mlsanityData to 7 Oct 2026

Active Learning in Gaussian Process State Space Model

arXiv 2108.00819 ↗First posted 30 Jul 2021cs.LG cs.AI stat.MLcited by 2

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Abstract (arXiv)

We investigate active learning in Gaussian Process state-space models (GPSSM). Our problem is to actively steer the system through latent states by determining its inputs such that the underlying dynamics can be optimally learned by a GPSSM. In order that the most informative inputs are selected, we employ mutual information as our active learning criterion. In particular, we present two approaches for the approximation of mutual information for the GPSSM given latent states. The proposed approaches are evaluated in several physical systems where we actively learn the underlying non-linear dynamics represented by the state-space model.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 16 of its 58 references that are in the map. First posted on arXiv 30 Jul 2021.

Builds on

16 of 58 references.

References · 16

  1. 2017Deep Bayesian Active Learning with Image Datacited by 812
  2. 2019On Variational Bounds of Mutual Informationcited by 550
  3. 2019BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learningcited by 428
  4. 2017Safe Model-based Reinforcement Learning with Stability Guaranteescited by 347
  5. 2018Formal Limitations on the Measurement of Mutual Informationcited by 189
  6. 2016GPflow: A Gaussian process library using TensorFlowcited by 139
  7. 2019Deep Active Learning: Unified and Principled Method for Query and Trainingcited by 92
  8. 2018Probabilistic Recurrent State-Space Modelscited by 64
  9. 2015On Sparse variational methods and the Kullback-Leibler divergence between stochastic processescited by 56
  10. 2014Variational Gaussian Process State-Space Modelscited by 43
  11. 2017Identification of Gaussian Process State Space Modelscited by 31
  12. 2019Actively Learning Gaussian Process Dynamicscited by 26
4 more
  1. 2013Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMCcited by 24
  2. 2020Localized active learning of Gaussian process state space modelscited by 8
  3. 2020Mutual Information Maximization for Robust Plannable Representationscited by 7
  4. 2018Closed-form Inference and Prediction in Gaussian Process State-Space Modelscited by 1

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Citing papers per year: 2024: 2.

2 papers.

  1. 2024Future Aware Safe Active Learning of Time Varying Systems using Gaussian Processescited by 1
  2. 2024Batch Active Learning in Gaussian Process Regression using Derivativescited by 0