New research advances conformal prediction for uncertainty quantification
作者PulseAugur 编辑部·[16 个来源]·
Several recent research papers explore advancements in conformal prediction, a method for quantifying uncertainty in machine learning models. One paper introduces an efficient online conformal selection technique that requires less feedback, while another focuses on the trade-offs involved in achieving fairness in conformal prediction, highlighting tensions between coverage and set size. Additional research delves into new theoretical frameworks for conformal prediction, including methods that use transported beta laws, tighten coverage bounds through score transformation, and optimize prediction sets without data splitting by extending to multi-variable calibration.
AI
影响
These papers advance theoretical understanding and practical application of uncertainty quantification in ML models.
排序理由
Cluster consists of multiple academic papers on conformal prediction.
arXiv:2605.21928v1 Announce Type: new Abstract: Estimating treatment effects from observational data requires choosing an adjustment set, but valid adjustment depends on an unknown causal graph. Graph misspecification can cause under-coverage, while graph-agnostic conformal wrapp…
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 $φ$. While traditional online conformal prediction focuses on maintaining validity f…
Conformal prediction is often calibrated with a single pooled threshold, but this can hide cross-group heterogeneity in score distributions and distort group-wise coverage. We study this phenomenon through the population score distributions underlying split conformal calibration.…
arXiv stat.ML
TIER_1English(EN)·Ziang Song, Ying Jin, Emmanuel J. Cand\`es·
arXiv:2605.20726v1 Announce Type: cross Abstract: Modern applications of conformal inference to multiple testing problems, such as outlier detection and candidate selection, often involve selecting test samples whose conformal p-values fall below a threshold. The quality of such …
arXiv stat.ML
TIER_1English(EN)·Emmanuel J. Candès·
Modern applications of conformal inference to multiple testing problems, such as outlier detection and candidate selection, often involve selecting test samples whose conformal p-values fall below a threshold. The quality of such methods is often measured by the false discovery p…
arXiv stat.ML
TIER_1English(EN)·Thiago R. Ramos, Helton Graziadei, Luben M. C. Cabezas·
arXiv:2605.19024v1 Announce Type: new Abstract: Split conformal prediction provides finite-sample marginal coverage under exchangeability, but this guarantee averages over the random calibration sample. We study instead the law of the calibration-conditional coverage induced by a…
Split conformal prediction provides finite-sample marginal coverage under exchangeability, but this guarantee averages over the random calibration sample. We study instead the law of the calibration-conditional coverage induced by a realized conformal threshold. In the continuous…
arXiv stat.ML
TIER_1English(EN)·Ziang Gao, Pengqi Liu, Archer Yi Yang, Mouloud Belbahri, Jesse C. Cresswell, Masoud Asgharian·
arXiv:2605.14260v2 Announce Type: replace Abstract: Conformal prediction is often calibrated with a single pooled threshold, but this can hide cross-group heterogeneity in score distributions and distort group-wise coverage. We study this phenomenon through the population score d…
arXiv:2605.12668v1 Announce Type: new Abstract: Conformal prediction provides a principled framework for uncertainty quantification with finite-sample coverage guarantees. While recent work has extended conformal prediction to online and sequential settings, existing methods typi…
Conformal prediction is often calibrated with a single pooled threshold, but this can hide cross-group heterogeneity in score distributions and distort group-wise coverage. We study this phenomenon through the population score distributions underlying split conformal calibration.…
Conformal prediction is often calibrated with a single pooled threshold, but this can hide cross-group heterogeneity in score distributions and distort group-wise coverage. We study this phenomenon through the population score distributions underlying split conformal calibration.…
arXiv:2603.23374v2 Announce Type: replace-cross Abstract: Achieving valid conditional coverage in conformal prediction is challenging due to the theoretical difficulty of satisfying pointwise constraints in finite samples. Building upon the characterization of conditional coverag…
arXiv stat.ML
TIER_1English(EN)·Laura L\"utzow, Simone Garatti, Marco C. Campi, Lars Lindemann, Matthias Althoff·
arXiv:2605.12341v1 Announce Type: new Abstract: Conformal prediction constructs prediction sets with finite-sample coverage guarantees, but its calibration stage is structurally constrained to a scalar score function and a single threshold variable - forcing shapes of prediction …
Conformal prediction provides a principled framework for uncertainty quantification with finite-sample coverage guarantees. While recent work has extended conformal prediction to online and sequential settings, existing methods typically focus on a single coverage level and do no…
Conformal prediction constructs prediction sets with finite-sample coverage guarantees, but its calibration stage is structurally constrained to a scalar score function and a single threshold variable - forcing shapes of prediction sets to be fixed before calibration, typically t…