Adaptive Conformal Inference

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

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Learning to Adapt and Calibrate: Score Distribution Alignment for Few-Shot Uncertainty Prediction in Medical VLMs
Sep 9, 2026Xuan Cuong Ngo, Ngan LeVLM Adaptation Conformal Prediction
University of Arkansas Fayetteville, Arkansas, USA
ConformalShift: Targeted Event Reordering Against Adaptive ECG Monitoring
Aug 4, 2026Arash Vashagh, Yasmin VashaghAdversarial Attacks Electrocardiography
Faculty of Computer Science, University of New Brunswick, Fredericton, New Brunswick E3B 5A3, Canada · Farzanegan Amin 2 High School, Isfahan, Iran
Conformalized Rate-Adaptive Sensing
Jul 29, 2026Jiawei Yang, Yao ZhangImage Reconstruction Computational Imaging
Department of Statistics and Data Science National University of Singapore
Simultaneous Coverage and Efficiency Guarantee in Online Conformal Prediction
Jul 29, 2026Rahul VazeSet Prediction Conformal Prediction
Tata Institute of Fundamental Research, Mumbai
Adaptive Multi-Scale Forecasting and Gate-Localized Conformal Prediction for Multivariate Nonstationary Time Series
Jul 25, 2026Ziling Ma, Junshu Jiang, Ángel López-Oriona +2Multivariate Time Series Forecasting Conformal Prediction
Statistics Department, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia · NeuroAI Laboratory, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
Adaptive Bayesian Online Learning via Expert Aggregation
Jul 22, 2026Jungbin Jun, Ilsang OhnBayesian Inference Adaptive Conformal Inference
Department of Statistics Inha University Incheon, 22212, South Korea
tsbootstrap: Distribution-Free Uncertainty Quantification and Conformal Prediction for Time Series
Jul 7, 2026Sankalp GildaUncertainty Quantification Conformal Prediction
DeepThought Solutions
Doubly Robust Adaptive Conformal Inference for Causal Effects Under Temporal Dependence
Jun 29, 2026Andreas Koukorinis, Ricardo SilvaCausal Effect Estimation Conformal Prediction
Department of Computer Science University College London · Department of Statistical Science University College London
When Can Conformal Risk Control Certify LLM Outputs? Bounds, Impossibility, and Adaptation for Structured Generation
Jun 27, 2026Varun KotteConformal Prediction LLM Reliability
Independent Researcher
SPACR: Single-Pass Adaptive Training of Uncertainty-Aware Conformal Regressors
Jun 9, 2026Soundouss Messoudi, Sylvain Rousseau, Sébastien DesterckeConformal Prediction Adaptive Conformal Inference
Heudiasyc - UMR CNRS 7253, Université de Technologie de Compiègne
Distribution-Free Uncertainty Quantification for Continuous AI Agent Evaluation
May 19, 2026Yuxuan Gao, Megan Wang, Yi Ling YuUncertainty Quantification AI Agent Evaluation
OpenMesh · University of Pennsylvania · Columbia Univer
Efficient Online Conformal Selection with Limited Feedback
May 14, 2026Sreenivas Gollapudi, Kostas Kollias, Kamesh Munagala +1Conformal Prediction Adaptive Conformal Inference
Google Research, Mountain View, CA, USA. · Department of Computer Science, Duke University, Durham, NC, USA.
RareCP: Regime-Aware Retrieval for Efficient Conformal Prediction
May 9, 2026Manuel Heurich, Maximilian Granz, Tim LandgrafWeighted Conformal Prediction Time Series Forecasting
Institute for Computer Science Freie Universität Berlin Arnimallee 7 14195 Berlin
Adaptive Geodesic Conformal Prediction for Egocentric Camera Pose Estimation
Apr 30, 2026Aishani Pathak, Hasti SeifiUncertainty Quantification Camera Pose Estimation
Arizona State University
Safe Control using Learned Safety Filters and Adaptive Conformal Inference
Apr 20, 2026Sacha Huriot, Ihab Tabbara, Hussein SibaiSafety-Critical Control Conformal Prediction
Computer Science & Engineering Washington University in St. Louis
Adaptive Conformal Inference through the Lens of Blackwell Approachability
Oct 17, 2025Guillaume Principato, Gilles StoltzConformal Prediction Adaptive Conformal Inference
Universit´e Paris-Saclay, CNRS, Inria, Laboratoire de math´ematiques d’Orsay, 91405, Orsay, France · EDF R&D, Palaiseau, France · HEC Paris, Jouy-en-Josas, France

Sep 9, 2026, cs.CVJ/K move · Enter open · S save

Learning to Adapt and Calibrate: Score Distribution Alignment for Few-Shot Uncertainty Prediction in Medical VLMs

Xuan Cuong Ngo, Ngan Le

University of Arkansas Fayetteville, Arkansas, USA

VLM Adaptation Conformal Prediction Medical VLMs Adaptive Conformal Inference VLM Calibration

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Uncertainty estimation for medical vision--language models (VLMs) using conformal prediction has gained increasing attention due to its distribution-free coverage guarantees. However, standard conformal prediction relies on exchangeability between calibration and test data and typically requires a sufficiently large calibration set to obtain reliable coverage. These assumptions are difficult to satisfy in few-shot transfer settings, where only a small labeled support set is available to adapt a pretrained VLM to a new medical task, while an unlabeled query set is used for evaluation. Supervised fine-tuning on the support set changes the model parameters and consequently shifts the nonconformity score distribution, breaking exchangeability between calibration and query samples and leading to unreliable coverage under distribution shift. Existing transductive conformal adaptation methods often preserve validity by avoiding supervised updates. While this helps maintain conformal assumptions, it underutilizes the scarce labeled support data and limits task adaptation, which is the primary objective in few-shot learning. In this setting, conformal prediction should serve as an uncertainty estimation layer that supports the adapted model, rather than preventing adaptation itself. To this end, we propose AlignCP, a framework that reconciles supervised few-shot adaptation with conformal uncertainty estimation under non-exchangeability. AlignCP learns a reweighted calibration distribution that reduces the score-level discrepancy between the labeled support set and the unlabeled query set. By aligning the one-dimensional nonconformity score distributions, AlignCP aims to close the coverage gap induced by adaptation without requiring query labels.

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