The primary aim of this workshop is to bring together researchers and practitioners working on calibration across machine learning, statistics, theoretical computer science, and applied domains. We seek to clarify foundational questions, align evaluation practices, and explore the practical implications of calibration for reliable and trustworthy AI systems.

Topics

The potential topics include, but are not limited to:

• Foundations of calibration and probabilistic forecasting
• Calibration metrics and evaluation methodologies
• Proper scoring rules and decision-theoretic perspectives
• Calibration in high-dimensional and multiclass settings
• Post-hoc and end-to-end calibration methods
• Calibration under distribution shift
• Calibration for generative models and large language models
• Calibration in high-stakes applications (e.g., medicine, forecasting, finance)
• Connections between calibration, uncertainty, and trust in AI

Submissions

🚨 Submit to our workshop and win a free registration for AISTATS 2026 🚨
We will offer a free conference registration to the best workshop submission led by a student, don't miss the opportunity to showcase your work and attend the conference for free!

We invite submissions of short papers presenting recent work on calibration. Submissions are accepted through OpenReview.

If your paper about calibration (or a closely related topic) is already accepted at the main AISTATS 2026 conference (congrats 🎉), you can register to present it at our poster session by filling the following form: main conference paper track.

Important dates

• Call for contributions: January 12, 2026
• Submission deadline: February 20, 2026 📣 Extended deadline: February 27, 2026 (Anywhere on Earth) 📣
• Notification of acceptance: March 9, 2026
• Workshop date: May 5, 2026

Format

Submissions should be formatted using the AISTATS LaTeX style. Papers are limited to 4 pages (excluding references and appendices). The review process will be double-blind. Accepted contributions will be presented as posters during the workshop. If you include an appendix, keep in mind that reviewers might not read it carefully. Your principal idea / contribution should be understandable from the main text.

Policies

Submissions under review at other venues are allowed. All accepted papers are non-archival and will be made publicly available on OpenReview.

Awards

• 🏆 Student Paper Award Free Registration Award
  From Entropy to Calibrated Uncertainty: Training Language Models to Reason About Uncertainty
  Azza Jenane, Nassim Walha, Lukas Kuhn, Florian Buettner
• 🏆 Non-Student Paper Award
  Calibrating the Calibration Tester: Optimal Binning and Minimax Calibration Testing for Continuous Predictive Models
  Alon Kipnis

