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Adversarial Learning

4,859 papers

Papers per year

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Papers

CARTL: Cooperative Adversarially-Robust Transfer Learning ICML 2021
SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation ICML 2021
Label-Only Membership Inference Attacks ICML 2021
Generalised Lipschitz Regularisation Equals Distributional Robustness ICML 2021
Mind the Box: $l_1$-APGD for Sparse Adversarial Attacks on Image Classifiers ICML 2021
Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning ICML 2021
Adversarial Robustness Guarantees for Random Deep Neural Networks ICML 2021
Learning Diverse-Structured Networks for Adversarial Robustness ICML 2021
Weight-covariance alignment for adversarially robust neural networks ICML 2021
Train simultaneously, generalize better: Stability of gradient-based minimax learners ICML 2021
Double-Win Quant: Aggressively Winning Robustness of Quantized Deep Neural Networks via Random Precision Training and Inference ICML 2021
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks ICML 2021
Strategic Classification in the Dark ICML 2021
Query Complexity of Adversarial Attacks ICML 2021
Adversarial Policy Learning in Two-player Competitive Games ICML 2021
Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks ICML 2021
SPECTRE: defending against backdoor attacks using robust statistics ICML 2021
Robust Density Estimation from Batches: The Best Things in Life are (Nearly) Free ICML 2021
Markpainting: Adversarial Machine Learning meets Inpainting ICML 2021
Functional Space Analysis of Local GAN Convergence ICML 2021
Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech ICML 2021
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? ICML 2021
Strategic Classification Made Practical ICML 2021
Improved, Deterministic Smoothing for L_1 Certified Robustness ICML 2021
A Novel Method to Solve Neural Knapsack Problems ICML 2021