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Tinashe Handina
Tinashe Handina
California Institute of Technology
Verified email at caltech.edu
Title
Cited by
Cited by
Year
Robust learning meets generative models: Can proxy distributions improve adversarial robustness?
V Sehwag, S Mahloujifar, T Handina, S Dai, C Xiang, M Chiang, P Mittal
arXiv preprint arXiv:2104.09425, 2021
1142021
Improving adversarial robustness using proxy distributions
V Sehwag, S Mahloujifar, T Handina, S Dai, C Xiang, M Chiang, P Mittal
arXiv preprint arXiv:2104.09425 1, 2021
312021
Chasing convex bodies and functions with black-box advice
N Christianson, T Handina, A Wierman
Conference on Learning Theory, 867-908, 2022
212022
Rethinking Scaling Laws for Learning in Strategic Environments
T Handina, E Mazumdar
arXiv preprint arXiv:2402.07588, 2024
2024
A Random walk in Extensive Form Games: An Investigation into information, strategy-proofness and Credibility
T Handina
2021
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Articles 1–5