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来自 | 知乎

作者 | GeorgeLee

地址 | https://zhuanlan.zhihu.com/p/145624170

编辑 | 机器学习算法与自然语言处理

本文仅作学术分享，若侵权，请联系后台删文处理

本文的主要目的是整理一下最新放榜的ICML2020中接收的对抗学习相关论文。毕竟之前伸手党做太久了，这次伸手失败还是自己整理共享一下吧，hhhh。如有遗漏和误解，欢迎在评论区指正。

对抗攻击

1. Adversarial Attacks on Probabilistic Autoregressive Forecasting Models

2. Stronger and Faster Wasserstein Adversarial Attacks

3. Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning

4. Adversarial Attacks on Copyright Detection Systems

5. Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack

6. Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

7. Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks

8. Nonlinear Gradient Estimation for Query Efficient Blackbox Attack

对抗防御

1. Adversarial Robustness via Runtime Masking and Cleansing

2. Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability

3. Adversarial Neural Pruning with Latent Vulnerability Suppression

4. Hierarchical Verification for Adversarial Robustness

5. Randomization matters How to defend against strong adversarial attacks

6. Margin-aware Adversarial Domain Adaptation with Optimal Transport (没看到文章不确定)

7. Second-Order Provable Defenses against Adversarial Attacks

8. Adversarial Risk via Optimal Transport and Optimal Couplings

9. Optimal Statistical Guaratees for Adversarially Robust Gaussian Classification

10. Scalable Differential Privacy with Certified Robustness in Adversarial Learning

11. Adversarial Robustness Against the Union of Multiple Threat Models

12. Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks

13. Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization

14. Black-box Certification and Learning under Adversarial Perturbations

相关机理的理解及与其他领域的结合

1. Feature-map-level Online Adversarial Knowledge Distillation

2. Attacks Which Do Not Kill Training Make Adversarial Learning Stronger

3. Towards Understanding the Regularization of Adversarial Robustness on Neural Networks

4. More Data Can Expand The Generalization Gap Between Adversarially Robust and Standard Models

5. Interpreting Robust Optimization via Adversarial Influence Functions

6. Proper Network Interpretability Helps Adversarial Robustness in Classification

7. DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training

8. Overfitting in adversarially robust deep learning

9. Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks

10. Adversarial Robustness for Code

11. Rank Aggregation from Pairwise Comparisons in the Presence of Adversarial Corruptions (没看到文章不确定)

12. Concise Explanations of Neural Networks using Adversarial Training

13. Logarithmic Regret for Online Control with Adversarial Noise (没看到文章不确定)

14. Adversarial Filters of Dataset Biases

15. Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations

16. Neural Network Control Policy Verification With Persistent Adversarial Perturbation

17. Efficiently Learning Adversarially Robust Halfspaces with Noise

18. Representation Learning via Adversarially-Contrastive Optimal Transport (没看到文章不确定)

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