mlsanityData to 7 Oct 2026

Deep Sigma Point Processes

arXiv 2002.09112 ↗First posted 21 Feb 2020stat.ML cs.LGcited by 16

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Abstract (arXiv)

We introduce Deep Sigma Point Processes, a class of parametric models inspired by the compositional structure of Deep Gaussian Processes (DGPs). Deep Sigma Point Processes (DSPPs) retain many of the attractive features of (variational) DGPs, including mini-batch training and predictive uncertainty that is controlled by kernel basis functions. Importantly, since DSPPs admit a simple maximum likelihood inference procedure, the resulting predictive distributions are not degraded by any posterior approximations. In an extensive empirical comparison on univariate and multivariate regression tasks we find that the resulting predictive distributions are significantly better calibrated than those obtained with other probabilistic methods for scalable regression, including variational DGPs--often by as much as a nat per datapoint.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Feb 2020.

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Citing papers per year: 2021: 3; 2022: 2; 2023: 0; 2024: 4; 2025: 5; 2026: 2.

16 papers.

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  1. 2025Stochastic Process Learning via Operator Flow Matchingcited by 14
  2. 2024Universal Functional Regression with Neural Operator Flowscited by 9
  3. 2021Scalable Cross Validation Losses for Gaussian Process Modelscited by 6
  4. 2021Bayesian Meta-Learning Through Variational Gaussian Processescited by 3
  5. 2022Gaussian Process Latent Variable Modeling for Few-shot Time Series Forecastingcited by 3
  6. 2025dynoGP: Deep Gaussian Processes for dynamic system identificationcited by 3
  7. 2021Uncertainty-aware Remaining Useful Life predictorcited by 2
  8. 2024Amortized Variational Inference for Deep Gaussian Processescited by 1
  9. 2025Evaluating Uncertainty in Deep Gaussian Processescited by 1
  10. 2022Photoelectric Factor Prediction Using Automated Learning and Uncertainty Quantificationcited by 0
  11. 2024Gradient-enhanced deep Gaussian processes for multifidelity modellingcited by 0
  12. 2024Deep Q-Exponential Processescited by 0
4 more
  1. 2025A Physics-Guided Probabilistic Surrogate Modeling Framework for Digital Twins of Underwater Radiated Noisecited by 0
  2. 2025Deep Gaussian Process Proximal Policy Optimizationcited by 0
  3. 2026Beliefs Beyond Posteriors: The Predictive Cost of ELBO Bindingcited by 0
  4. 2026Deep Sigma Point Processes for RCS Modeling in Spaceborne SAR Imagerycited by 0