perturbation analysis

Use perturbations when learning from explanations NeurIPS 2023 Bounded-Regret MPC via Perturbation Analysis: Prediction Error, Constraints, and Nonlinearity NeurIPS 2022 On the Effectiveness of Iterative Learning Control L4DC 2022 Robustness Analysis of Video-Language Models Against Visual and Language Perturbations NeurIPS 2022 A Note on Sparse Generalized Eigenvalue Problem NeurIPS 2021 Explaining Time Series Predictions with Dynamic Masks ICML 2021 Information-Theoretic Generalization Bounds for Stochastic Gradient Descent COLT 2021 Perturbation CheckLists for Evaluating NLG Evaluation Metrics EMNLP 2021 Regularization in ResNet with Stochastic Depth NeurIPS 2021 The Sparse Vector Technique, Revisited COLT 2021 Automatically Identifying Gender Issues in Machine Translation using Perturbations EMNLP 2020 Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond NeurIPS 2020 The Sensitivity of Language Models and Humans to Winograd Schema Perturbations ACL 2020 What do we expect from Multiple-choice QA Systems? EMNLP 2020 What-if I ask you to explain: Explaining the effects of perturbations in procedural text EMNLP 2020 Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study ACL 2019 Generalized No Free Lunch Theorem for Adversarial Robustness ICML 2019 Interpretable Question Answering on Knowledge Bases and Text ACL 2019 On the (In)fidelity and Sensitivity of Explanations NeurIPS 2019 Perturbation Analysis of the 8-Point Algorithm: A Case Study for Wide FoV Cameras CVPR 2019 Sensitivity Analysis of Deep Neural Networks AAAI 2019 Understanding Deep Networks via Extremal Perturbations and Smooth Masks ICCV 2019 A causal framework for explaining the predictions of black-box sequence-to-sequence models EMNLP 2017 Clustering Stable Instances of Euclidean k-means. NeurIPS 2017 Online and Differentially-Private Tensor Decomposition NeurIPS 2016