2 citations · 2 across the 19 of their papers we have counts for
6 papers · 1 filter
Narrowing the Collaboration Gap, Probably
Mirah Shi, Marcel Hussing, Ira Globus-Harris +3
Large language models are increasingly deployed as teams of agents that hold different private information about a shared task. How should such agents concisely communicate in a wa…
Delegating Authorization to Misaligned Agents: Coalitional Alignment and Safe Control
Natalie Collina, Surbhi Goel, Aaron Roth +1
Long-running AI agents create a control problem: each action they take changes the state, which in turn affects the trajectory of future actions. If the agent is not fully aligned,…
The Sample Complexity of Multicalibration
Natalie Collina, Jiuyao Lu, Georgy Noarov +1
We study the minimax sample complexity of multicalibration in the batch setting. A learner observes i.i.d. samples from an unknown distribution and must output a (possibly rand…
Personalization Aids Pluralistic Alignment Under Competition
Natalie Collina, Surbhi Goel, Aaron Roth +1
Can competition among misaligned AI providers yield aligned outcomes for a diverse population of users, and what role does model personalization play? We study a setting where mult…
Recommending Best Paper Awards for ML/AI Conferences via the Isotonic Mechanism
Garrett G. Wen, Buxin Su, Natalie Collina +2
Machine learning and artificial intelligence conferences such as NeurIPS and ICML now regularly receive tens of thousands of submissions, posing significant challenges to maintaini…
Optimal Lower Bounds for Online Multicalibration
Natalie Collina, Jiuyao Lu, Georgy Noarov +1
We prove tight lower bounds for online multicalibration, establishing an information-theoretic separation from marginal calibration. In the general setting where group functions ca…