AISTATS · 2026
Leveraging Machine-Learned Advice in Strategic Interactions with No-Regret Learners
Proceedings of the International Conference on Artificial Intelligence and Statistics
How useful is imperfect advice against an adaptive opponent?
A measure of advice quality connects simulators and payoff predictions to strategic performance. The paper establishes benefits of reliable advice for approximate Stackelberg play and limitations on simultaneously exploiting accurate advice and protecting against inaccurate advice.
Research themes
Connections
Prediction errors become strategic risks
In strategic environments, advice quality affects both opportunity and risk. The Bayesian game and repeated game results expose limits on combining exploitation with protection against inaccurate beliefs.
Open questions
Accountable learning across agents
How should shared models balance efficiency, strategic risk, and equity?
Explore open directionsCite this paper
Tinashe Handina, Tongxin Li, Kishan Panaganti, Eric Mazumdar, Adam Wierman. Leveraging Machine-Learned Advice in Strategic Interactions with No-Regret Learners. Proceedings of the International Conference on Artificial Intelligence and Statistics, 2026. https://arxiv.org/abs/2606.10261
BibTeX
@inproceedings{tongxin-strategic-advice,
title = {{Leveraging Machine-Learned Advice in Strategic Interactions with No-Regret Learners}},
author = {Tinashe Handina and Tongxin Li and Kishan Panaganti and Eric Mazumdar and Adam Wierman},
year = {2026},
booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics},
url = {https://arxiv.org/abs/2606.10261}
}
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