Post-Hoc Calibration

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9 papers in the last four weeks, up 80% on the four weeks before. 0.1% of all new papers.

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Latest papers 57

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Recovering Stranded Discrimination in Knowledge Tracing: Per-Item Bias Correction via Empirical-Bayes Shrinkage
Jun 12, 2026Xiaoran Yan, Cheng Tang, Atsushi ShimadaPost-Hoc Calibration Empirical Bayes
Kyushu University, Fukuoka, Japan
Conformal calibration and look-elsewhere effect in anomaly detection for new-physics searches
Jun 11, 2026Jack Y. Araz, Michael SpannowskyPost-Hoc Calibration Conformal Prediction
Department of Physics and Astronomy, University College London, London, WC1E 6B, UK · Department of Engineering, City St. George’s, University of London, London, EC1V 0HB, UK · Institute for Theoretical Physics, Campus Süd, Karlsruhe Institute of Technology (KIT), D-76128 Karlsruhe, Germany +1
Conformal Bayes under Label Shift: Post-Hoc Calibration vs. In-Training Adaptation
Jun 10, 2026Seungjin ChoiWeighted Conformal Prediction Label Shift
1CROID Research and aSSIST University, Seoul, Korea.
Adaptive Calibration for Fair and Performant Facial Recognition
Jun 3, 2026Ryan Brown, Chris RussellPost-Hoc Calibration Algorithmic Fairness
University of Oxford
CalArena: A Large-Scale Post-Hoc Calibration Benchmark
May 28, 2026Eugène Berta, David Holzmüller, Francis Bach +1Post-Hoc Calibration Probability Calibration
Inria - Ecole Normale Supérieure PSL Research University · Inria
Aligning LLMs with Human Uncertainty: A Beta-Bernoulli Calibrator for LLM Forecasting
May 26, 2026Hui Dai, Ryan Teehan, Parsa Torabian +1Post-Hoc Calibration Probabilistic Forecasting
Agentic Learning AI Lab, New York University · Chronologies AI
Spiking the training data to correct for test set contamination
May 24, 2026Johnny Tian-Zheng Wei, Jerry Li, Ameya Godbole +1Post-Hoc Calibration Benchmark Contamination
University of Southern California
Multicalibration Boosting: Theory, Convergence, and Transferability
May 23, 2026Hanxuan Ye, Hongzhe LiPost-Hoc Calibration Model Calibration
Department of Biostatistics and Epidemiology University of Pennsylvania Philadelphia, PA 19104-6021, USA · Department of Biostatistics and Epidemiology University of Pennsylvania2026 Philadelphia, PA 19104-6021, USA
Anomaly-Informed Confidence Calibration for Vision-Based Safety Prediction
May 20, 2026Zhenjiang Mao, Jiawen Wu, Gabriel Wagner +2Post-Hoc Calibration Autonomous Racing
Trustworthy Engineered Autonomy (TEA) Lab, Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA
Goal-Oriented Lower-Tail Calibration of Gaussian Processes for Bayesian Optimization
May 19, 2026Aurélien Pion, Emmanuel VazquezPost-Hoc Calibration Bayesian Optimization
Transvalor S.A., Biot, France · Univ. Paris-Saclay, CNRS, CentraleSup´elec, L2S, Gif-sur-Yvette, France
Uncertainty Reliability Under Domain Shift: An Investigation for Data-Driven Blood Pressure Estimation in Photoplethysmography
May 18, 2026Mohammad Moulaeifard, Ciaran Bench, Philip J. Aston +1Distribution Shift Robustness Post-Hoc Calibration
AI4Health Department, University of Oldenburg, Oldenburg, Germany · Department of Data Science and AI, National Physical Laboratory, Teddington, United Kingdom · School of Mathematics and Physics, University of Surrey, Surrey, United Kingdom
Isotonic Survival Regression: Calibrated Survival Distributions from Deep Cox Models
May 15, 2026Anchit Jain, Kevin Zhang, Stephen BatesPost-Hoc Calibration Survival Analysis
EECS, MIT, Cambridge MA, USA.
Discovery of Hidden Miscalibration Regimes
May 13, 2026Katarzyna Kobalczyk, Mihaela van der SchaarPost-Hoc Calibration Language Model Calibration
University of Cambridge
VERDI: Single-Call Confidence Estimation for Verification-Based LLM Judges via Decomposed Inference
May 11, 2026Jasmine Qi, Danylo Dantsev, Muyang SunPost-Hoc Calibration LLM-as-a-Judge
Indeed Inc.
Unified Approach for Weakly Supervised Multicalibration
May 11, 2026Futoshi Futami, Takashi IshidaPost-Hoc Calibration Model Calibration
The University of Osaka / RIKEN AIP / The University of Tokyo · The University of Tokyo / RIKEN AIP
Probing Routing-Conditional Calibration in Attention-Residual Transformers
May 11, 2026Wenhao Liang, Lin Yue, Wei Emma Zhang +4Post-Hoc Calibration Transformer
Adelaide University · Australian Institute for Machine Learning (AIML), Adelaide University · The University of Queensland
Two Ways to De-Bias an LLM-as-a-Judge: A Continuous-Score Comparison of Hierarchical Bayesian Calibration and Neural-ODE Score Transport
May 9, 2026Andrea MorandiPost-Hoc Calibration LLM-as-a-Judge
Cisco Systems, Inc.
The Minimax Rate of Perturbed Second-Order Calibration
May 8, 2026Kamil Ciosek, Banafsheh Rafiee, Sina Ghiassian +1Post-Hoc Calibration Minimax Estimation
Spotify
The Manokhin Probability Matrix: A Diagnostic Framework for Classifier Probability Quality
May 5, 2026Valery ManokhinPost-Hoc Calibration Probability Calibration
Independent Researcher
CHRep: Cross-modal Histology Representation and Post-hoc Calibration for Spatial Gene Expression Prediction
Apr 23, 2026Changfan Wang, Xinran Wang, Donghai Liu +4Spatial Transcriptomics Post-Hoc Calibration
Beijing University of Posts and Telecommunications, Beijing 100876, China, and also with the Beijing Key Laboratory of Network System and Network Culture, Beijing 100876, China · Peking University Third Hospital, Beijing 100191, China
Calibeating Prediction-Powered Inference
Apr 23, 2026Lars van der Laan, Mark Van Der LaanInverse Probability Weighting Parameter Estimation
Department of Statistics, University of Washington · Department of Statistics and Division of Biostatistics, Center for Targeted Machine Learning and Causal Inference, University of California, Berkeley
Calibrating Scientific Foundation Models with Inference-Time Stochastic Attention
Apr 21, 2026Akash Yadav, Taiwo A. Adebiyi, Ruda ZhangPost-Hoc Calibration Probabilistic Forecasting
University of Houston
TwinTrack: Post-hoc Multi-Rater Calibration for Medical Image Segmentation
Apr 17, 2026Tristan Kirscher, Alexandra Ertl, Klaus Maier-Hein +3Image Segmentation Post-Hoc Calibration
ICube, Institut Strauss · ICube Laboratory, CNRS UMR-7357, University of Strasbourg, Strasbourg, France · CLCC Institut Strauss, Strasbourg, France +6
V-ECE: Estimating General Expected Calibration Errors
Feb 27, 2026Eugène Berta, Sacha Braun, Francis Bach +2Probability Calibration Uncertainty Calibration
INRIA · Ecole Normale Sup´erieure, PSL Research University · UC Berkeley
Confidence-Uncertainty Boundary Calibration for Bayesian Deep Learning in Medical Image Analysis
Feb 12, 2026Hua Xu, Julián D. Arias-Londoño, Juan I. Godino-LlorenteBayesian Neural Networks Post-Hoc Calibration
Escuela Técnica Superior de Ingenieros de Telecomunicación Universidad Politécnica de Madrid 28040, Madrid, Spain
Adaptive Cumulative Mass Calibration with Conformal Prediction
May 21, 2025Daniil Kazantsev, Eric Moulines, Maxim Panov +2Post-Hoc Calibration Conformal Prediction
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates · École pour l’informatique et les techniques avancées (EPITA), Paris, France
A Calibration Audit of Confidence in Feed-Forward 3D Reconstruction Models
Date pendingNanxing Nick Deng, Qing Cheng, Niclas Zeller +1Post-Hoc Calibration Probability Calibration
Technical University of Munich · Munich Center for Machine Learning (MCML) · Karlsruhe University of Applied Sciences

