Multicalibration

Follow this topic Ask about this topic

Momentum

1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 12

All topics

Multigroup Fairness and Omniprediction: Separations and Equivalences
Oct 5, 2026Sílvia Casacuberta, Parikshit Gopalan, Varun Kanade +3Group Fairness Multicalibration
Stanford University · Apple · University of Oxford +2
Augmented Feature Boosting for Multicalibration
Sep 27, 2026Ira Globus-Harris, Inbal Livni NavonMulticalibration
Cornell University · Ben-Gurion University of the Negev
Sample Complexity of Multicalibration for Multilevel Properties
Aug 4, 2026Jiuyao Lu, Krishnakumar Balasubramanian, Aleksandr Podkopaev +1Sample Complexity Model Calibration
Department of Statistics and Data Science, The Wharton School, University of Pennsylvania · 2Amazon · Department of Statistics, University of California, Davis
Fast Rates for Swap-Agnostic Learning of Proper Losses
Jul 30, 2026Princewill OkoroaforStatistical Learning Theory Online Learning
Harvard University
Optimal Deterministic Multicalibration and Omniprediction
Jun 18, 2026Georgy Noarov, Aaron RothMinimax Estimation Model Calibration
University of Pennsylvania
COPF: An Online Framework for Deployment-Stable Counterfactual Fairness in Evolving Graphs
May 30, 2026Sheng'en Li, Dongmian ZouAlgorithmic Fairness Counterfactual Fairness
Zu Chongzhi Center and DIRC, Duke Kunshan University, Kunshan, China.
Multicalibration Boosting: Theory, Convergence, and Transferability
May 23, 2026Hanxuan Ye, Hongzhe LiPost-Hoc Calibration Model Calibration
Department of Biostatistics and Epidemiology University of Pennsylvania Philadelphia, PA 19104-6021, USA · Department of Biostatistics and Epidemiology University of Pennsylvania2026 Philadelphia, PA 19104-6021, USA
Unified Approach for Weakly Supervised Multicalibration
May 11, 2026Futoshi Futami, Takashi IshidaPost-Hoc Calibration Model Calibration
The University of Osaka / RIKEN AIP / The University of Tokyo · The University of Tokyo / RIKEN AIP
Instance-Adaptive Online Multicalibration
May 10, 2026Zhiming Huang, Jamie Morgenstern, Aaron Roth +1Probability Calibration Model Calibration
Paul G. Allen School of Computer Science and Engineering, University of Washington · Department of Computer and Information Sciences, University of Pennsylvania
The Sample Complexity of Multicalibration
Apr 23, 2026Natalie Collina, Jiuyao Lu, Georgy Noarov +1Probability Calibration Sample Complexity
University of Pennsylvania
Multicalibration for Unbiased Model-Based Prevalence Estimation
Apr 23, 2026Fridolin Linder, Thomas Leeper, Daniel Haimovich +3Probability Calibration Distribution Shift
Meta Platforms Inc. · The London School of Economics and Political Science
An Efficient Black-Box Reduction from Online Learning to Multicalibration, and a New Route to $Φ$ -Regret Minimization
Apr 21, 2026Gabriele Farina, Juan Carlos PerdomoModel Calibration Regret Minimization
MIT EECS · New York University

Oct 5, 2026, cs.LGJ/K move · Enter open · S save

Multigroup Fairness and Omniprediction: Separations and Equivalences

Sílvia Casacuberta, Parikshit Gopalan, Varun Kanade, Omer Reingold+2

Stanford University · Apple · University of Oxford+2

Group Fairness Multicalibration

Open paper AskPDF arXiv

Omniprediction is a learning guarantee which requires a single predictor to be competitive relative to the best hypothesis from a benchmark class for any loss chosen from a family of loss functions. Loss Outcome Indistinguishability (loss OI for short) is a stronger notion that implies omniprediction. It requires the predicted distribution on labels to be indistinguishable from the true distribution to tests that depend on the loss functions and the benchmark class. Multiaccuracy and multicalibration are multigroup fairness notions that generalize classical notions of calibration and accuracy in expectation. Most known learning algorithms for omniprediction (both for the standard notion and for strengthenings like loss OI) rely on some version of these multigroup fairness notions, or on an intermediate notion called calibrated multiaccuracy. We ask if this is necessary: Does omniprediction require some form of multigroup fairness? We show that the answer is no for (plain) omniprediction, and yes for loss OI. First, a sequence of works shows that multicalibration or calibrated multiaccuracy imply omniprediction. We rule out even a weak converse, by showing that omniprediction for proper losses does not imply even accuracy in expectation, a much weaker notion than any of calibration, multiaccuracy, or multicalibration. Second, prior work showed how to achieve loss OI from a combination of calibration and multiaccuracy. We show a converse: loss OI is equivalent to a form of calibrated multiaccuracy.

Momentum

Related topics

Latest papers 12