Cover Image

Sparse Gaussian Processes: Structured Approximations and Power-EP Revisited

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bui, Thang D., Titsias, Michalis K.
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

New Bounds for Sparse Variational Gaussian Processes
by: Titsias, Michalis K.
Published: (2025)

Sparse Orthogonal Variational Inference for Gaussian Processes
by: Shi, Jiaxin, et al.
Published: (2019)

Demystifying Diffusion Objectives: Reweighted Losses are Better Variational Bounds
by: Shi, Jiaxin, et al.
Published: (2025)

Gaussian Invariant Markov Chain Monte Carlo
by: Titsias, Michalis K., et al.
Published: (2025)

Likelihood approximations via Gaussian approximate inference
by: Bui, Thang D.
Published: (2024)

Personalized Federated Learning with Exact Stochastic Gradient Descent
by: Nikoloutsopoulos, Sotirios, et al.
Published: (2022)

Variance Reduction for the Independent Metropolis Sampler
by: Liu, Siran, et al.
Published: (2024)

Simplified and Generalized Masked Diffusion for Discrete Data
by: Shi, Jiaxin, et al.
Published: (2024)

Gradient Estimation with Discrete Stein Operators
by: Shi, Jiaxin, et al.
Published: (2022)

Sparse Autoencoders Enable Scalable and Reliable Circuit Identification in Language Models
by: O'Neill, Charles, et al.
Published: (2024)

Tighter sparse variational Gaussian processes
by: Bui, Thang D., et al.
Published: (2025)

Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models
by: Rannen-Triki, Amal, et al.
Published: (2024)

Learning-Order Autoregressive Models with Application to Molecular Graph Generation
by: Wang, Zhe, et al.
Published: (2025)

Beyond ReinMax: Low-Variance Gradient Estimators for Discrete Latent Variables
by: Wang, Daniel, et al.
Published: (2026)

Revisiting Kernel Attention with Correlated Gaussian Process Representation
by: Bui, Long Minh, et al.
Published: (2025)

The Illusion of Stochasticity in LLMs
by: Gu, Xiangming, et al.
Published: (2026)

Kalman Filter for Online Classification of Non-Stationary Data
by: Titsias, Michalis K., et al.
Published: (2023)

Towards Credit-Fraud Detection via Sparsely Varying Gaussian Approximations
by: Sharma, Harshit, et al.
Published: (2020)

Sparse Gaussian Neural Processes
by: Rochussen, Tommy, et al.
Published: (2025)

Variational Learning for Insertion-based Generation
by: Zhang, Yangtian, et al.
Published: (2026)

Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset
by: Galashov, Alexandre, et al.
Published: (2024)

Power Flow Approximations for Multiphase Distribution Networks using Gaussian Processes
by: Glover, Daniel, et al.
Published: (2025)

Grokking Beyond Neural Networks: An Empirical Exploration with Model Complexity
by: Miller, Jack, et al.
Published: (2023)

Calibrating Transformers via Sparse Gaussian Processes
by: Chen, Wenlong, et al.
Published: (2023)

Inverse-Free Sparse Variational Gaussian Processes
by: Cortinovis, Stefano, et al.
Published: (2026)

Fast Gaussian Process Approximations for Autocorrelated Data
by: Chokhachian, Ahmadreza, et al.
Published: (2025)

Recommendations for Baselines and Benchmarking Approximate Gaussian Processes
by: Ober, Sebastian W., et al.
Published: (2024)

Sparse Techniques for Regression in Deep Gaussian Processes
by: Latz, Jonas, et al.
Published: (2025)

Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes
by: Wei, Yunyue, et al.
Published: (2024)

Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings
by: Grewal, Yashvir S., et al.
Published: (2024)

Rao-Blackwellised Reparameterisation Gradients
by: Lam, Kevin H., et al.
Published: (2025)

Scalable Bayesian Optimization with Sparse Gaussian Process Models
by: Yang, Ang
Published: (2020)

Symbolic Regression on Sparse and Noisy Data with Gaussian Processes
by: Hsin, Junette, et al.
Published: (2023)

Revisiting the Equivalence of Bayesian Neural Networks and Gaussian Processes: On the Importance of Learning Activations
by: Sendera, Marcin, et al.
Published: (2024)

On Additive Gaussian Processes for Wind Farm Power Prediction
by: Brealy, Simon M., et al.
Published: (2026)

Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project
by: Rathore, Pratik, et al.
Published: (2025)

On the Laplace Approximation as Model Selection Criterion for Gaussian Processes
by: Besginow, Andreas, et al.
Published: (2024)

Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process Regression
by: Zou, Lu, et al.
Published: (2024)

Gaussian Processes Sampling with Sparse Grids under Additive Schwarz Preconditioner
by: Chen, Haoyuan, et al.
Published: (2024)

Energy Scale Degradation in Sparse Quantum Solvers: A Barrier to Quantum Utility
by: Dinh, Thang N., et al.
Published: (2025)