Sparse Gaussian Processes

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9 papers1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

On Basis Function Selection for Sparse Gaussian Process Regression
Sep 22, 2026Marnix Van Soom, Ivan De BoiFeature Selection Sparse Gaussian Processes
AI Lab Vrije Universiteit Brussel · InViLab University of Antwerp
Video Captioning in Low-Light Conditions through Efficient Uncertainty-Aware Caption Correction
Sep 18, 2026Arefeh RezaeiSparse Gaussian Processes Video Captioning
Faculty of Computer Engineering, K.N. Toosi University of Technology, Tehran, Iran
Triply-Scalable Equivariant Gaussian Process Modeling
Sep 17, 2026Tim Steinert, David GinsbourgerSparse Gaussian Processes Gaussian Processes
Institute of Mathematical Statistics and Actuarial Science, University of Bern, Bern, Switzerland
Sequential sparse Gaussian process quantile regression
Jun 30, 2026Hugo Nicolas, Olivier Le MaîtreQuantile Regression Uncertainty Quantification
PLATON, CMAP · Inria, Center for Applied Mathematics, ´Ecole polytechnique, Institut Polytechnique de Paris, Palaiseau, France · CNRS, Center for Applied Mathematics, ´Ecole polytechnique, Institut Polytechnique de Paris, Palaiseau, France
SIKA-GP: Accelerating Gaussian Process Inference with Sparse Inducing Kernel Approximations for Bayesian Deep Learning
May 26, 2026Wenyuan Zhao, Rui Tuo, Chao TianBayesian Neural Networks Gaussian Processes
Department of Electrical and Computer Engineering, Texas A&M University, College Station, US · Department of Industrial and Systems Engineering, Texas A&M University, College Station, US
Collaborative Navigation and Exploration with $β$ -Sparse Gaussian Processes
May 25, 2026Evangelos Psomiadis, Dipankar Maity, Panagiotis TsiotrasMulti-Robot Systems Robot Navigation
D. Guggenheim School of Aerospace Engineering, Georgia Tech, Atlanta, GA, USA · Department of Electrical and Computer Engineering, UNC Charlotte, Charlotte, NC, USA
A Terrain-Adaptive epsilon-Constraint MPC for Uneven Terrain Kinodynamic Planning
May 20, 2026Otobong Jerome, Geesara Kalathunga, Tiago NascimentoConstrained Motion Planning Kinodynamic Motion Planning
Laborat´orio de Engenharia de Sistemas e Rob´otica, Universidade Federal da Para´ıba (UFPB), Jo˜ao Pessoa, Para´ıba, 58055-000, Brazil · School of Computer Science, University of Lincoln, Lincoln, United Kingdom
Inverse-Free Sparse Variational Gaussian Processes
Apr 1, 2026Stefano Cortinovis, Laurence Aitchison, Stefanos Eleftheriadis +1Sparse Gaussian Processes Gaussian Processes
University of Oxford · University of Bristol
A Distributed Gaussian Process Model for Multi-Robot Mapping
Mar 7, 2026Seth Nabarro, Mark van der Wilk, Andrew J. DavisonSparse Gaussian Processes Gaussian Belief Propagation
Dyson Robotics Lab, Imperial College London · Department of Computer Science, University of Oxford

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On Basis Function Selection for Sparse Gaussian Process Regression

Marnix Van Soom, Ivan De Boi

AI Lab Vrije Universiteit Brussel · InViLab University of Antwerp

Feature Selection Sparse Gaussian Processes Gaussian Process Regression

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Sparse Gaussian processes achieve

O(N)

inference by replacing the kernel with an appropriate expansion in a fixed basis

\{φ_j\}

on the input space. Given a compute budget

M \ll N

, practitioners conventionally truncate the basis to its first

M

entries. Nothing in the formalism, however, prevents one from selecting only those

M

basis functions that matter for the data at hand. This would avoid spending budget on basis functions where there is no signal, but it requires a criterion for ranking the candidates. We propose three such criteria derived from an information-theoretic view of the basis-function selection problem. Each criterion matches a different state of knowledge at selection time: a no-data state, a no-prior state, and an in-between state. We then study the performance of truncation versus selection strategies on six UCI regression benchmarks across three basis families: Hilbert-space Gaussian processes (HSGP), variational Fourier features (VFF), and variational inducing spherical harmonics (VISH). We observe that the no-data criterion is a safe default, matching or improving on truncation for HSGP, VFF and VISH, with substantial gains for VISH and improvements over a recently developed selection heuristic for that basis family. The data-aware no-prior and in-between criteria provide substantial gains over truncation specifically for HSGP, which is the most broadly used of the three families in practice.