6 papers · 1 filter
Tractable Agreement Protocols
Natalie Collina, Surbhi Goel, Varun Gupta +1
We present an efficient reduction that converts any machine learning algorithm into an interactive protocol, enabling collaboration with another party (e.g., a human) to achieve co…
The Value of Ambiguous Commitments in Multi-Follower Games
Natalie Collina, Rabanus Derr, Aaron Roth
We study games in which a leader makes a single commitment, and then multiple followers (each with a different utility function) respond. In particular, we study ambiguous commitme…
The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review
Buxin Su, Jiayao Zhang, Natalie Collina +6
We conducted an experiment during the review process of the 2023 International Conference on Machine Learning (ICML), asking authors with multiple submissions to rank their papers…
Repeated Contracting with Multiple Non-Myopic Agents: Policy Regret and Limited Liability
Natalie Collina, Varun Gupta, Aaron Roth
We study a repeated contracting setting in which a Principal adaptively chooses amongst Agents at each of rounds. The Agents are non-myopic, and so a mechanism for the Prin…
An Elementary Predictor Obtaining Distance to Calibration
Eshwar Ram Arunachaleswaran, Natalie Collina, Aaron Roth +1
Blasiok et al. [2023] proposed distance to calibration as a natural measure of calibration error that unlike expected calibration error (ECE) is continuous. Recently, Qiao and Zhen…
Forecasting for Swap Regret for All Downstream Agents
Aaron Roth, Mirah Shi
We study the problem of making predictions so that downstream agents who best respond to them will be guaranteed diminishing swap regret, no matter what their utility functions are…