Distribution-informed Online Conformal Prediction

• URL: http://arxiv.org/abs/2512.07770v1
• Date: Mon, 08 Dec 2025 17:51:49 GMT
• Title: Distribution-informed Online Conformal Prediction
• Authors: Dongjian Hu, Junxi Wu, Shu-Tao Xia, Changliang Zou,
• Abstract summary: We propose Conformal Optimistic Prediction (COP), an online conformal prediction algorithm incorporating underlying data pattern into the update rule.<n>COP produces tighter prediction sets when predictable pattern exists, while retaining valid coverage guarantees even when estimates are inaccurate.<n>We prove that COP can achieve valid coverage and construct shorter prediction intervals than other baselines.
• Score: 53.674678995825666
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Conformal prediction provides a pivotal and flexible technique for uncertainty quantification by constructing prediction sets with a predefined coverage rate. Many online conformal prediction methods have been developed to address data distribution shifts in fully adversarial environments, resulting in overly conservative prediction sets. We propose Conformal Optimistic Prediction (COP), an online conformal prediction algorithm incorporating underlying data pattern into the update rule. Through estimated cumulative distribution function of non-conformity scores, COP produces tighter prediction sets when predictable pattern exists, while retaining valid coverage guarantees even when estimates are inaccurate. We establish a joint bound on coverage and regret, which further confirms the validity of our approach. We also prove that COP achieves distribution-free, finite-sample coverage under arbitrary learning rates and can converge when scores are $i.i.d.$. The experimental results also show that COP can achieve valid coverage and construct shorter prediction intervals than other baselines.

Related papers

• Time-uniform conformal and PAC prediction [0.8021197489470758]
  We develop an extension of the conformal prediction and related probably approximately correct (PAC) prediction frameworks to sequential settings.<n>The resulting prediction sets are anytime-valid in that their expected coverage is at the required level at any time chosen by the analyst.<n>We present theoretical guarantees for our proposed methods and demonstrate their validity and utility on simulated and real datasets.
  arXiv  Detail & Related papers  (2026-02-06T01:41:10Z)
• Optimal Conformal Prediction under Epistemic Uncertainty [61.46247583794497]
  Conformal prediction (CP) is a popular framework for representing uncertainty.<n>We introduce Bernoulli prediction sets (BPS) which produce the smallest prediction sets that ensure conditional coverage.<n>When given first-order predictions, BPS reduces to the well-known adaptive prediction sets (APS)
  arXiv  Detail & Related papers  (2025-05-25T08:32:44Z)
• Conformal Prediction Sets with Improved Conditional Coverage using Trust Scores [52.92618442300405]
  It is impossible to achieve exact, distribution-free conditional coverage in finite samples.<n>We propose an alternative conformal prediction algorithm that targets coverage where it matters most.
  arXiv  Detail & Related papers  (2025-01-17T12:01:56Z)
• Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning [53.42244686183879]
  Conformal prediction provides model-agnostic and distribution-free uncertainty quantification.<n>Yet, conformal prediction is not reliable under poisoning attacks where adversaries manipulate both training and calibration data.<n>We propose reliable prediction sets (RPS): the first efficient method for constructing conformal prediction sets with provable reliability guarantees under poisoning.
  arXiv  Detail & Related papers  (2024-10-13T15:37:11Z)
• Probabilistic Conformal Prediction with Approximate Conditional Validity [81.30551968980143]
  We develop a new method for generating prediction sets that combines the flexibility of conformal methods with an estimate of the conditional distribution. Our method consistently outperforms existing approaches in terms of conditional coverage.
  arXiv  Detail & Related papers  (2024-07-01T20:44:48Z)
• Robust Conformal Prediction Using Privileged Information [17.886554223172517]
  We develop a method to generate prediction sets with a guaranteed coverage rate that is robust to corruptions in the training data.<n>Our approach builds on conformal prediction, a powerful framework to construct prediction sets that are valid under the i.i.d assumption.
  arXiv  Detail & Related papers  (2024-06-08T08:56:47Z)
• Post-selection Inference for Conformal Prediction: Trading off Coverage for Precision [0.0]
  Traditionally, conformal prediction inference requires a data-independent specification of miscoverage level. We develop simultaneous conformal inference to account for data-dependent miscoverage levels.
  arXiv  Detail & Related papers  (2023-04-12T20:56:43Z)
• Distribution-Free Finite-Sample Guarantees and Split Conformal Prediction [0.0]
  split conformal prediction represents a promising avenue to obtain finite-sample guarantees under minimal distribution-free assumptions. We highlight the connection between split conformal prediction and classical tolerance predictors developed in the 1940s.
  arXiv  Detail & Related papers  (2022-10-26T14:12:24Z)
• Predictive Inference with Feature Conformal Prediction [80.77443423828315]
  We propose feature conformal prediction, which extends the scope of conformal prediction to semantic feature spaces. From a theoretical perspective, we demonstrate that feature conformal prediction provably outperforms regular conformal prediction under mild assumptions. Our approach could be combined with not only vanilla conformal prediction, but also other adaptive conformal prediction methods.
  arXiv  Detail & Related papers  (2022-10-01T02:57:37Z)
• Practical Adversarial Multivalid Conformal Prediction [27.179891682629183]
  We give a generic conformal prediction method for sequential prediction. It achieves target empirical coverage guarantees against adversarially chosen data. It is computationally lightweight -- comparable to split conformal prediction.
  arXiv  Detail & Related papers  (2022-06-02T14:33:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.