Familywise Error Rate Control by Interactive Unmasking
Boyan Duan (Carnegie Mellon University); Aaditya Ramdas (Carnegie Mellon University); Larry Wasserman (Carnegie Mellon University)
Learning Theory, Tue Jul 14 07:00 AM — 07:45 AM & Tue Jul 14 06:00 PM — 06:45 PM (PDT)
Stochastic Regret Minimization in Extensive-Form Games
Gabriele Farina (Carnegie Mellon University); Christian Kroer (Columbia University); Tuomas Sandholm (CMU, Strategy Robot, Inc., Optimized Markets, Inc., Strategic Machine, Inc.)
Learning Theory, Tue Jul 14 07:00 AM — 07:45 AM & Tue Jul 14 06:00 PM — 06:45 PM (PDT)
Strategyproof Mean Estimation from Multiple-Choice Questions
Anson Kahng (Carnegie Mellon University); Gregory Kehne (Carnegie Mellon University); Ariel D Procaccia (Harvard University)
Learning Theory, Tue Jul 14 08:00 AM — 08:45 AM & Tue Jul 14 07:00 PM — 07:45 PM (PDT)
On Learning Language-Invariant Representations for Universal Machine Translation
Han Zhao (Carnegie Mellon University); Junjie Hu (Carnegie Mellon University); Andrej Risteski (CMU)
Learning Theory, Wed Jul 15 05:00 AM — 05:45 AM & Wed Jul 15 04:00 PM — 04:45 PM (PDT)
Class-Weighted Classification: Trade-offs and Robust Approaches
Ziyu Xu (Carnegie Mellon University); Chen Dan (Carnegie Mellon University); Justin Khim (Carnegie Mellon University); Pradeep Ravikumar (Carnegie Mellon University)
Learning Theory, Wed Jul 15 08:00 AM — 08:45 AM & Wed Jul 15 09:00 PM — 09:45 PM (PDT)
Sparsified Linear Programming for Zero-Sum Equilibrium Finding
Brian H Zhang (Carnegie Mellon University); Tuomas Sandholm (CMU, Strategy Robot, Inc., Optimized Markets, Inc., Strategic Machine, Inc.)
Learning Theory, Thu Jul 16 06:00 AM — 06:45 AM & Thu Jul 16 06:00 PM — 06:45 PM (PDT)
Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification
Chen Dan (Carnegie Mellon University); Yuting Wei (CMU); Pradeep Ravikumar (Carnegie Mellon University)
Learning Theory, Thu Jul 16 06:00 AM — 06:45 AM & Thu Jul 16 06:00 PM — 06:45 PM (PDT)
Uniform Convergence of Rank-weighted Learning
Justin Khim (Carnegie Mellon University); Liu Leqi (Carnegie Mellon University); Adarsh Prasad (Carnegie Mellon University); Pradeep Ravikumar (Carnegie Mellon University)
Learning Theory, Thu Jul 16 07:00 AM — 07:45 AM & Thu Jul 16 06:00 PM — 06:45 PM (PDT)
Online Control of the False Coverage Rate and False Sign Rate
Asaf Weinstein (The Hebrew University of Jerusalem); Aaditya Ramdas (Carnegie Mellon University)
Online Learning, Active Learning, and Bandits, Tue Jul 14 10:00 AM — 10:45 AM & Tue Jul 14 09:00 PM — 09:45 PM (PDT)
On conditional versus marginal bias in multi-armed bandits
Jaehyeok Shin (Carnegie Mellon University); Aaditya Ramdas (Carnegie Mellon University); Alessandro Rinaldo (Carnegie Mellon University)
Online Learning, Active Learning, and Bandits, Wed Jul 15 08:00 AM — 08:45 AM & Wed Jul 15 07:00 PM — 07:45 PM (PDT)
General Machine Learning
Influence Diagram Bandits: Variational Thompson Sampling for Structured Bandit Problems
Tong Yu (Carnegie Mellon University); Branislav Kveton (Google Research); Zheng Wen (DeepMind); Ruiyi Zhang (Duke University); Ole J. Mengshoel (Carnegie Mellon University)
Online Learning, Active Learning, and Bandits, Tue Jul 14 10:00 AM — 10:45 AM & Tue Jul 14 10:00 PM — 10:45 PM (PDT)
