Publications

2026

1. Leveraging Machine-Learned Advice in Strategic Interactions with No-Regret Learners

   Tinashe Handina, Tongxin Li , Kishan Panaganti , and 2 more authors

   In 29th International Conference on Artificial Intelligence and Statistics (AISTATS 2026) , 2026

2025

1. Plan for the Worst with Advice: Advice-Augmented Robust Markov Decision Processes

   Tinashe Handina, Kishan Panaganti , Eric Mazumdar , and 1 more author

   In 64th IEEE Conference on Decision and Control (CDC 2025) , 2025

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2024

1. Understanding Model Selection For Learning In Strategic Environments

   Tinashe Handina, and Eric Mazumdar

   In 38th Conference on Neural Information Processing Systems (NeurIPS 2024) , 2024

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2. Safe Exploitative Play with Untrusted Type Beliefs

   Tongxin Li , Tinashe Handina, Shaolei Ren , and 1 more author

   In 38th Conference on Neural Information Processing Systems (NeurIPS 2024) , 2024

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2022

1. Chasing Convex Bodies and Functions with Black-Box Advice

   Nicolas Christianson , Tinashe Handina, and Adam Wierman

   In Proceedings of Thirty Fifth Conference on Learning Theory , 2022

   Abs PDF

   We consider the problem of convex function chasing with black-box advice, where an online decision-maker aims to minimize the total cost of making and switching between decisions in a normed vector space, aided by black-box advice such as the decisions of a machine-learned algorithm. The decision-maker seeks cost comparable to the advice when it performs well, known as \emphconsistency, while also ensuring worst-case \emphrobustness even when the advice is adversarial. We first consider the common paradigm of algorithms that switch between the decisions of the advice and a competitive algorithm, showing that no algorithm in this class can improve upon 3-consistency while staying robust. We then propose two novel algorithms that bypass this limitation by exploiting the problem’s convexity. The first, \textscInterp, achieves (\sqrt2+ε)-consistency and \mathcalO(\fracCε^2)-robustness for any ε> 0, where C is the competitive ratio of an algorithm for convex function chasing or a subclass thereof. The second, \textscBdInterp, achieves (1+ε)-consistency and \mathcalO(\fracCDε)-robustness when the problem has bounded diameter D. Further, we show that \textscBdInterp achieves near-optimal consistency-robustness trade-off for the special case where cost functions are α-polyhedral.

2021

1. Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?

   Vikash Sehwag , Saeed Mahloujifar , Tinashe Handina, and 4 more authors

   In International Conference on Learning Representations , 2021
2. A Random Walk in Extensive Form Games: An Investigation into Information, Strategy-Proofness, and Credibility

   Tinashe Handina

   2021