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Tinashe Handina
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[i5]Tinashe Handina, Tongxin Li, Kishan Panaganti, Eric Mazumdar, Adam Wierman:
Leveraging Machine-Learned Advice in Strategic Interactions with No-Regret Learners. CoRR abs/2606.10261 (2026)- 2025
[c5]Tinashe Handina, Kishan Panaganti, Eric Mazumdar, Adam Wierman:
Plan for the Worst with Advice: Advice-augmented Robust Markov Decision Processes. CDC 2025: 4182-4187- 2024
[c4]Tinashe Handina, Eric Mazumdar:
Understanding Model Selection for Learning in Strategic Environments. NeurIPS 2024
[c3]Tongxin Li, Tinashe Handina, Shaolei Ren, Adam Wierman:
Safe Exploitative Play with Untrusted Type Beliefs. NeurIPS 2024
[i4]Tinashe Handina, Eric Mazumdar:
Rethinking Scaling Laws for Learning in Strategic Environments. CoRR abs/2402.07588 (2024)
[i3]Tongxin Li, Tinashe Handina, Shaolei Ren, Adam Wierman:
Safe Exploitative Play with Untrusted Type Beliefs. CoRR abs/2411.07679 (2024)- 2022
[c2]Nicolas Christianson, Tinashe Handina, Adam Wierman:
Chasing Convex Bodies and Functions with Black-Box Advice. COLT 2022: 867-908
[c1]Vikash Sehwag, Saeed Mahloujifar, Tinashe Handina, Sihui Dai, Chong Xiang, Mung Chiang, Prateek Mittal:
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness? ICLR 2022
[i2]Nicolas Christianson, Tinashe Handina, Adam Wierman:
Chasing Convex Bodies and Functions with Black-Box Advice. CoRR abs/2206.11780 (2022)- 2021
[i1]Vikash Sehwag, Saeed Mahloujifar, Tinashe Handina, Sihui Dai, Chong Xiang, Mung Chiang, Prateek Mittal:
Improving Adversarial Robustness Using Proxy Distributions. CoRR abs/2104.09425 (2021)
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