arXiv Machine Learning July 7, 2026 By Xinyun Lu, Haoang Chi, Zhiheng Zhang

Denoised Conformal Alignment for Reliable Selection of Conditional Average Treatment Effect Predictions

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arXiv:2607. 03161v1 Announce Type: cross Abstract: In selective deployment, practitioners act only on a model-chosen subset of individuals based on predicted conditional average treatment effects, but marginal conformal guarantees need not control reliability on that selected subset.

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arXiv Machine Learning

Jul 28

Robust Conformalized Selection with Noisy Responses

arXiv:2607. 22985v1 Announce Type: cross Abstract: Conformalized selection has been widely applied to select high-quality candidates from large datasets with rigorous uncertainty quantification, such as reliable labeling, drug discovery, and the alignment of large language models.

By Chengyao Yu, Hongxin Wei, Bingyi Jing

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arXiv Machine Learning

Jul 10

Beyond Fixed False Discovery Rates: Post-Hoc Conformal Selection with E-Variables

arXiv:2604. 11305v3 Announce Type: replace Abstract: Conformal selection (CS) uses calibration data to identify test inputs whose unobserved outcomes are likely to satisfy a pre-specified minimal quality requirement, while controlling the false discovery rate (FDR).

By Meiyi Zhu, Osvaldo Simeone

arXiv Machine Learning

Jun 16

Conformal Candidate Certification for Offline Model-Based Optimization

arXiv:2606. 15217v1 Announce Type: cross Abstract: Offline model-based optimization (MBO) proposes candidates by optimizing a surrogate trained on a fixed historical dataset.

By Seungjin Choi

arXiv Machine Learning

Sep 15

Conformal Individual Treatment Effect Estimation under Networked Interference

arXiv:2609.15254v1 Announce Type: cross Abstract: Conformal counterfactual prediction constructs prediction sets with finite-sample coverage guarantees for counterfactual outcomes and individual trea...

By Matteo Zecchin, Osvaldo Simeone

arXiv Machine Learning

Jun 11

Conformal Bayes under Label Shift: Post-Hoc Calibration vs. In-Training Adaptation

arXiv:2606. 11865v1 Announce Type: cross Abstract: Conformal Bayes combines Bayesian posterior predictives with conformal calibration to produce prediction sets that are both statistically valid and geometrically efficient.

By Seungjin Choi

arXiv Machine Learning

Jul 21

Isotonic Conformal Prediction

arXiv:2607. 16675v1 Announce Type: cross Abstract: A point prediction that is well calibrated on average can still be systematically biased conditional on its own value, undermining its use in downstream decision-making.

By Daniel Bensimon, Sean Xiang Yu, Eric D. Kolaczyk, Archer Y. Yang

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