The Implicit Regularization of Stochastic Gradient Flow for Least Squares
Alnur Ali (Stanford University); Edgar Dobriban (University of Pennsylvania); Ryan Tibshirani (Carnegie Mellon University)
Supervised Learning, Thu Jul 16 09:00 AM — 09:45 AM & Thu Jul 16 08:00 PM — 08:45 PM (PDT)
Near Input Sparsity Time Kernel Embeddings via Adaptive Sampling
David Woodruff (CMU); Amir Zandieh (EPFL)
General Machine Learning Techniques, Tue Jul 14 02:00 PM — 02:45 PM & Wed Jul 15 03:00 AM — 03:45 AM (PDT)
InfoGAN-CR and Model Centrality: Self-supervised Model Training and Selection for Disentangling GANs [code]
Zinan Lin (Carnegie Mellon University); Kiran K Thekumparampil (University of Illinois at Urbana-Champaign); Giulia Fanti (CMU); Sewoong Oh (University of Washington)
Representation Learning, Wed Jul 15 08:00 AM — 08:45 AM & Wed Jul 15 08:00 PM — 08:45 PM (PDT)
LTF: A Label Transformation Framework for Correcting Label Shift
Jiaxian Guo (The University of Sydney); Mingming Gong (University of Melbourne); Tongliang Liu (The University of Sydney); Kun Zhang (Carnegie Mellon University); Dacheng Tao (The University of Sydney)
Transfer, Multitask and Meta-learning, Tue Jul 14 07:00 AM — 07:45 AM & Tue Jul 14 07:00 PM — 07:45 PM (PDT)
Optimizing Dynamic Structures with Bayesian Generative Search
Minh Hoang (Carnegie Mellon University); Carleton Kingsford (Carnegie Mellon University)
Transfer, Multitask and Meta-learning, Thu Jul 16 06:00 AM — 06:45 AM & Thu Jul 16 05:00 PM — 05:45 PM (PDT)
Label-Noise Robust Domain Adaptation
Xiyu Yu (Baidu Inc.); Tongliang Liu (The University of Sydney); Mingming Gong (University of Melbourne); Kun Zhang (Carnegie Mellon University); Kayhan Batmanghelich (University of Pittsburgh); Dacheng Tao (The University of Sydney)
Unsupervised and Semi-Supervised Learning, Wed Jul 15 05:00 AM — 05:45 AM & Wed Jul 15 07:00 PM — 07:45 PM (PDT)
Input-Sparsity Low Rank Approximation in Schatten Norm
Yi Li (Nanyang Technological University); David Woodruff (Carnegie Mellon University)
Unsupervised and Semi-Supervised Learning, Thu Jul 16 06:00 AM — 06:45 AM & Thu Jul 16 06:00 PM — 06:45 PM (PDT)
A Pairwise Fair and Community-preserving Approach to k-Center Clustering
Brian Brubach (University of Maryland); Darshan Chakrabarti (Carnegie Mellon University); John P Dickerson (University of Maryland); Samir Khuller (Northwestern University); Aravind Srinivasan (University of Maryland College Park); Leonidas Tsepenekas (University of Maryland, College Park)
Unsupervised and Semi-Supervised Learning, Thu Jul 16 06:00 AM — 06:45 AM & Thu Jul 16 05:00 PM — 05:45 PM (PDT)
Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates
Jeff Calder (University of Minnesota); Brendan Cook (University of Minnesota); Matthew Thorpe (University of Manchester); Dejan Slepcev (Carnegie Mellon University)
Unsupervised and Semi-Supervised Learning, Thu Jul 16 07:00 AM — 07:45 AM & Thu Jul 16 06:00 PM — 06:45 PM (PDT)
Trustworthy Machine Learning
Explaining Groups of Points in Low-Dimensional Representations [code]
Gregory Plumb CMU); Jonathan Terhorst (U-M LSA); Sriram Sankararaman (UCLA); Ameet Talwalkar (CMU)
Accountability, Transparency and Interpretability, Tue Jul 14 07:00 AM — 07:45 AM & Tue Jul 14 06:00 PM — 06:45 PM (PDT)
Overfitting in adversarially robust deep learning [code]
