Robustness

Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual EMNLP 2019 Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty NeurIPS 2019 Verifying Robustness of Gradient Boosted Models AAAI 2019 What if We Simply Swap the Two Text Fragments? A Straightforward yet Effective Way to Test the Robustness of Methods to Confounding Signals in Nature Language Inference Tasks AAAI 2019 Adversarially Robust Generalization Requires More Data NeurIPS 2018 Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution NeurIPS 2018 Deep Defense: Training DNNs with Improved Adversarial Robustness NeurIPS 2018 Defense Against Universal Adversarial Perturbations CVPR 2018 Dual Principal Component Pursuit: Improved Analysis and Efficient Algorithms NeurIPS 2018 Improving Occlusion and Hard Negative Handling for Single-Stage Pedestrian Detectors CVPR 2018 Robust Hypothesis Testing Using Wasserstein Uncertainty Sets NeurIPS 2018 Thwarting Adversarial Examples: An L_0-Robust Sparse Fourier Transform NeurIPS 2018 Towards Robust Detection of Adversarial Examples NeurIPS 2018 Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data ACL 2018 Wasserstein Distributionally Robust Kalman Filtering NeurIPS 2018 Certified Defenses for Data Poisoning Attacks NeurIPS 2017 Consistent Robust Regression NeurIPS 2017 Exploring Generalization in Deep Learning NeurIPS 2017 Lower bounds on the robustness to adversarial perturbations NeurIPS 2017 Robust Adversarial Reinforcement Learning ICML 2017 Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks NeurIPS 2017 Improving the Robustness of Deep Neural Networks via Stability Training CVPR 2016 Pull the Plug? Predicting If Computers or Humans Should Segment Images CVPR 2016 Safe Policy Improvement by Minimizing Robust Baseline Regret NeurIPS 2016 Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences NeurIPS 2016