mlsanityData to 7 Oct 2026

Simplex-to-Euclidean Bijection for Conjugate and Calibrated Multiclass Gaussian Process Classification

arXiv 2603.16621 ↗First posted 17 Mar 2026cs.LGcited by 0

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Abstract (arXiv)

We propose a conjugate and calibrated Gaussian process (GP) model for multi-class classification by exploiting the geometry of the probability simplex. Our approach uses Aitchison geometry to map simplex-valued class probabilities to an unconstrained Euclidean representation, turning classification into a GP regression problem with fewer latent dimensions than standard multi-class GP classifiers. This yields conjugate inference and reliable predictive probabilities without relying on distributional approximations in the model construction. The method is compatible with standard sparse GP regression techniques, enabling scalable inference on larger datasets. Empirical results show well-calibrated and competitive performance across synthetic and real-world datasets.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 29 of its 44 references that are in the map. First posted on arXiv 17 Mar 2026.

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References · 29

  1. 2017On Calibration of Modern Neural Networkscited by 4,860
  2. 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 603
  3. 2013Gaussian Processes for Big Datacited by 377
  4. 2014Scalable Variational Gaussian Process Classificationcited by 226
  5. 2015Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)cited by 175
  6. 2019Exact Gaussian Processes on a Million Data Pointscited by 118
  7. 2016Variational Fourier features for Gaussian processescited by 85
  8. 2020Pathwise Conditioning of Gaussian Processescited by 63
  9. 2018Dirichlet-based Gaussian Processes for Large-scale Calibrated Classificationcited by 50
  10. 2015MCMC for Variationally Sparse Gaussian Processescited by 44
  11. 2018Product Kernel Interpolation for Scalable Gaussian Processescited by 31
  12. 2021GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learningcited by 26
17 more
  1. 2022Posterior and Computational Uncertainty in Gaussian Processescited by 23
  2. 2021Preconditioning for Scalable Gaussian Process Hyperparameter Optimizationcited by 22
  3. 2018Deep convolutional Gaussian processescited by 21
  4. 2019Bayesian Image Classification with Deep Convolutional Gaussian Processescited by 21
  5. 2018Orthogonally Decoupled Variational Gaussian Processescited by 19
  6. 2018Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentationcited by 17
  7. 2019Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentationcited by 16
  8. 2023Stochastic Gradient Descent for Gaussian Processes Done Rightcited by 16
  9. 2019Adversarial Robustness Guarantees for Classification with Gaussian Processescited by 11
  10. 2020Multi-class Gaussian Process Classification with Noisy Inputscited by 10
  11. 2015Scalable Gaussian Process Classification via Expectation Propagationcited by 8
  12. 2022Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classificationcited by 7
  13. 2017Scalable Multi-Class Gaussian Process Classification using Expectation Propagationcited by 5
  14. 2025SketchDNN: Joint Continuous-Discrete Diffusion for CAD Sketch Generationcited by 5
  15. 2025Flows on convex polytopescited by 3
  16. 2025Simplex-to-Euclidean Bijections for Categorical Flow Matchingcited by 3
  17. 2019Scalable Gaussian Process Classification with Additive Noise for Various Likelihoodscited by 1

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