Leslie Rice (Carnegie Mellon University); Eric Wong (Carnegie Mellon University); Zico Kolter (Carnegie Mellon University)
Adversarial Examples, Tue Jul 14 08:00 AM — 08:45 AM & Tue Jul 14 07:00 PM — 07:45 PM (PDT)
Adversarial Robustness Against the Union of Multiple Perturbation Models
Pratyush Maini (IIT Delhi); Eric Wong (Carnegie Mellon University); Zico Kolter (Carnegie Mellon University)
Adversarial Examples, Wed Jul 15 09:00 AM — 09:45 AM & Wed Jul 15 09:00 PM — 09:45 PM (PDT)
Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs
AmirEmad Ghassami (UIUC); Alan Yang (University of Illinois at Urbana-Champaign); Negar Kiyavash (École Polytechnique Fédérale de Lausanne); Kun Zhang (Carnegie Mellon University)
Causality, Tue Jul 14 07:00 AM — 07:45 AM & Tue Jul 14 08:00 PM — 08:45 PM (PDT)
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing
Elan Rosenfeld (Carnegie Mellon University); Ezra Winston (Carnegie Mellon University); Pradeep Ravikumar (Carnegie Mellon University); Zico Kolter (Carnegie Mellon University)
Trustworthy Machine Learning, Tue Jul 14 09:00 AM — 09:45 AM & Tue Jul 14 08:00 PM — 08:45 PM (PDT)
FACT: A Diagnostic for Group Fairness Trade-offs
Joon Sik Kim (Carnegie Mellon University); Jiahao Chen (JPMorgan AI Research); Ameet Talwalkar (CMU)
Fairness, Equity, Justice, and Safety, Thu Jul 16 06:00 AM — 06:45 AM & Thu Jul 16 05:00 PM — 05:45 PM (PDT)
Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing
Sanghamitra Dutta (CMU); Dennis Wei (IBM Research); Hazar Yueksel (IBM Research); Pin-Yu Chen (IBM Research); Sijia Liu (IBM Research); Kush R Varshney (IBM Research)
Fairness, Equity, Justice, and Safety, Thu Jul 16 07:00 AM — 07:45 AM & Thu Jul 16 06:00 PM — 06:45 PM (PDT)
Deep Learning
Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction [code]
Filipe de Avila Belbute-Peres (Carnegie Mellon University); Thomas D. Economon (SU2 Foundation); Zico Kolter (Carnegie Mellon University)
Deep Learning – General, Wed Jul 15 05:00 AM — 05:45 AM & Wed Jul 15 04:00 PM — 04:45 PM (PDT)
Optimizing Data Usage via Differentiable Rewards
Xinyi Wang (Carnegie Mellon University); Hieu Pham (Carnegie Mellon University); Paul Michel (Carnegie Mellon University); Antonios Anastasopoulos (Carnegie Mellon University); Jaime Carbonell (Carnegie Mellon University); Graham Neubig (Carnegie Mellon University)
Deep Learning – Algorithms, Thu Jul 16 08:00 AM — 08:45 AM & Thu Jul 16 07:00 PM — 07:45 PM (PDT)
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning
Nikunj Saunshi (Princeton University); Yi Zhang (Princeton); Mikhail Khodak (Carnegie Mellon University); Sanjeev Arora (Princeton University)
Deep Learning – Theory, Tue Jul 14 10:00 AM — 10:45 AM & Tue Jul 14 09:00 PM — 09:45 PM (PDT)
Stabilizing Transformers for Reinforcement Learning
Emilio Parisotto (Carnegie Mellon University); Francis Song (DeepMind); Jack Rae (Deepmind); Razvan Pascanu (Google Deepmind); Caglar Gulcehre (DeepMind); Siddhant Jayakumar (DeepMind); Max Jaderberg (DeepMind); Raphaël Lopez Kaufman (DeepMind); Aidan Clark (DeepMind); Seb Noury (DeepMind); Matthew Botvinick (Google); Nicolas Heess (DeepMind); Raia Hadsell (Deepmind)
Reinforcement Learning – Deep RL, Wed Jul 15 05:00 AM — 05:45 AM & Wed Jul 15 04:00 PM — 04:45 PM (PDT)
Planning to Explore via Self-Supervised World Models [code]
