Improving Adversarial Robustness Using Proxy Distributions. - Vikash Sehwag; Saeed Mahloujifar; Tinashe Handina; Sihui Dai; Chong Xiang 001; Mung Chiang; Prateek Mittal (CoRR, 2021)

Chasing Convex Bodies and Functions with Black-Box Advice. - Nicolas Christianson; Tinashe Handina; Adam Wierman (COLT, 2022)

Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness? - Vikash Sehwag; Saeed Mahloujifar; Tinashe Handina; Sihui Dai; Chong Xiang 001; Mung Chiang; Prateek Mittal (ICLR, 2022)

Understanding Model Selection for Learning in Strategic Environments. - Tinashe Handina; Eric Mazumdar (NeurIPS, 2024)

Safe Exploitative Play with Untrusted Type Beliefs. - Tongxin Li; Tinashe Handina; Shaolei Ren; Adam Wierman (NeurIPS, 2024)

Rethinking Scaling Laws for Learning in Strategic Environments. - Tinashe Handina; Eric Mazumdar (CoRR, 2024)