Deep Gaussian Processes

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10 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Efficient Deep Gaussian Process Surrogates via Mini-Batch Latent MAP Estimation
Oct 27, 2026Chun-Yi Chang, Chih-Li SungDeep Gaussian Processes Surrogate Modeling
Michigan State University
Continuous-depth Deep Gaussian Processes
Oct 22, 2026Ying Li, Sanyou Wu, Zi Yang +6Deep Gaussian Processes Gaussian Process Regression
National University of Singapore · Columbia University · The University of Hong Kong +3
Generalized Splines and Gaussian Processes
Aug 28, 2026Michael UnserDeep Gaussian Processes Gaussian Processes
Biomedical Imaging Group, École polytechnique fédérale de Lausanne (EPFL), Station 17, CH-1015, Lausanne, Switzerland
Enhancing Bayesian Optimization and Active Learning Through Kernel Diversity
Aug 25, 2026Heng Zhang, Haotian Xiang, Konstantinos D. Polyzos +2Bayesian Optimization Deep Gaussian Processes
University of Georgia, Athens, GA, USA · University of California San Diego, La Jolla, CA, USA
A Bayesian Framework for Built-in Input Dimension Reduction for Gaussian Process Modeling
Jul 21, 2026Eric Herrison Gyamfi, Emily L. Kang, Bledar A. Konomi +1Bayesian Inference Deep Gaussian Processes
Division of Statistics and Data Science, Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH · Department of Mathematics, Purdue University, West Lafayette, IN
Deep Gaussian Processes on Directed Acyclic Graphs
Jul 10, 2026Federico L. Perlino, Oliver Hamelijnck, Adam M. Johansen +1Surrogate Modeling Deep Gaussian Processes
Department of Statistics, University of Warwick · 2AI Team, Unilink Software Ltd · Department of Computer Science, University of Warwick
An Analysis of Posterior Collapse, Parameterization and Initialization in Variational Deep Gaussian Processes
Jun 24, 2026Francisco Javier Sáez-Maldonado, Juan Maroñas, Daniel Hernández-LobatoDeep Learning Optimization Posterior Collapse
Department of Computer Science and Artificial Intelligence Universidad de Granada Granada, Spain · Department of Quantitative Methods CUNEF Universidad Madrid, Spain · Machine Learning Group at Universidad Autónoma de Madrid +1
How Deep Are Deep GPs, Really? A Sharp Threshold and a Non-Gaussian Limit for Compositional GPs
Jun 6, 2026Mark Kozdoba, Shie MannorDeep Gaussian Processes Gaussian Processes
Technion, IIT · Technion, IIT and NVIDIA
Onsager-Machlup Posterior Transport for Deep Gaussian Processes
May 22, 2026Jian Xu, Delu Zeng, John Paisley +1Deep Gaussian Processes Gaussian Processes
RIKEN iTHEMS · RIKEN AIP · South China University of Technology +1
Deep Jump Gaussian Processes for Surrogate Modeling of High-Dimensional Piecewise Continuous Functions
Oct 24, 2025Yang Xu, Chiwoo ParkDeep Gaussian Processes Surrogate Modeling
Industrial & Systems Engineering, University of Washington, Seattle, WA, USA

Oct 27, 2026J/K move · Enter open · S save

Efficient Deep Gaussian Process Surrogates via Mini-Batch Latent MAP Estimation

Chun-Yi Chang, Chih-Li Sung

Michigan State University

Deep Gaussian Processes Surrogate Modeling MAP Estimation

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Deep Gaussian processes (DGPs) are flexible surrogate models for computer experiments with nonstationary response surfaces, but inference over their latent representations remains computationally challenging. Existing methods typically rely on variational inference or Markov chain Monte Carlo (MCMC), which impose variational-family assumptions or require costly posterior sampling. We propose the latent maximum-a-posteriori deep Gaussian process (LaMP-DGP), which uses gradient-based optimization to obtain MAP estimates of the latent representations and enables efficient probabilistic prediction through linked-GP calculations. To scale LaMP-DGP to large datasets, we develop a mini-batch optimization algorithm and, under a polynomial mini-batch bias-decay condition, establish a stationarity bound for latent-state optimization that motivates a growing batch-size schedule. Across numerical experiments and real-data analyses, LaMP-DGP provides consistently competitive predictive performance relative to doubly stochastic variational inference, stochastic gradient Hamiltonian Monte Carlo, and fully Bayesian Vecchia DGP inference. Computationally, LaMP-DGP is substantially faster than the Vecchia MCMC approach while remaining broadly competitive with the stochastic-gradient alternatives. These results position LaMP-DGP as a competitive and computationally efficient alternative for DGP inference. An R package, "LaMPDGP", is available for implementing the proposed method.