mlsanityData to 14 Oct 2026

Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification

arXiv 2205.15008 ↗First posted 30 May 2022cs.LG stat.MLcited by 7

Not on the map. Why?

Abstract (arXiv)

This work introduces the Efficient Transformed Gaussian Process (ETGP), a new way of creating C stochastic processes characterized by: 1) the C processes are non-stationary, 2) the C processes are dependent by construction without needing a mixing matrix, 3) training and making predictions is very efficient since the number of Gaussian Processes (GP) operations (e.g. inverting the inducing point's covariance matrix) do not depend on the number of processes. This makes the ETGP particularly suited for multi-class problems with a very large number of classes, which are the problems studied in this work. ETGPs exploit the recently proposed Transformed Gaussian Process (TGP), a stochastic process specified by transforming a Gaussian Process using an invertible transformation. However, unlike TGPs, ETGPs are constructed by transforming a single sample from a GP using C invertible transformations. We derive an efficient sparse variational inference algorithm for the proposed model and demonstrate its utility in 5 classification tasks which include low/medium/large datasets and a different number of classes, ranging from just a few to hundreds. Our results show that ETGPs, in general, outperform state-of-the-art methods for multi-class classification based on GPs, and have a lower computational cost (around one order of magnitude smaller).

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 25 of its 60 references that are in the map. First posted on arXiv 30 May 2022.

Builds on

25 of 60 references.

References · 25

  1. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  2. 2017On Calibration of Modern Neural Networkscited by 4,960
  3. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  4. 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 609
  5. 2015Deep Kernel Learningcited by 445
  6. 2013Gaussian Processes for Big Datacited by 383
  7. 2015Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networkscited by 295
  8. 2014Scalable Variational Gaussian Process Classificationcited by 228
  9. 2017Concrete Dropoutcited by 227
  10. 2019Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processescited by 151
  11. 2017Doubly Stochastic Variational Inference for Deep Gaussian Processescited by 143
  12. 2016GPflow: A Gaussian process library using TensorFlowcited by 140
13 more
  1. 2013Structure Discovery in Nonparametric Regression through Compositional Kernel Searchcited by 88
  2. 2014Manifold Gaussian Processes for Regressioncited by 80
  3. 2014Input Warping for Bayesian Optimization of Non-stationary Functionscited by 67
  4. 2020A Framework for Interdomain and Multioutput Gaussian Processescited by 48
  5. 2017Convolutional Gaussian Processescited by 44
  6. 2021TabularNet: A Neural Network Architecture for Understanding Semantic Structures of Tabular Datacited by 36
  7. 2015Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlocited by 29
  8. 2020Transforming Gaussian Processes With Normalizing Flowscited by 28
  9. 2019Scalable Exact Inference in Multi-Output Gaussian Processescited by 25
  10. 2019Compositionally-Warped Gaussian Processescited by 14
  11. 2019Multi-resolution Multi-task Gaussian Processescited by 11
  12. 2019Neural Likelihoods for Multi-Output Gaussian Processescited by 4
  13. 2020Transport Gaussian Processes for Regressioncited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 0; 2025: 1; 2026: 1.

7 papers.

  1. 2022Markovian Gaussian Process Variational Autoencoderscited by 16
  2. 2023Bayesian Gaussian Process ODEs via Double Normalizing Flowscited by 7
  3. 2023Towards Flexibility and Interpretability of Gaussian Process State-Space Modelcited by 5
  4. 2023Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Modelscited by 3
  5. 2025Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systemscited by 2
  6. 2023Deep Transformed Gaussian Processescited by 1
  7. 2026Simplex-to-Euclidean Bijection for Conjugate and Calibrated Multiclass Gaussian Process Classificationcited by 0