8 citations · 14 across the 5 of their papers we have counts for
6 papers
Strategic Decision Focused Learning
Tinashe Handina, Yuehan Diao, Adam Wierman +1
Machine learning (ML) predictions are increasingly being used to guide decision-making, giving rise to the problem of decision-focused learning (DFL) where predictors are optimized…
Leveraging Machine-Learned Advice in Strategic Interactions with No-Regret Learners
Tinashe Handina, Tongxin Li, Kishan Panaganti +2
We study how an agent in a two-player repeated game can effectively utilize potentially imperfect advice when interacting with a no-regret learner. We characterize the advice lands…
Safe Exploitative Play with Untrusted Type Beliefs
Tongxin Li, Tinashe Handina, Shaolei Ren +1
The combination of the Bayesian game and learning has a rich history, with the idea of controlling a single agent in a system composed of multiple agents with unknown behaviors giv…
Understanding Model Selection For Learning In Strategic Environments
Tinashe Handina, Eric Mazumdar
The deployment of ever-larger machine learning models reflects a growing consensus that the more expressive the model class one optimizes over$\unicode{x2013}$and the more data one…
Chasing Convex Bodies and Functions with Black-Box Advice
Nicolas Christianson, Tinashe Handina, Adam Wierman
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 i…
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?
Vikash Sehwag, Saeed Mahloujifar, Tinashe Handina +4
While additional training data improves the robustness of deep neural networks against adversarial examples, it presents the challenge of curating a large number of specific real-w…