Copertina

Kernel-based guarantees for nonlinear parametric models in Bayesian optimization

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autore principale:	Oliveira, Rafael
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Theoretical guarantees for neural estimators in parametric statistics
di: Rödder, Almut, et al.
Pubblicazione: (2025)

Learning to optimize with convergence guarantees using nonlinear system theory
di: Martin, Andrea, et al.
Pubblicazione: (2024)

Kernel-based learning with guarantees for multi-agent applications
di: Kowalczyk, Krzysztof, et al.
Pubblicazione: (2024)

Proximal basin hopping: global optimization with guarantees
di: Lauga, Guillaume, et al.
Pubblicazione: (2026)

Kernel-based retrieval models for hyperspectral image data optimized with Kernel Flows
di: Duma, Zina-Sabrina, et al.
Pubblicazione: (2024)

Learning based convex approximation for constrained parametric optimization
di: Liu, Kang, et al.
Pubblicazione: (2025)

Accounting for shared covariates in semi-parametric Bayesian additive regression trees
di: Prado, Estevão B., et al.
Pubblicazione: (2021)

Automatic nonlinear MPC approximation with closed-loop guarantees
di: Tokmak, Abdullah, et al.
Pubblicazione: (2023)

Robust optimization for adversarial learning with finite sample complexity guarantees
di: Bertolace, André, et al.
Pubblicazione: (2024)

Revisiting RIP guarantees for sketching operators on mixture models
di: Belhadji, Ayoub, et al.
Pubblicazione: (2023)

Tensor train completion: local recovery guarantees via Riemannian optimization
di: Budzinskiy, Stanislav, et al.
Pubblicazione: (2021)

A nonlinear extension of parametric model embedding for dimensionality reduction in parametric shape design
di: Serani, Andrea, et al.
Pubblicazione: (2026)

Bayesian Optimization by Kernel Regression and Density-based Exploration
di: Zhu, Tansheng, et al.
Pubblicazione: (2025)

Bayesian Kernel Regression for Functional Data
di: Kusaba, Minoru, et al.
Pubblicazione: (2025)

Statistical guarantees for denoising reflected diffusion models
di: Holk, Asbjørn, et al.
Pubblicazione: (2024)

Kernelized Normalizing Constant Estimation: Bridging Bayesian Quadrature and Bayesian Optimization
di: Cai, Xu, et al.
Pubblicazione: (2024)

Kernel-based optimization of measurement operators for quantum reservoir computers
di: Gross, Markus, et al.
Pubblicazione: (2026)

PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs
di: Brivio, Simone, et al.
Pubblicazione: (2024)

Universal generalization guarantees for Wasserstein distributionally robust models
di: Le, Tam, et al.
Pubblicazione: (2024)

Learning to optimize with guarantees: a complete characterization of linearly convergent algorithms
di: Martin, Andrea, et al.
Pubblicazione: (2025)

Adversarially robust clustering with optimality guarantees
di: Jana, Soham, et al.
Pubblicazione: (2023)

Derivatives of Stochastic Gradient Descent in parametric optimization
di: Iutzeler, Franck, et al.
Pubblicazione: (2024)

An exponential mechanism based on quadratic approximations for fine-tuning machine learning models with privacy guarantees
di: Tran, Hoang, et al.
Pubblicazione: (2026)

BSD: a Bayesian framework for parametric models of neural spectra
di: Medrano, Johan, et al.
Pubblicazione: (2024)

Laplace Approximation for Bayesian Tensor Network Kernel Machines
di: Saiapin, Albert, et al.
Pubblicazione: (2026)

Symmetry-Aware Bayesian Optimization via Max Kernels
di: Bardou, Anthony, et al.
Pubblicazione: (2025)

Surrogate modeling for Bayesian optimization beyond a single Gaussian process
di: Lu, Qin, et al.
Pubblicazione: (2022)

Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models
di: Tomasetto, Matteo, et al.
Pubblicazione: (2024)

Federated learning over physical channels: adaptive algorithms with near-optimal guarantees
di: Zhang, Rui, et al.
Pubblicazione: (2025)

Integrating uncertainty quantification into randomized smoothing based robustness guarantees
di: Däubener, Sina, et al.
Pubblicazione: (2024)

Semi-parametric Expert Bayesian Network Learning with Gaussian Processes and Horseshoe Priors
di: Weng, Yidou, et al.
Pubblicazione: (2024)

Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels
di: Long, Da, et al.
Pubblicazione: (2023)

Omnipresent Yet Overlooked: Heat Kernels in Combinatorial Bayesian Optimization
di: Doumont, Colin, et al.
Pubblicazione: (2025)

Bayesian Optimization over Bounded Domains with the Beta Product Kernel
di: Nguyen, Huy Hoang, et al.
Pubblicazione: (2025)

Bayesian Adaptive Calibration and Optimal Design
di: Oliveira, Rafael, et al.
Pubblicazione: (2024)

Kernel Density Bayesian Inverse Reinforcement Learning
di: Mandyam, Aishwarya, et al.
Pubblicazione: (2023)

Respecting the limit:Bayesian optimization with a bound on the optimal value
di: Wang, Hanyang, et al.
Pubblicazione: (2024)

Generative Bayesian Optimization: Generative Models as Acquisition Functions
di: Oliveira, Rafael, et al.
Pubblicazione: (2025)

Minimizing robust density power-based divergences for general parametric density models
di: Okuno, Akifumi
Pubblicazione: (2023)

Scalable Spatiotemporally Varying Coefficient Modelling with Bayesian Kernelized Tensor Regression
di: Lei, Mengying, et al.
Pubblicazione: (2021)