Jun 12, 2026, cs.LGJ/K move · Enter open · S save

Recovering Stranded Discrimination in Knowledge Tracing: Per-Item Bias Correction via Empirical-Bayes Shrinkage

Xiaoran Yan, Cheng Tang, Atsushi Shimada

Kyushu University, Fukuoka, Japan

Post-Hoc Calibration Empirical Bayes Knowledge Tracing

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Deployed knowledge-tracing models are typically frozen after training, yet systematic per-item logit bias arises, from limited per-item expressivity in backbone architectures and from post-deployment shifts in item properties, degrading prediction quality. Global post-hoc calibrators such as Platt scaling, temperature scaling, and isotonic regression improve probability estimates but leave discriminative ability, as measured by AUC, unchanged. This AUC invariance is a structural consequence of monotone score-only transforms; recovering the stranded discrimination requires conditioning on item identity. We propose SLC (State-space Logit Correction), which converts binary observations to Gaussian pseudo-observations via Laplace/IRLS, applies empirical-Bayes shrinkage through a Kalman smoother, and fits an offset-Platt link. The state-space formulation also yields a detectability bound that characterizes the Bernoulli information floor, explaining why temporal tracking provides no benefit at current data densities. Across four datasets, five backbones, and three seeds, SLC improves AUC on all four datasets and NLL on three, with the advantage concentrating on sparse items. Cross-domain controls suggest that the same phenomenon can arise beyond education when the deployed backbone leaves entity-level bias.

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