Online Calibration

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12 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

CALM-5G: Event-Level Conformal Risk Control for Predictive Service-Assurance Alarms
Oct 13, 2026Ömer Faruk İşcanlı, Yağmur Sabucu SandalOnline Calibration Conformal Risk Control
Siemens A.¸S., Istanbul, Türkiye
High-dimensional online calibration from harmonic weights
Oct 6, 2026Maxwell Fishelson, Mehryar MohriOnline Calibration Probability Calibration
Institute for Advanced Study. · Google Research and Courant Institute of Mathematical Sciences, New York.
Model-Agnostic Online Certificate-Driven Calibration for Time Series Forecasting Under Distribution Shift
Sep 25, 2026Chenfeng Huang, Zixuan Ma, George MichailidisOnline Calibration Time Series Forecasting
Department of Statistics and Data Science, University of California, Los Angeles, Los Angeles, California, USA
DreamLedger: Where to Refuse World-Model Imagination Using Execution-Settled Credit
Aug 24, 2026Xianyao Li, Ruitong Tian, Rui Min +2Online Calibration Selective Prediction
Department of Civil and Coastal Engineering, University of Florida, Gainesville, FL 32611, USA · Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611, USA
Truthful Calibration Measures for Sequential Prediction
Aug 21, 2026Anagha Gokul, Jason Hartline, Lunjia Hu +2Online Calibration Sequence Prediction
Khoury College of Computer Sciences · Department of Computer Science · Microsoft Research New England
Optimal Recalibration of an Online Predictor
Jul 22, 2026Lunjia Hu, Kevin Tian, Chutong YangModel Calibration Online Calibration
Northeastern University · University of Texas at Austin
Efficient Sequential Calibration with $O(T^{2/3-ε})$ Error Bound
Jul 14, 2026Zihan ZhangBlackwell Approachability Online Calibration
Department of CSE, HKUST
Anomaly-Informed Confidence Calibration for Vision-Based Safety Prediction
May 20, 2026Zhenjiang Mao, Jiawen Wu, Gabriel Wagner +2Online Calibration Post-Hoc Calibration
Trustworthy Engineered Autonomy (TEA) Lab, Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA
Adaptive Calibration in Non-Stationary Environments
May 12, 2026Junyan Liu, Haipeng Luo, Lillian J. RatliffOnline Calibration Model Calibration
University of Washington · University of Southern California
Instance-Adaptive Online Multicalibration
May 10, 2026Zhiming Huang, Jamie Morgenstern, Aaron Roth +1Probability Calibration Model Calibration
Paul G. Allen School of Computer Science and Engineering, University of Washington · Department of Computer and Information Sciences, University of Pennsylvania
Calibeating Made Simple
Mar 23, 2026Yurong Chen, Zhiyi Huang, Michael I. Jordan +1Online Calibration Probability Calibration
Inria, École Normale Supérieure, PSL Research University · The University of Hong Kong · University of California, Berkeley +1
Airborne Magnetic Anomaly Navigation with Neural-Network-Augmented Online Calibration
Mar 9, 2026Antonia Hager, Sven Nebendahl, Alexej Klushyn +3Online Calibration GNSS-Denied Localization
Airbus, 82024 Taufkirchen, Germany. · Norwegian University of Science and Technology, 7034 Trondheim, Norway.

Oct 13, 2026J/K move · Enter open · S save

CALM-5G: Event-Level Conformal Risk Control for Predictive Service-Assurance Alarms

Ömer Faruk İşcanlı, Yağmur Sabucu Sandal

Siemens A.¸S., Istanbul, Türkiye

Online Calibration Conformal Risk Control Model Calibration Conformal Prediction Wireless Communications

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Predictive service assurance converts forecasts of key performance indicators into alarms, and that conversion decides whether the assurance function is deployable. We present CALM-5G, an assurance layer that operates above any forecasting backbone and issues alarms with distribution-free, finite-sample guarantees. It calibrates on service-level violation events, scores violation onset directly, selects its calibration scheme out of sample, and reports the alarm duty cycle with every certified miss rate. Four corpora are evaluated separately, namely a production cellular network, commercial 5G traces, an open radio access network testbed and our 5G standalone testbed. Risk control on the regression-derived score yields duty cycles of 0.24 to 0.91. Onset targeting with cross-validated calibration reduces time-in-alarm by 56.3% to 89.7% on three corpora, with no significant change on the fourth. The realised miss rate stays within a two-standard-error tolerance on three corpora and within one standard error of it on the fourth. Under label shift, pooled batch calibration exceeds that tolerance structurally, by 10.9 standard errors on the production corpus. Online calibration restores compliance. Calibration schemes cannot be ranked on the split that fits their thresholds. Miss rate and duty cycle are certified jointly where within-entity dependence leaves sufficient calibration units.