45 Results

Poster
Tue 7:00 Collaborative Machine Learning with Incentive-Aware Model Rewards
Rachael Hwee Ling Sim, Yehong Zhang, Mun Choon Chan, Bryan Kian Hsiang Low
Poster
Tue 7:00 Fair Learning with Private Demographic Data
Hussein Mozannar, Mesrob Ohannessian, Nati Srebro
Poster
Tue 7:00 Zeno++: Robust Fully Asynchronous SGD
Cong Xie, Sanmi Koyejo, Indranil Gupta
Poster
Tue 7:00 Confidence-Aware Learning for Deep Neural Networks
Jooyoung Moon, Jihyo Kim, Younghak Shin, Sangheum Hwang
Poster
Tue 8:00 NADS: Neural Architecture Distribution Search for Uncertainty Awareness
Randy Ardywibowo, Shahin Boluki, Xinyu Gong, Zhangyang Wang, Xiaoning Qian
Poster
Tue 8:00 Detecting Out-of-Distribution Examples with Gram Matrices
Chandramouli Shama Sastry, Sageev Oore
Poster
Tue 8:00 Adversarial Neural Pruning with Latent Vulnerability Suppression
Divyam Madaan, Jinwoo Shin, Sung Ju Hwang
Poster
Tue 8:00 Feature Noise Induces Loss Discrepancy Across Groups
Fereshte Khani, Percy Liang
Poster
Tue 8:00 FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis
Aman Sinha, Matthew O'Kelly, Hongrui Zheng, Rahul Mangharam, John Duchi, Russ Tedrake
Poster
Tue 9:00 Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences
Daniel Brown, Russell Coleman, Ravi Srinivasan, Scott Niekum
Poster
Tue 9:00 Two Simple Ways to Learn Individual Fairness Metrics from Data
Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun
Poster
Tue 10:00 Responsive Safety in Reinforcement Learning by PID Lagrangian Methods
Adam Stooke, Joshua Achiam, Pieter Abbeel
Poster
Tue 13:00 Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks
David Stutz, Matthias Hein, Bernt Schiele
Poster
Wed 5:00 Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics
Debjani Saha, Candice Schumann, Duncan McElfresh, John P Dickerson, Michelle Mazurek, Michael Tschantz
Poster
Wed 5:00 Data preprocessing to mitigate bias: A maximum entropy based approach
L. Elisa Celis, Vijay Keswani, Nisheeth K. Vishnoi
Poster
Wed 5:00 Fair k-Centers via Maximum Matching
Matthew Jones, Huy Nguyen, Thy Nguyen
Poster
Wed 5:00 Causal Modeling for Fairness In Dynamical Systems
Elliot Creager, David Madras, Toniann Pitassi, Richard Zemel
Poster
Wed 8:00 Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings
Jesse Zhang, Brian Cheung, Chelsea Finn, Sergey Levine, Dinesh Jayaraman
Poster
Wed 8:00 Circuit-Based Intrinsic Methods to Detect Overfitting
Satrajit Chatterjee, Alan Mishchenko
Poster
Wed 9:00 Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning
Esther Rolf, Max Simchowitz, Sarah Dean, Lydia T. Liu, Daniel Bjorkegren, Moritz Hardt, Joshua Blumenstock
Poster
Wed 9:00 Accountable Off-Policy Evaluation With Kernel Bellman Statistics
Yihao Feng, Tongzheng Ren, Ziyang Tang, Qiang Liu
Poster
Wed 9:00 SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
Lingkai Kong, Jimeng Sun, Chao Zhang
Poster
Wed 9:00 Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints
Cong Shen, Zhiyang Wang, Sofia Villar, Mihaela van der Schaar
Poster
Wed 9:00 Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning
Jize Zhang, Bhavya Kailkhura, T. Yong-Jin Han
Poster
Wed 10:00 How to Solve Fair k-Center in Massive Data Models
Ashish Chiplunkar, Sagar Kale, Sivaramakrishnan Natarajan Ramamoorthy
Poster
Wed 10:00 Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Poster
Wed 11:00 DeBayes: a Bayesian Method for Debiasing Network Embeddings
Maarten Buyl, Tijl De Bie
Poster
Wed 11:00 Multidimensional Shape Constraints
Maya Gupta, Erez Louidor, Oleksandr Mangylov, Nobu Morioka, Taman Narayan, Sen Zhao
Poster
Wed 12:00 Too Relaxed to Be Fair
Michael Lohaus, Michaƫl Perrot, Ulrike von Luxburg
Poster
Wed 16:00 Safe Reinforcement Learning in Constrained Markov Decision Processes
Akifumi Wachi, Yanan Sui
Poster
Wed 16:00 On Lp-norm Robustness of Ensemble Decision Stumps and Trees
Yihan Wang, Huan Zhang, Hongge Chen, Duane Boning, Cho-Jui Hsieh
Poster
Thu 6:00 FACT: A Diagnostic for Group Fairness Trade-offs
Joon Kim, Jiahao Chen, Ameet Talwalkar
Poster
Thu 6:00 Constrained Markov Decision Processes via Backward Value Functions
Harsh Satija, Philip Amortila, Joelle Pineau
Poster
Thu 6:00 FR-Train: A Mutual Information-Based Approach to Fair and Robust Training
Yuji Roh, Kangwook Lee, Steven Whang, Changho Suh
Poster
Thu 7:00 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
Poster
Thu 8:00 Minimax Pareto Fairness: A Multi Objective Perspective
Natalia Martinez Gil, Martin Bertran, Guillermo Sapiro
Poster
Thu 8:00 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
Poster
Thu 9:00 Bounding the fairness and accuracy of classifiers from population statistics
Sivan Sabato, Elad Yom-Tov
Poster
Thu 15:00 Neural Network Control Policy Verification With Persistent Adversarial Perturbation
Yuh-Shyang Wang, Lily Weng, Luca Daniel
Workshop
Fri 5:00 Workshop on AI for Autonomous Driving (AIAD)
Wei-Lun (Harry) Chao, Rowan McAllister, Adrien Gaidon, Li Erran Li, Sven Kreiss
Workshop
Fri 6:30 Theoretical Foundations of Reinforcement Learning
Emma Brunskill, Thodoris Lykouris, Max Simchowitz, Wen Sun, Mengdi Wang
Workshop
Fri 9:00 Poster Session (click to see links)
Workshop
Fri 10:40 Conservative Exploration in Bandits and Reinforcement Learning
Mohammad Ghavamzadeh
Workshop
Sat 4:10 Invited talk 2: Detecting Distribution Shift with Deep Generative Models
Eric Nalisnick
Workshop
Sat 8:00 Incentives in Machine Learning
Boi Faltings, Yang Liu, David Parkes, Goran Radanovic, Dawn Song