arXiv Machine Learning July 16, 2026 By Guillaume Principato, Gilles Stoltz

Adaptive Conformal Inference through the Lens of Blackwell Approachability

Read the original on arXiv Machine Learning →

arXiv:2510. 15824v2 Announce Type: replace-cross Abstract: This article considers an online version of conformal inference, called adaptive conformal inference [ACI] and introduced by Gibbs and Cand\`es (2021): prediction sets are issued sequentially, after observing features and before the outcomes are revealed.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

safety

arXiv Machine Learning

Jul 30

Simultaneous Coverage and Efficiency Guarantee in Online Conformal Prediction

arXiv:2607. 26577v1 Announce Type: new Abstract: Adaptive conformal inference (ACI) of Gibbs and Cand{\`e}s and its variants are the standard approach to online conformal prediction under distribution shift, but they suffer from three fundamental limitations.

By Rahul Vaze

benchmarks safety

arXiv Machine Learning

Jul 28

Efficient Online Conformal Selection with Limited Feedback

arXiv:2605. 14953v2 Announce Type: replace Abstract: We address the problem of conformal selection, where an agent must select a minimal subset of options to ensure that at least one ``success'' is identified with a pre-specified target probability $\phi$.

By Sreenivas Gollapudi, Kostas Kollias, Kamesh Munagala, Ali Sinop

agents reinforcement-learning benchmarks

arXiv Machine Learning

Jul 10

Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents

arXiv:2306. 02704v2 Announce Type: replace-cross Abstract: We introduce \emph{Calibrated Stackelberg Games (CSGs)}, a generalization of the standard Stackelberg Games (SGs) framework.

By Nika Haghtalab, Chara Podimata, Kunhe Yang

agents safety

arXiv Machine Learning

Jun 5

Regret Minimization with Adaptive Opponents in Repeated Games

arXiv:2606. 06486v1 Announce Type: new Abstract: In this paper, we study regret minimization in repeated games with \emph{adaptive} opponents who can respond based on histories of play.

By Mingyang Liu, Asuman Ozdaglar, Tiancheng Yu, Kaiqing Zhang

arXiv Machine Learning

Jul 3

Prediction Sets for Counterfactual Decisions: Coverage, Optimality, and Conformal Prediction

arXiv:2607. 02206v1 Announce Type: cross Abstract: Predictions are increasingly used to guide high-stakes decisions, from treatment selection to policy making.

By Yurui Zheng, Ying Jin

arXiv Machine Learning

Jun 26

Blackwell Approachability and Gradient Equilibrium are Equivalent

arXiv:2606. 27315v1 Announce Type: new Abstract: Gradient equilibrium (GEQ) is a recently introduced online optimization framework that generalizes first-order stationarity from offline optimization and abstracts problems like online conformal prediction.

By Brian W. Lee, Nika Haghtalab, Michael I. Jordan, Ryan J. Tibshirani

Related stories

Simultaneous Coverage and Efficiency Guarantee in Online Conformal Prediction

Efficient Online Conformal Selection with Limited Feedback

Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents

Regret Minimization with Adaptive Opponents in Repeated Games

Prediction Sets for Counterfactual Decisions: Coverage, Optimality, and Conformal Prediction

Blackwell Approachability and Gradient Equilibrium are Equivalent