6 papers · 1 filter
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…
Emergent Alignment via Competition
Natalie Collina, Surbhi Goel, Aaron Roth +2
Aligning AI systems with human values remains a fundamental challenge, but does our inability to create perfectly aligned models preclude obtaining the benefits of alignment? We st…
Collaborative Prediction: Tractable Information Aggregation via Agreement
Natalie Collina, Ira Globus-Harris, Surbhi Goel +3
We give efficient "collaboration protocols" through which two parties, who observe different features about the same instances, can interact to arrive at predictions that are more…
Sample Efficient Omniprediction and Downstream Swap Regret for Non-Linear Losses
Jiuyao Lu, Aaron Roth, Mirah Shi
We define "decision swap regret" which generalizes both prediction for downstream swap regret and omniprediction, and give algorithms for obtaining it for arbitrary multi-dimension…
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…
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…