Cover Image

An Adaptive Dropout Approach for High-Dimensional Bayesian Optimization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Huang, Jundi, Zhan, Dawei
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

Expected Coordinate Improvement for High-Dimensional Bayesian Optimization
by: Zhan, Dawei
Published: (2024)

A Simple and Efficient Approach to Batch Bayesian Optimization
by: Zhan, Dawei, et al.
Published: (2024)

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

Navigating in High-Dimensional Search Space: A Hierarchical Bayesian Optimization Approach
by: Li, Wenxuan, et al.
Published: (2024)

Adaptive Candidate Point Thompson Sampling for High-Dimensional Bayesian Optimization
by: Fan, Donney, et al.
Published: (2026)

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)

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

Adaptive Linear Embedding for Nonstationary High-Dimensional Optimization
by: Wen, Yuejiang, et al.
Published: (2025)

Comparison of High-Dimensional Bayesian Optimization Algorithms on BBOB
by: Santoni, Maria Laura, et al.
Published: (2023)

Safe Bayesian Optimization for the Control of High-Dimensional Embodied Systems
by: Wei, Yunyue, et al.
Published: (2024)

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

High-Dimensional Bayesian Optimization via Random Projection of Manifold Subspaces
by: Nguyen, Quoc-Anh Hoang, et al.
Published: (2024)

Trading Convergence Rate with Computational Budget in High Dimensional Bayesian Optimization
by: Tran-The, Hung, et al.
Published: (2019)

Relaxing the Additivity Constraints in Decentralized No-Regret High-Dimensional Bayesian Optimization
by: Bardou, Anthony, et al.
Published: (2023)

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

Adaptive Tabu Dropout for Regularization of Deep Neural Network
by: Hasan, Md. Tarek, et al.
Published: (2024)

Enhancing Dropout-based Bayesian Neural Networks with Multi-Exit on FPGA
by: Chen, Hao Mark, et al.
Published: (2024)

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

Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization
by: Xu, Zhitong, et al.
Published: (2024)

BO4Mob: Bayesian Optimization Benchmarks for High-Dimensional Urban Mobility Problem
by: Ryu, Seunghee, et al.
Published: (2025)

Regional Expected Improvement for Efficient Trust Region Selection in High-Dimensional Bayesian Optimization
by: Namura, Nobuo, et al.
Published: (2024)

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings
by: Priem, Rémy, et al.
Published: (2025)

Adaptive Bayesian Optimization for High-Precision Motion Systems
by: König, Christopher, et al.
Published: (2024)

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

High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling
by: Yin, Yuxuan, et al.
Published: (2023)

Variational Adaptive Noise and Dropout towards Stable Recurrent Neural Networks
by: Kobayashi, Taisuke, et al.
Published: (2025)

Categorical Optimization with Bayesian Anchored Latent Trust Regions for Structural Design under High-Dimensional Uncertainty
by: Liang, Zhangyong, et al.
Published: (2026)

FairDropout: Using Example-Tied Dropout to Enhance Generalization of Minority Groups
by: Nanfack, Geraldin, et al.
Published: (2025)

Bayesian Neural Networks with Monte Carlo Dropout for Probabilistic Electricity Price Forecasting
by: Das, Abhinav, et al.
Published: (2025)

From Sorting Algorithms to Scalable Kernels: Bayesian Optimization in High-Dimensional Permutation Spaces
by: Xie, Zikai, et al.
Published: (2025)

EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization
by: Cheon, Mujin, et al.
Published: (2024)

Provable Generalization Bounds for Deep Neural Networks with Momentum-Adaptive Gradient Dropout
by: Safder, Adeel
Published: (2025)

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout
by: Liu, Ji, et al.
Published: (2025)

Nonlinear Dimensionality Reduction Techniques for Bayesian Optimization
by: Long, Luo, et al.
Published: (2025)

Testing Spintronics Implemented Monte Carlo Dropout-Based Bayesian Neural Networks
by: Ahmed, Soyed Tuhin, et al.
Published: (2024)

Continuum Dropout for Neural Differential Equations
by: Lee, Jonghun, et al.
Published: (2025)

Efficient Sparse Training with Structured Dropout
by: Lo, Andy
Published: (2024)

Federated Dropout: Convergence Analysis and Resource Allocation
by: Xie, Sijing, et al.
Published: (2024)

Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression
by: Denault, William R. P.
Published: (2025)