Accepted papers

• Interpretable Multivariate Conformal Prediction with Balanced and Jointly Calibrated Rectangular Envelopes
  Nabil Alami, Rafael Izbicki, Souhaib Ben Taieb
• Calibration Collapse Under Sycophancy Fine-Tuning: How Reward Hacking Breaks Uncertainty Quantification in LLMs
  Subramanyam Sahoo
• Full Conformal Prediction under Stochastic Non-Conformity Measure
  Thanawat Sornwanee
• SPACR: Single-Pass Adaptive Training of Uncertainty-Aware Conformal Regressors
  Soundouss Messoudi, Sylvain Rousseau, Sebastien Destercke
• On the Global and Local Calibration of Graph Neural Networks
  Francesco Ferrini, Veronica Lachi, Antonio Longa, Cesare Barbera, Andrea Pugnana, Andrea Passerini, Manfred Jaeger
• Calibrating the Calibration Tester: Optimal Binning and Minimax Calibration Testing for Continuous Predictive Models
  Alon Kipnis
• Same Graph, Different Likelihoods: Calibration of Autoregressive Graph Generators via Permutation-Equivalent Encodings
  Laurits Fredsgaard, Aaron Thomas, Michael Riis Andersen, Mikkel N. Schmidt, Mahito Sugiyama
• Multi-Class Classification with Abstention Based on Crammer–Singer Surrogate with Linear Growth Rate
  Hongyu Zhang, Han Bao, Junya Honda
• Bounding Worst-Case Calibration Error in OOD Detection Under Distribution Shift
  Claudio Cesar Claros-Olivares, Austin J. Brockmeier
• From Entropy to Calibrated Uncertainty: Training Language Models to Reason About Uncertainty
  Azza Jenane, Nassim Walha, Lukas Kuhn, Florian Buettner
• Bridging the "Predictability Desert": A Probabilistic Bias Correction Framework for AI and Dynamical Subseasonal Forecasts
  Hannah Guan, Soukayna Mouatadid, Paulo Orenstein, Judah Cohen, Haiyu Dong, Genevieve Elaine Flaspohler, Alex Xijie Lu, Jonathan A. Weyn, Lester Mackey
• Threshold Calibration: Making All Large Predicted Probabilities Trustworthy
  Alexandru Lopotenco, Edgar Dobriban
• Auditing the Performance and Calibration of Multi-Modal Large Language Models
  Brendan Kennedy, Lauren Phillips, Sai Munikoti, Sameera Horawalavithana, Ian Stewart, Karl Pazdernik
• Calibration in Context: A Case Study with Score Decompositions
  Johannes Resin
• Calibrated Multivariate Distributional Regression with Pre-Rank Regularization
  Aya Laajil, Elnura Zhalieva, Naomi Desobry, Souhaib Ben Taieb
• Where to Drop: Tuning Monte Carlo Dropout for Uncertainty Calibration in Image Classification
  Lina Benyamina, Emilien Jemelen
• Exploring Geometric Concentration for Quantifying Uncertainty in Scientific Image Caption Generation
  Souradeep Chattopadhyay, Brendan Kennedy, Sai Munikoti, Karl Pazdernik, Soumik Sarkar
• A Variational Estimator for Calibration Errors
  Eugène Berta, Sacha Braun, David Holzmüller, Michael I. Jordan, Francis Bach
• On the Calibration of Isotonic Distributional Regression
  Tobias Biegert, Johannes Resin, Alexander I. Jordan, Sebastian Lerch
• On Sharpness Diagrams
  Alexander I. Jordan
• Dirichlet Calibration Goes Local
  Cesare Barbera, Lorenzo Perini, Giovanni De Toni, Andrea Passerini, Andrea Pugnana
• Online Conformal Prediction via Universal Portfolio Algorithm
  Tuo Liu, Edgar Dobriban, Francesco Orabona
• When Synthetic Data Is Enough: Calibration for Tabular Model Ranking
  Gennadii Filatov, Irina Deeva
• When Does Calibration Matter for Safe Model Routing? Conformal Risk Control Under Imperfect Gate Calibration
  Iqtedar Uddin, Mazin Khider, André Bauer
• Demographic Calibration of Vision-Language Models for Dermatology
  Sonnet Xu, Roxana Daneshjou
• Calibrated Regression-as-Classification for Probabilistic Forecasting
  Jef Jonkers, Glenn Van Wallendael, Luc Duchateau, Sofie Van Hoecke
• Conformal Robust Optimization and Satisficing for Prescriptive Analytics with Black-Box Predictors
  Lingjie Zhao, Hansheng Jiang, Wei Qi
• Reward Calibration Beyond the Convex Hull: Depth-Based Feasibility and Regularized Exponential Tilting for Generative Models
  Manoj Saravanan, Rohit Kumar Salla
• Beyond Accuracy: Controlling Broad Error Types in Selective Classification
  Emilien Jemelen, Sandrine Katsahian, Francisco Orchard, Agathe Guilloux
• Conformal Calibration from Unlabelled Pools
  Kianoosh Ashouritaklimi
• Calibrated Multi-Level Quantile Forecasting
  Tiffany Ding, Isaac Gibbs, Ryan Tibshirani
• Towards a Venn-Abers Calibration Method for Object Detectors
  Bruce Cyusa Mukama, Soundouss Messoudi, Sylvain Rousseau, Sebastien Destercke

Main conference track accepted papers

• On the calibration of survival models with competing risks
  Julie Alberge, Tristan Haugomat, Gaël Varoquaux, Judith Abécassis
• Policy-Oriented Binary Classification: Improving (KD-)CART Final Splits for Subpopulation Targeting
  Bill Wang, Zhenbang Jiao, Fangyi Wang
• Scalable Utility-Aware multiclass calibration
  Mahmoud Hegazy, Michael Jordan, Aymeric Dieuleveut
• Brenier Isotonic Regression
  Han Bao, Amirreza Eshraghi, Yutong Wang
• Patch2Loc: Learning to Localize Patches for Unsupervised Brain Lesion Detection
  Hassan Baker, Austin J. Brockmeier
• Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLMs
  Hen Davidov, Shai Feldman, Gilad Freidkin, Yaniv Romano
• Computationally Lightweight Classifiers with Frequentist Bounds on Prediction Errors
  Shreeram Murali, Cristian Rojas, Dominik Baumann
• Structured Matrix Scaling for Multi-Class Calibration
  Eugène Berta, David Holzmüller, Michael Jordan, Francis Bach
• Panprediction: Optimal Predictions for Any Downstream Task and Loss
  Sivaraman Balakrishnan, Nika Haghtalab, Daniel Hsu, Brian Lee, Eric Zhao
• Regularizing attention scores with bootstrapping
  Neo Christopher Chung, Maxim Laletin
• Multiclass Local Calibration with the Jensen-Shannon Distance
  Cesare Barbera, Lorenzo Perini, Giovanni De Toni, Andrea Passerini, Andrea Pugnana

Conference venue

Hilton Tanger Al Houara
Km 17.5, Route de l'Aéroport, Al Houara, Tangier, 90000, Morocco

Poster printing

Participants are responsible for printing their own posters.

Poster board specifications

• Dimensions: 2.4 m x 1.2 m
• Material: 100% wood
• Fixing method: pins or command strips

Local printing options

• Webprint Typoedit
• Le Printer
• NPG Print