mlsanityData to 7 Oct 2026

Class Adaptive Conformal Training

arXiv 2601.09522 ↗First posted 14 Jan 2026cs.LG cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Deep neural networks have achieved remarkable success across a variety of tasks, yet they often suffer from unreliable probability estimates. As a result, they can be overconfident in their predictions. Conformal Prediction (CP) offers a principled framework for uncertainty quantification, yielding prediction sets with rigorous coverage guarantees. Existing conformal training methods optimize for overall set size, but shaping the prediction sets in a class-conditional manner is not straightforward and typically requires prior knowledge of the data distribution. In this work, we introduce Class Adaptive Conformal Training (CaCT), which formulates conformal training as an augmented Lagrangian optimization problem that adaptively learns to shape prediction sets class-conditionally without making any distributional assumptions. Experiments on multiple benchmark datasets, including standard and long-tailed image recognition as well as text classification, demonstrate that CaCT consistently outperforms prior conformal training methods, producing significantly smaller and more informative prediction sets while maintaining the desired coverage guarantees.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 45 references that are in the map. First posted on arXiv 14 Jan 2026.

Builds on

27 of 45 references.

References · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  4. 2019Learning Imbalanced Datasets with Label-Distribution-Aware Margin Losscited by 987
  5. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 777
  6. 2019Large-Scale Long-Tailed Recognition in an Open Worldcited by 704
  7. 2019Conformalized Quantile Regressioncited by 620
  8. 2020Classification with Valid and Adaptive Coveragecited by 390
  9. 2020Uncertainty Sets for Image Classifiers using Conformal Predictioncited by 365
  10. 2016Least Ambiguous Set-Valued Classifiers with Bounded Error Levelscited by 270
  11. 2024Benchmarking LLMs via Uncertainty Quantificationcited by 124
  12. 2019Stochastic Optimization of Sorting Networks via Continuous Relaxationscited by 111
15 more
  1. 2023Class-Conditional Conformal Prediction with Many Classescited by 109
  2. 2021Learning Optimal Conformal Classifierscited by 108
  3. 2023A Closer Look at the Few-Shot Adaptation of Large Vision-Language Modelscited by 67
  4. 2022Training Uncertainty-Aware Classifiers with Conformalized Deep Learningcited by 62
  5. 2020Augmented Lagrangian Adversarial Attackscited by 28
  6. 2021Optimized conformal classification using gradient descent approximationcited by 27
  7. 2022Monotonic Differentiable Sorting Networkscited by 25
  8. 2024An Information Theoretic Perspective on Conformal Predictioncited by 21
  9. 2021Constrained Optimization to Train Neural Networks on Critical and Under-Represented Classescited by 19
  10. 2024Conformal Semantic Image Segmentation: Post-hoc Quantification of Predictive Uncertaintycited by 19
  11. 2023Kandinsky Conformal Prediction: Efficient Calibration of Image Segmentation Algorithmscited by 14
  12. 2025Conformal Prediction for Zero-Shot Modelscited by 14
  13. 2022Class Adaptive Network Calibrationcited by 13
  14. 2024Are foundation models for computer vision good conformal predictors?cited by 12
  15. 2025Direct Prediction Set Minimization via Bilevel Conformal Classifier Trainingcited by 8

Built on by

Not cited yet.