
Data Augmentation Can Improve Robustness
Adversarial training suffers from robust overfitting, a phenomenon where...
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Improving Robustness using Generated Data
Recent work argues that robust training requires substantially larger da...
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Defending Against Image Corruptions Through Adversarial Augmentations
Modern neural networks excel at image classification, yet they remain vu...
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Fixing Data Augmentation to Improve Adversarial Robustness
Adversarial training suffers from robust overfitting, a phenomenon where...
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Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification
Many realworld physical control systems are required to satisfy constra...
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Balancing Constraints and Rewards with MetaGradient D4PG
Deploying Reinforcement Learning (RL) agents to solve realworld applica...
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Uncovering the Limits of Adversarial Training against NormBounded Adversarial Examples
Adversarial training and its variants have become de facto standards for...
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NonStationary Bandits with Intermediate Observations
Online recommender systems often face long delays in receiving feedback,...
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Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations
Recent research has made the surprising finding that stateoftheart de...
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An Alternative Surrogate Loss for PGDbased Adversarial Testing
Adversarial testing methods based on Projected Gradient Descent (PGD) ar...
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Adaptive TemporalDifference Learning for Policy Evaluation with PerState Uncertainty Estimates
We consider the core reinforcementlearning problem of onpolicy value f...
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Robust Reinforcement Learning for Continuous Control with Model Misspecification
We provide a framework for incorporating robustness  to perturbations ...
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A Bayesian Approach to Robust Reinforcement Learning
Robust Markov Decision Processes (RMDPs) intend to ensure robustness wit...
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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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Temporal Difference Learning with Neural Networks  Study of the Leakage Propagation Problem
TemporalDifference learning (TD) [Sutton, 1988] with function approxima...
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A Dual Approach to Scalable Verification of Deep Networks
This paper addresses the problem of formally verifying desirable propert...
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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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Offpolicy evaluation for MDPs with unknown structure
Offpolicy learning in dynamic decision problems is essential for provid...
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Timothy Mann
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