mlsanityData to 14 Oct 2026

Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification

arXiv 1805.10915 ↗First posted 28 May 2018cs.LG stat.MLcited by 51

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Abstract (arXiv)

In this paper, we study the problem of deriving fast and accurate classification algorithms with uncertainty quantification. Gaussian process classification provides a principled approach, but the corresponding computational burden is hardly sustainable in large-scale problems and devising efficient alternatives is a challenge. In this work, we investigate if and how Gaussian process regression directly applied to the classification labels can be used to tackle this question. While in this case training time is remarkably faster, predictions need be calibrated for classification and uncertainty estimation. To this aim, we propose a novel approach based on interpreting the labels as the output of a Dirichlet distribution. Extensive experimental results show that the proposed approach provides essentially the same accuracy and uncertainty quantification of Gaussian process classification while requiring only a fraction of computational resources.

Key facts. Cited by 51 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 May 2018.

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Citing papers per year: 2021: 10; 2022: 10; 2023: 9; 2024: 6; 2025: 6; 2026: 10.

51 papers.

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  1. 2022Efficient Dataset Distillation Using Random Feature Approximationcited by 111
  2. 2021Classifier Calibration: A survey on how to assess and improve predicted class probabilitiescited by 73
  3. 2021Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibrationcited by 48
  4. 2022On Uncertainty, Tempering, and Data Augmentation in Bayesian Classificationcited by 46
  5. 2021Meta-Cal: Well-controlled Post-hoc Calibration by Rankingcited by 33
  6. 2023Looping in the Human Collaborative and Explainable Bayesian Optimizationcited by 27
  7. 2022T-Cal: An optimal test for the calibration of predictive modelscited by 24
  8. 2021Kernel Interpolation for Scalable Online Gaussian Processescited by 18
  9. 2021Active Learning in Bayesian Neural Networks with Balanced Entropy Learning Principlecited by 14
  10. 2023Beyond In-Domain Scenarios: Robust Density-Aware Calibrationcited by 14
  11. 2022Improved Random Features for Dot Product Kernelscited by 12
  12. 2023Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimationcited by 11
38 more
  1. 2023Gaussian Process Probes (GPP) for Uncertainty-Aware Probingcited by 11
  2. 2024FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learningcited by 11
  3. 2022Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picturecited by 10
  4. 2022Multi-Instance Partial-Label Learning: Towards Exploiting Dual Inexact Supervisioncited by 9
  5. 2021Deep Classifiers with Label Noise Modeling and Distance Awarenesscited by 8
  6. 2024A Safe Reinforcement Learning driven Weights-varying Model Predictive Control for Autonomous Vehicle Motion Controlcited by 7
  7. 2023Guided Deep Kernel Learningcited by 6
  8. 2023Adaptive Batch Sizes for Active Learning A Probabilistic Numerics Approachcited by 6
  9. 2022Analytic Mutual Information in Bayesian Neural Networkscited by 5
  10. 2021Laplace Matching for fast Approximate Inference in Latent Gaussian Modelscited by 4
  11. 2023Online Decision Mediationcited by 4
  12. 2021Gaussian Processes with Differential Privacycited by 3
  13. 2021Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian Networkscited by 3
  14. 2024Regularized KL-Divergence for Well-Defined Function-Space Variational Inference in Bayesian neural networkscited by 3
  15. 2024Safety Monitoring of Machine Learning Perception Functions: a Surveycited by 3
  16. 2022SLOVA: Uncertainty Estimation Using Single Label One-Vs-All Classifiercited by 2
  17. 2023Materials Expert-Artificial Intelligence for Materials Discoverycited by 2
  18. 2025Learning Hyperparameters via a Data-Emphasized Variational Objectivecited by 2
  19. 2025Sparse Gaussian Neural Processescited by 2
  20. 2025h-calibration: Rethinking Classifier Recalibration with Probabilistic Error-Bounded Objectivecited by 2
  21. 2026Concepts Worth Having: Refining VLM-Guided Concept Bottleneck Models with Minimal Annotationscited by 2
  22. 2022Distributional Gaussian Processes Layers for Out-of-Distribution Detectioncited by 1
  23. 2022Counterfactual Learning with Multioutput Deep Kernelscited by 1
  24. 2024Likelihood approximations via Gaussian approximate inferencecited by 1
  25. 2025CODEI: Resource-Efficient Task-Driven Co-Design of Perception and Decision Making for Mobile Robots Applied to Autonomous Vehiclescited by 1
  26. 2025Few-Shot Speech Deepfake Detection Adaptation with Gaussian Processescited by 1
  27. 2025Does Calibration Affect Human Actions?cited by 1
  28. 2026Human-in-the-Loop Meta Bayesian Optimization for Fusion Energy and Scientific Applicationscited by 1
  29. 2021Probability-Generating Function Kernels for Spherical Datacited by 0
  30. 2023Function-Space Regularization for Deep Bayesian Classificationcited by 0
  31. 2024Efficient Two-Stage Gaussian Process Regression Via Automatic Kernel Search and Subsamplingcited by 0
  32. 2026Function-Space Empirical Bayes Regularisation with Large Vision-Language Model Priorscited by 0
  33. 2026Function-Space Empirical Bayes Regularisation with Student's t Priorscited by 0
  34. 2026Simplex-to-Euclidean Bijection for Conjugate and Calibrated Multiclass Gaussian Process Classificationcited by 0
  35. 2026Scalable Gaussian process inference via neural feature mapscited by 0
  36. 2026Supervised Distributional Reduction via Optimal Transport and Dependence Maximizationcited by 0
  37. 2026Rethinking Post-Hoc Calibration in Semantic Segmentationcited by 0
  38. 2026On the Brittleness of Maximum Likelihood Estimation for Gaussian Process Hyperparameter Optimizationcited by 0

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