Cover Image

Vanilla Bayesian Optimization Performs Great in High Dimensions

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hvarfner, Carl, Hellsten, Erik Orm, Nardi, Luigi
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Leveraging Axis-Aligned Subspaces for High-Dimensional Bayesian Optimization with Group Testing
by: Hellsten, Erik, et al.
Published: (2025)

Self-Correcting Bayesian Optimization through Bayesian Active Learning
by: Hvarfner, Carl, et al.
Published: (2023)

A General Framework for User-Guided Bayesian Optimization
by: Hvarfner, Carl, et al.
Published: (2023)

CATBench: A Compiler Autotuning Benchmarking Suite for Black-box Optimization
by: Tørring, Jacob O., et al.
Published: (2024)

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

Understanding High-Dimensional Bayesian Optimization
by: Papenmeier, Leonard, et al.
Published: (2025)

Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces
by: Papenmeier, Leonard, et al.
Published: (2023)

Exploring Exploration in Bayesian Optimization
by: Papenmeier, Leonard, et al.
Published: (2025)

Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces
by: Papenmeier, Leonard, et al.
Published: (2023)

Falsification of Cyber-Physical Systems using Bayesian Optimization
by: Ramezani, Zahra, et al.
Published: (2022)

Bencher: Simple and Reproducible Benchmarking for Black-Box Optimization
by: Papenmeier, Leonard, et al.
Published: (2025)

A Unified Framework for Entropy Search and Expected Improvement in Bayesian Optimization
by: Cheng, Nuojin, et al.
Published: (2025)

Rethinking Trust Region Bayesian Optimization in High Dimensions
by: Tang, Wei-Ting, et al.
Published: (2026)

Computationally Efficient High-Dimensional Bayesian Optimization via Variable Selection
by: Shen, Yihang, et al.
Published: (2021)

An Adaptive Dimension Reduction Estimation Method for High-dimensional Bayesian Optimization
by: Hu, Shouri, et al.
Published: (2024)

Memory-Guided Trust-Region Bayesian Optimization (MG-TuRBO) for High Dimensions
by: Saroj, Abhilasha, et al.
Published: (2026)

Bayesian Optimization that Limits Search Region to Lower Dimensions Utilizing Local GPR
by: Taguchi, Yasunori, et al.
Published: (2024)

Tight Regret Bounds for Bayesian Optimization in One Dimension
by: Scarlett, Jonathan
Published: (2018)

Structural Dimension Reduction in Bayesian Networks
by: Heng, Pei, et al.
Published: (2026)

Automated Random Embedding for Practical Bayesian Optimization with Unknown Effective Dimension
by: Qian, Hong, et al.
Published: (2026)

Asymptotic Performance of Time-Varying Bayesian Optimization
by: Bardou, Anthony, et al.
Published: (2025)

Learning Solutions of Stochastic Optimization Problems with Bayesian Neural Networks
by: Lahoud, Alan A., et al.
Published: (2024)

Revisit the Stability of Vanilla Federated Learning Under Diverse Conditions
by: Lee, Youngjoon, et al.
Published: (2025)

Variational Inference for Bayesian MIDAS Regression
by: Simeone, Luigi
Published: (2026)

Vanilla Gradient Descent for Oblique Decision Trees
by: Panda, Subrat Prasad, et al.
Published: (2024)

Stacking Variational Bayesian Monte Carlo
by: Silvestrin, Francesco, et al.
Published: (2025)

An Adaptive Dropout Approach for High-Dimensional Bayesian Optimization
by: Huang, Jundi, et al.
Published: (2025)

Beyond Vanilla Variational Autoencoders: Detecting Posterior Collapse in Conditional and Hierarchical Variational Autoencoders
by: Dang, Hien, et al.
Published: (2023)

High dimensional Bayesian Optimization via Condensing-Expansion Projection
by: Lu, Jiaming, et al.
Published: (2024)

Unleashing the Potential of Acquisition Functions in High-Dimensional Bayesian Optimization
by: Zhao, Jiayu, et al.
Published: (2023)

High Dimensional Bayesian Optimization using Lasso Variable Selection
by: Hoang, Vu Viet, et al.
Published: (2025)

We Still Don't Understand High-Dimensional Bayesian Optimization
by: Doumont, Colin, et al.
Published: (2025)

Spatio-Temporal Attention Graph Neural Network: Explaining Causalities With Attention
by: Koistinen, Kosti, et al.
Published: (2026)

Low-Dimension-to-High-Dimension Generalization And Its Implications for Length Generalization
by: Chen, Yang, et al.
Published: (2024)

A General Online Algorithm for Optimizing Complex Performance Metrics
by: Kotłowski, Wojciech, et al.
Published: (2024)

Amortized Bayesian Experimental Design for Decision-Making
by: Huang, Daolang, et al.
Published: (2024)

Global Convergence Analysis of Vanilla Gradient Descent for Asymmetric Matrix Completion
by: Zhang, Xu, et al.
Published: (2025)

Pretrained Joint Predictions for Scalable Batch Bayesian Optimization of Molecular Designs
by: Wang-Henderson, Miles, et al.
Published: (2025)

Neural Networks Perform Sufficient Dimension Reduction
by: Xu, Shuntuo, et al.
Published: (2024)

High-dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategy
by: Ngo, Lam, et al.
Published: (2024)