David Pennock

2 accepted papers

2025

Stochastically Dominant Peer Prediction

Yichi Zhang, Shengwei Xu, Grant Schoenebeck, David Pennock

NeurIPS 2025poster

Eliciting reliable human feedback is essential for many machine learning tasks, such as learning from noisy labels and aligning AI systems with human preferences. Peer prediction mechanisms incentivize truthful reporting without ground truth verification by scoring agents based on correlations with…

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2020

No-Regret and Incentive-Compatible Online Learning

Rupert Freeman, David Pennock, Chara Podimata, Jennifer Wortman Vaughan

ICML 2020poster

We study online learning settings in which experts act strategically to maximize their influence on the learning algorithm’s predictions by potentially misreporting their beliefs about a sequence of binary events. Our goal is twofold. First, we want the learning algorithm to be no-regret with respec…

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