Ramanan Sekar (University of Pennsylvania); Oleh Rybkin (University of Pennsylvania); Kostas Daniilidis (University of Pennsylvania); Pieter Abbeel (UC Berkeley); Danijar Hafner (Google); Deepak Pathak (CMU, FAIR)
Reinforcement Learning – Deep RL, Wed Jul 15 08:00 AM — 08:45 AM & Wed Jul 15 07:00 PM — 07:45 PM (PDT)
One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control [code]
Wenlong Huang (UC Berkeley); Igor Mordatch (Google); Deepak Pathak (CMU, FAIR)
Reinforcement Learning – Deep RL, Thu Jul 16 08:00 AM — 08:45 AM & Thu Jul 16 08:00 PM — 08:45 PM (PDT)
VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video Processing
“Zoltán Á Milacski (Eötvös Loránd University); Barnabas Poczos (Carnegie Mellon University); Andras Lorincz (Eötvös Loránd University)
Sequential, Network, and Time-Series Modeling, Wed Jul 15 02:00 PM — 02:45 PM & Thu Jul 16 01:00 AM — 01:45 AM (PDT)
Applications
An EM Approach to Non-autoregressive Conditional Sequence Generation
Zhiqing Sun (Carnegie Mellon University); Yiming Yang (Carnegie Mellon University)
Applications – Language, Speech and Dialog, Tue Jul 14 08:00 AM — 08:45 AM & Tue Jul 14 07:00 PM — 07:45 PM (PDT)
XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalisation [code]
Junjie Hu (Carnegie Mellon University); Sebastian Ruder (DeepMind); Aditya Siddhant (Google Research); Graham Neubig (Carnegie Mellon University); Orhan Firat (Google); Melvin Johnson (Google)
Applications – Language, Speech and Dialog, Tue Jul 14 10:00 AM — 10:45 AM & Tue Jul 14 09:00 PM — 09:45 PM (PDT)
Learning Factorized Weight Matrix for Joint Filtering
Xiangyu Xu (Carnegie Mellon University); Yongrui Ma (SenseTime); Wenxiu Sun (SenseTime Research)
Applications – Computer Vision, Thu Jul 16 03:00 PM — 03:45 PM & Fri Jul 17 04:00 AM — 04:45 AM (PDT)
Uncertainty-Aware Lookahead Factor Models for Quantitative Investing
Lakshay Chauhan (Euclidean Technologies); John Alberg (Euclidean Technologies LLC); Zachary Lipton (Carnegie Mellon University)
Applications – Other, Tue Jul 14 08:00 AM — 08:45 AM & Tue Jul 14 08:00 PM — 08:45 PM (PDT)
Learning Robot Skills with Temporal Variational Inference
Tanmay Shankar (Facebook AI Research); Abhinav Gupta (CMU/FAIR)
Applications – Other, Thu Jul 16 06:00 AM — 06:45 AM & Thu Jul 16 05:00 PM — 05:45 PM (PDT)
Optimization
Nearly Linear Row Sampling Algorithm for Quantile Regression
Yi Li (Nanyang Technological University); Ruosong Wang (Carnegie Mellon University); Lin Yang (UCLA); Hanrui Zhang (Duke University)
Optimization – Large Scale, Parallel and Distributed, Tue Jul 14 09:00 AM — 09:45 AM & Tue Jul 14 08:00 PM — 08:45 PM (PDT)
The Non-IID Data Quagmire of Decentralized Machine Learning
Kevin Hsieh (Microsoft Research); Amar Phanishayee (Microsoft Research); Onur Mutlu (ETH Zurich); Phillip B Gibbons (CMU)
Optimization – Large Scale, Parallel and Distributed, Wed Jul 15 08:00 AM — 08:45 AM & Wed Jul 15 09:00 PM — 09:45 PM (PDT)
Refined bounds for algorithm configuration: The knife-edge of dual class approximability
Maria-Florina Balcan (Carnegie Mellon University); Tuomas Sandholm (CMU, Strategy Robot, Inc., Optimized Markets, Inc., Strategic Machine, Inc.); Ellen Vitercik (Carnegie Mellon University)
Optimization – General, Thu Jul 16 06:00 AM — 06:45 AM & Thu Jul 16 06:00 PM — 06:45 PM (PDT)