6 papers
Toward Simultaneously Optimal Regret in U-Calibration
Rafael Frongillo, Haipeng Luo, Nishant A. Mehta +1
U-calibration studies online forecasting algorithms whose predictions can be consumed by any unknown downstream agent, guaranteeing sublinear regret simultaneously for all proper l…
Calibeating Made Simple
Yurong Chen, Zhiyi Huang, Michael I. Jordan +1
We study calibeating, the problem of post-processing external forecasts online to minimize cumulative losses and match an informativeness-based benchmark. Unlike prior work, which…
Efficient Swap Multicalibration of Elicitable Properties
Lunjia Hu, Haipeng Luo, Spandan Senapati +1
Multicalibration [HJKRR18] is an algorithmic fairness perspective that demands that the predictions of a predictor are correct conditional on themselves and membership in a collect…
Improved Bounds for Swap Multicalibration and Swap Omniprediction
Haipeng Luo, Spandan Senapati, Vatsal Sharan
In this paper, we consider the related problems of multicalibration -- a multigroup fairness notion and omniprediction -- a simultaneous loss minimization paradigm, both in the dis…
Simultaneous Swap Regret Minimization via KL-Calibration
Haipeng Luo, Spandan Senapati, Vatsal Sharan
Calibration is a fundamental concept that aims at ensuring the reliability of probabilistic predictions by aligning them with real-world outcomes. There is a surge of studies on ne…
Optimal Multiclass U-Calibration Error and Beyond
Haipeng Luo, Spandan Senapati, Vatsal Sharan
We consider the problem of online multiclass U-calibration, where a forecaster aims to make sequential distributional predictions over classes with low U-calibration error, tha…