:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Ament, Sebastian, Santorella, Elizabeth, Eriksson, David, Letham, Ben, Balandat, Maximilian, Bakshy, Eytan
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Optimization and Control
Online Access:	https://arxiv.org/abs/2410.24222
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure
by: Lin, Jihao Andreas, et al.
Published: (2024)

Empirical Gaussian Processes
by: Lin, Jihao Andreas, et al.
Published: (2026)

Unexpected Improvements to Expected Improvement for Bayesian Optimization
by: Ament, Sebastian, et al.
Published: (2023)

Scalable Gaussian Processes with Latent Kronecker Structure
by: Lin, Jihao Andreas, et al.
Published: (2025)

Experimenting, Fast and Slow: Bayesian Optimization of Long-term Outcomes with Online Experiments
by: Feng, Qing, et al.
Published: (2025)

BONSAI: Bayesian Optimization with Natural Simplicity and Interpretability
by: Daulton, Samuel, et al.
Published: (2026)

Embedding by Elicitation: Dynamic Representations for Bayesian Optimization of System Prompts
by: Lin, Zhiyuan Jerry, et al.
Published: (2026)

Informed Initialization for Bayesian Optimization and Active Learning
by: Hvarfner, Carl, et al.
Published: (2025)

LILO: Bayesian Optimization with Natural Language Feedback
by: Kobalczyk, Katarzyna, et al.
Published: (2025)

Bayesian Optimization of Function Networks with Partial Evaluations
by: Buathong, Poompol, et al.
Published: (2023)

Joint Composite Latent Space Bayesian Optimization
by: Maus, Natalie, et al.
Published: (2023)

Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes
by: Belakaria, Syrine, et al.
Published: (2024)

Learning Neural Networks by Neuron Pursuit
by: Kumar, Akshay, et al.
Published: (2025)

Gaussian Process Thompson Sampling via Rootfinding
by: Adebiyi, Taiwo A., et al.
Published: (2024)

Reinforcement Learning applied to Insurance Portfolio Pursuit
by: Young, Edward James, et al.
Published: (2024)

Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need
by: Yang, Shangda, et al.
Published: (2024)

Intrinsic Gaussian Processes on Manifolds and Their Accelerations by Symmetry
by: Ye, Ke, et al.
Published: (2020)

Theoretical Analysis of Heteroscedastic Gaussian Processes with Posterior Distributions
by: Ito, Yuji
Published: (2024)

Gaussian Process Regression with Soft Inequality and Monotonicity Constraints
by: Kochan, Didem, et al.
Published: (2024)

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

Wasserstein Barycenter Gaussian Process based Bayesian Optimization
by: Candelieri, Antonio, et al.
Published: (2025)

No-Regret Gaussian Process Optimization of Time-Varying Functions
by: Mauduit, Eliabelle, et al.
Published: (2025)

Physics-informed Gaussian Processes as Linear Model Predictive Controller
by: Tebbe, Jörn, et al.
Published: (2024)

Data-Driven Stochastic AC-OPF using Gaussian Processes
by: Mitrovic, Mile
Published: (2024)

Near-Optimal Policy Identification in Robust Constrained Markov Decision Processes via Epigraph Form
by: Kitamura, Toshinori, et al.
Published: (2024)

Finite-Sample-Based Reachability for Safe Control with Gaussian Process Dynamics
by: Prajapat, Manish, et al.
Published: (2025)

Towards Robust Interpretable Surrogates for Optimization
by: Goerigk, Marc, et al.
Published: (2024)

Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization
by: Tran-The, Hung, et al.
Published: (2022)

Robust Angular Synchronization via Directed Graph Neural Networks
by: He, Yixuan, et al.
Published: (2023)

Safe Time-Varying Optimization based on Gaussian Processes with Spatio-Temporal Kernel
by: Li, Jialin, et al.
Published: (2024)

Learning Surrogate Potential Mean Field Games via Gaussian Processes: A Data-Driven Approach to Ill-Posed Inverse Problems
by: Zhang, Jingguo, et al.
Published: (2025)

Reward-Relevance-Filtered Linear Offline Reinforcement Learning
by: Zhou, Angela
Published: (2024)

Frequentist Regret Analysis of Gaussian Process Thompson Sampling via Fractional Posteriors
by: Roy, Somjit, et al.
Published: (2026)

Efficient Algorithms for Robust Markov Decision Processes with $s$-Rectangular Ambiguity Sets
by: Ho, Chin Pang, et al.
Published: (2026)

The Vizier Gaussian Process Bandit Algorithm
by: Song, Xingyou, et al.
Published: (2024)

Global Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel Approximation
by: Xie, Yilin, et al.
Published: (2024)

An Efficient Spatial Branch-and-Bound Algorithm for Global Optimization of Gaussian Process Posterior Mean Functions
by: Tang, Wei-Ting, et al.
Published: (2026)

Learning Sequential Decisions from Multiple Sources via Group-Robust Markov Decision Processes
by: Xu, Mingyuan, et al.
Published: (2026)

Scalable DC Optimization via Adaptive Frank-Wolfe Algorithms
by: Pokutta, Sebastian
Published: (2025)

Group Projected Subspace Pursuit for Block Sparse Signal Reconstruction: Convergence Analysis and Applications
by: He, Roy Y., et al.
Published: (2024)