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02/14/2022
MuZero with Self-competition for Rate Control in VP9 Video Compression
Video streaming usage has seen a significant rise as entertainment, educ...
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11/09/2021
Data Augmentation Can Improve Robustness
Adversarial training suffers from robust overfitting, a phenomenon where...
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10/18/2021
Improving Robustness using Generated Data
Recent work argues that robust training requires substantially larger da...
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04/02/2021
Defending Against Image Corruptions Through Adversarial Augmentations
Modern neural networks excel at image classification, yet they remain vu...
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03/02/2021
Fixing Data Augmentation to Improve Adversarial Robustness
Adversarial training suffers from robust overfitting, a phenomenon where...
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10/20/2020
Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification
Many real-world physical control systems are required to satisfy constra...
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10/13/2020
Balancing Constraints and Rewards with Meta-Gradient D4PG
Deploying Reinforcement Learning (RL) agents to solve real-world applica...
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10/07/2020
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples
Adversarial training and its variants have become de facto standards for...
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06/03/2020
Non-Stationary Bandits with Intermediate Observations
Online recommender systems often face long delays in receiving feedback,...
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12/06/2019
Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations
Recent research has made the surprising finding that state-of-the-art de...
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10/21/2019
An Alternative Surrogate Loss for PGD-based Adversarial Testing
Adversarial testing methods based on Projected Gradient Descent (PGD) ar...
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06/19/2019
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates
We consider the core reinforcement-learning problem of on-policy value f...
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06/18/2019
Robust Reinforcement Learning for Continuous Control with Model Misspecification
We provide a framework for incorporating robustness -- to perturbations ...
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05/20/2019
A Bayesian Approach to Robust Reinforcement Learning
Robust Markov Decision Processes (RMDPs) intend to ensure robustness wit...
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10/30/2018
On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models
Recent works have shown that it is possible to train models that are ver...
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07/09/2018
Temporal Difference Learning with Neural Networks - Study of the Leakage Propagation Problem
Temporal-Difference learning (TD) [Sutton, 1988] with function approxima...
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03/17/2018
A Dual Approach to Scalable Verification of Deep Networks
This paper addresses the problem of formally verifying desirable propert...
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12/24/2015
Deep Reinforcement Learning in Large Discrete Action Spaces
Being able to reason in an environment with a large number of discrete a...
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02/11/2015
