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Adversarial Learning

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Papers

Adversarial Intrinsic Motivation for Reinforcement Learning NIPS 2021
Consistent Non-Parametric Methods for Maximizing Robustness NIPS 2021
Adversarial Examples in Multi-Layer Random ReLU Networks NIPS 2021
Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes NIPS 2021
A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer Attacks NIPS 2021
Calibration and Consistency of Adversarial Surrogate Losses NIPS 2021
The Many Faces of Adversarial Risk NIPS 2021
A single gradient step finds adversarial examples on random two-layers neural networks NIPS 2021
Adversarial Feature Desensitization NIPS 2021
A Separation Result Between Data-oblivious and Data-aware Poisoning Attacks NIPS 2021
Counterbalancing Learning and Strategic Incentives in Allocation Markets NIPS 2021
Towards Efficient and Effective Adversarial Training NIPS 2021
Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data NIPS 2021
Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks NIPS 2021
Adversarial Robustness without Adversarial Training: A Teacher-Guided Curriculum Learning Approach NIPS 2021
Prior-independent Dynamic Auctions for a Value-maximizing Buyer NIPS 2021
Learning Transferable Adversarial Perturbations NIPS 2021
Improving Calibration through the Relationship with Adversarial Robustness NIPS 2021
A PAC-Bayes Analysis of Adversarial Robustness NIPS 2021
What Matters for Adversarial Imitation Learning? NIPS 2021
Adversarial Reweighting for Partial Domain Adaptation NIPS 2021
Anti-Backdoor Learning: Training Clean Models on Poisoned Data NIPS 2021
Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks NIPS 2021
CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks NIPS 2021
Who Leads and Who Follows in Strategic Classification? NIPS 2021