⋅ Haoming Jiang ⋅ Zichong Li ⋅ Tuo Zhao ⋅ Hongyuan Zha
Few-shot Domain Adaptation by Causal Mechanism Transfer
Takeshi Teshima ⋅ Issei Sato ⋅ Masashi Sugiyama
Learning Efficient Multi-agent Communication: An Information Bottleneck Approach
Rundong Wang ⋅ Xu He ⋅ Runsheng Yu ⋅ Wei Qiu ⋅ Bo An ⋅ Zinovi Rabinovich
On Layer Normalization in the Transformer Architecture
Ruibin Xiong ⋅ Yunchang Yang ⋅ Di He ⋅ Kai Zheng ⋅ Shuxin Zheng ⋅ Chen Xing ⋅ Huishuai Zhang ⋅ Yanyan Lan ⋅ Liwei Wang ⋅ Tie-Yan Liu
Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training
Xuxi Chen ⋅ Wuyang Chen ⋅ Tianlong Chen ⋅ Ye Yuan ⋅ Chen Gong ⋅ Kewei Chen ⋅ Zhangyang “Atlas” Wang
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