Image de couverture de livre

Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Wei, Yunyue, Zhuang, Vincent, Soedarmadji, Saraswati, Sui, Yanan
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Safe Bayesian Optimization for the Control of High-Dimensional Embodied Systems
par: Wei, Yunyue, et autres
Publié: (2024)

Scalable Exploration for High-Dimensional Continuous Control via Value-Guided Flow
par: Wei, Yunyue, et autres
Publié: (2026)

Embodied Learning of Reward for Musculoskeletal Control with Vision Language Models
par: Soedarmadji, Saraswati, et autres
Publié: (2025)

An Invariant Information Geometric Method for High-Dimensional Online Optimization
par: Zhang, Zhengfei, et autres
Publié: (2024)

Improving sample efficiency of high dimensional Bayesian optimization with MCMC
par: Yi, Zeji, et autres
Publié: (2024)

Scalable Bayesian Optimization with Sparse Gaussian Process Models
par: Yang, Ang
Publié: (2020)

Sparse Gaussian Neural Processes
par: Rochussen, Tommy, et autres
Publié: (2025)

Motion Control of High-Dimensional Musculoskeletal Systems with Hierarchical Model-Based Planning
par: Wei, Yunyue, et autres
Publié: (2025)

Regime-Adaptive Bayesian Optimization via Dirichlet Process Mixtures of Gaussian Processes
par: Zhang, Yan, et autres
Publié: (2026)

Population Transformer: Learning Population-level Representations of Neural Activity
par: Chau, Geeling, et autres
Publié: (2024)

Pre-trained Gaussian Processes for Bayesian Optimization
par: Wang, Zi, et autres
Publié: (2021)

Calibrating Transformers via Sparse Gaussian Processes
par: Chen, Wenlong, et autres
Publié: (2023)

Quantum Gaussian Process Regression for Bayesian Optimization
par: Rapp, Frederic, et autres
Publié: (2023)

Learning to Focus: Focal Attention for Selective and Scalable Transformers
par: Ram, Dhananjay, et autres
Publié: (2025)

Generative Bayesian Computation as a Scalable Alternative to Gaussian Process Surrogates
par: Polson, Nick, et autres
Publié: (2026)

Wasserstein Barycenter Gaussian Process based Bayesian Optimization
par: Candelieri, Antonio, et autres
Publié: (2025)

Scalable Bayesian Inference in the Era of Deep Learning: From Gaussian Processes to Deep Neural Networks
par: Antoran, Javier
Publié: (2024)

Towards Scalable Bayesian Optimization via Gradient-Informed Bayesian Neural Networks
par: Makrygiorgos, Georgios, et autres
Publié: (2025)

A Sparse Bayesian Learning Algorithm for Estimation of Interaction Kernels in Motsch-Tadmor Model
par: Feng, Jinchao, et autres
Publié: (2025)

Provably Efficient Bayesian Optimization with Unknown Gaussian Process Hyperparameter Estimation
par: Ha, Huong, et autres
Publié: (2023)

Weighted Wasserstein Barycenter of Gaussian Processes for exotic Bayesian Optimization tasks
par: Candelieri, Antonio, et autres
Publié: (2026)

Constrained Bayesian Optimization under Bivariate Gaussian Process with Application to Cure Process Optimization
par: Li, Yezhuo, et autres
Publié: (2025)

Bayesian Gaussian Process ODEs via Double Normalizing Flows
par: Xu, Jian, et autres
Publié: (2023)

Learning to Forget: Bayesian Time Series Forecasting using Recurrent Sparse Spectrum Signature Gaussian Processes
par: Tóth, Csaba, et autres
Publié: (2024)

Variational Gaussian Process Diffusion Processes
par: Verma, Prakhar, et autres
Publié: (2023)

Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization
par: Xu, Zhitong, et autres
Publié: (2024)

Improved Regret Bounds for Gaussian Process Upper Confidence Bound in Bayesian Optimization
par: Iwazaki, Shogo
Publié: (2025)

Goal-Oriented Lower-Tail Calibration of Gaussian Processes for Bayesian Optimization
par: Pion, Aurélien, et autres
Publié: (2026)

Sparse Orthogonal Variational Inference for Gaussian Processes
par: Shi, Jiaxin, et autres
Publié: (2019)

Inverse-Free Sparse Variational Gaussian Processes
par: Cortinovis, Stefano, et autres
Publié: (2026)

Scalable Gaussian Processes with Latent Kronecker Structure
par: Lin, Jihao Andreas, et autres
Publié: (2025)

Scalable Deep Basis Kernel Gaussian Processes
par: Zhu, Yunqin, et autres
Publié: (2025)

Graph Random Features for Scalable Gaussian Processes
par: Zhang, Matthew, et autres
Publié: (2025)

Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process Regression
par: Zou, Lu, et autres
Publié: (2024)

From Shallow Bayesian Neural Networks to Gaussian Processes: General Convergence, Identifiability and Scalable Inference
par: de Araújo, Gracielle Antunes, et autres
Publié: (2026)

Stochastic Attention: Connectome-Inspired Randomized Routing for Expressive Linear-Time Attention
par: Jin, Zehao, et autres
Publié: (2026)

Sparse Techniques for Regression in Deep Gaussian Processes
par: Latz, Jonas, et autres
Publié: (2025)

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

Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
par: Terenin, Alexander, et autres
Publié: (2022)

Poisson Process for Bayesian Optimization
par: Wang, Xiaoxing, et autres
Publié: (2024)