mlsanityData to 7 Oct 2026

Output-Dependent Gaussian Process State-Space Model

arXiv 2212.07608 ↗First posted 15 Dec 2022cs.LG cs.SY eess.SPcited by 6

Not on the map. Why?

Abstract (arXiv)

Gaussian process state-space model (GPSSM) is a fully probabilistic state-space model that has attracted much attention over the past decade. However, the outputs of the transition function in the existing GPSSMs are assumed to be independent, meaning that the GPSSMs cannot exploit the inductive biases between different outputs and lose certain model capacities. To address this issue, this paper proposes an output-dependent and more realistic GPSSM by utilizing the well-known, simple yet practical linear model of coregionalization (LMC) framework to represent the output dependency. To jointly learn the output-dependent GPSSM and infer the latent states, we propose a variational sparse GP-based learning method that only gently increases the computational complexity. Experiments on both synthetic and real datasets demonstrate the superiority of the output-dependent GPSSM in terms of learning and inference performance.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 12 of its 28 references that are in the map. First posted on arXiv 15 Dec 2022.

Builds on

12 of 28 references.

References · 12

  1. 2019An Introduction to Variational Autoencoderscited by 997
  2. 2013Gaussian Processes for Big Datacited by 377
  3. 2016Structured Inference Networks for Nonlinear State Space Modelscited by 182
  4. 2018Probabilistic Recurrent State-Space Modelscited by 64
  5. 2014Variational Gaussian Process State-Space Modelscited by 43
  6. 2017Identification of Gaussian Process State Space Modelscited by 31
  7. 2013Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMCcited by 24
  8. 2019Overcoming Mean-Field Approximations in Recurrent Gaussian Process Modelscited by 20
  9. 2022Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modelingcited by 12
  10. 2019Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Seriescited by 9
  11. 2018Multitask Gaussian Process with Hierarchical Latent Interactionscited by 4
  12. 2021Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systemscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 1; 2025: 1; 2026: 1.

6 papers.

  1. 2023Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inferencecited by 5
  2. 2023Towards Flexibility and Interpretability of Gaussian Process State-Space Modelcited by 4
  3. 2024Regularization-Based Efficient Continual Learning in Deep State-Space Modelscited by 4
  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. 2026Simulation-Free Learning of GP-SDEs from Irregular Observationscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Recursive Gaussian Process State Space Model
  2. 2025Maximum Likelihood Learning of Latent Dynamics Without Reconstruction
  3. 2021Traversing Time with Multi-Resolution Gaussian Process State-Space Models
  4. 2019Sequential Gaussian Processes for Online Learning of Nonstationary Functions
  5. 2024From Generalization Analysis to Optimization Designs for State Space Models
  6. 2024Time Series Clustering with General State Space Models via Stochastic Variational Inference
  7. 2026Conditioning Gaussian Processes on Almost Anything
  8. 2022Linear Variational State-Space Filtering