Cover Image

Automated Random Embedding for Practical Bayesian Optimization with Unknown Effective Dimension

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Qian, Hong, Shu, Xiang, Xia, Xiang, Liu, Xuhui, Fu, Yangde, Liang, Bei, Wang, Huibin, Dou, Liang
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Hyperbolic Hierarchical Knowledge Graph Embeddings for Link Prediction in Low Dimensions
by: Zheng, Wenjie, et al.
Published: (2022)

Content-Style Learning from Unaligned Domains: Identifiability under Unknown Latent Dimensions
by: Shrestha, Sagar, et al.
Published: (2024)

LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch
by: Jiang, Caigao, et al.
Published: (2024)

Optimizing the Unknown: Black Box Bayesian Optimization with Energy-Based Model and Reinforcement Learning
by: Miao, Ruiyao, et al.
Published: (2025)

HELENE: Hessian Layer-wise Clipping and Gradient Annealing for Accelerating Fine-tuning LLM with Zeroth-order Optimization
by: Zhao, Huaqin, et al.
Published: (2024)

Semantic Evolvement Enhanced Graph Autoencoder for Rumor Detection
by: Tao, Xiang, et al.
Published: (2024)

Language Model Embeddings Can Be Sufficient for Bayesian Optimization
by: Nguyen, Tung, et al.
Published: (2024)

From Wide to Deep: Dimension Lifting Network for Parameter-efficient Knowledge Graph Embedding
by: Cai, Borui, et al.
Published: (2023)

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

Enhancing Continual Learning for Software Vulnerability Prediction: Addressing Catastrophic Forgetting via Hybrid-Confidence-Aware Selective Replay for Temporal LLM Fine-Tuning
by: Dou, Xuhui, et al.
Published: (2026)

Degradation Modeling and Prognostic Analysis Under Unknown Failure Modes
by: Fu, Ying, et al.
Published: (2024)

Bi-Level Graph Structure Learning for Next POI Recommendation
by: Wang, Liang, et al.
Published: (2024)

A Randomized PDE Energy driven Iterative Framework for Efficient and Stable PDE Solutions
by: Bing, Yi, et al.
Published: (2026)

Expensive Multi-Objective Bayesian Optimization Based on Diffusion Models
by: Li, Bingdong, et al.
Published: (2024)

Adaptive Self-supervised Robust Clustering for Unstructured Data with Unknown Cluster Number
by: Ding, Chen-Lu, et al.
Published: (2024)

One-shot Federated Learning Methods: A Practical Guide
by: Liu, Xiang, et al.
Published: (2025)

Automated Kernel Discovery Towards Understanding High-dimensional Bayesian Optimization
by: Yun, Taeyoung, et al.
Published: (2026)

Learnable Chernoff Baselines for Inference-Time Alignment
by: Madhow, Sunil, et al.
Published: (2026)

Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation
by: Tang, Haoteng, et al.
Published: (2024)

PSPO*: An Effective Process-supervised Policy Optimization for Reasoning Alignment
by: Li, Jiawei, et al.
Published: (2024)

Disentangled Double Machine Learning for Accurate Causal Effect Estimation
by: Xiang, Guodu, et al.
Published: (2026)

Practical Battery Health Monitoring using Uncertainty-Aware Bayesian Neural Network
by: Zhao, Yunyi, et al.
Published: (2024)

Dual-Kernel Graph Community Contrastive Learning
by: Chen, Xiang, et al.
Published: (2025)

Known Meets Unknown: Mitigating Overconfidence in Open Set Recognition
by: Zhao, Dongdong, et al.
Published: (2025)

Automated Computational Energy Minimization of ML Algorithms using Constrained Bayesian Optimization
by: Mitra, Pallavi, et al.
Published: (2024)

Continuous Optimization for Feature Selection with Permutation-Invariant Embedding and Policy-Guided Search
by: Liu, Rui, et al.
Published: (2025)

Croppable Knowledge Graph Embedding
by: Zhu, Yushan, et al.
Published: (2024)

On Surprising Effectiveness of Masking Updates in Adaptive Optimizers
by: Joo, Taejong, et al.
Published: (2026)

On the Limitations and Capabilities of Position Embeddings for Length Generalization
by: Chen, Yang, et al.
Published: (2025)

Decentralized Online Convex Optimization with Unknown Feedback Delays
by: Qiu, Hao, et al.
Published: (2026)

Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints
by: Kong, Lingkai, et al.
Published: (2024)

DySCo: Dynamic Semantic Compression for Effective Long-term Time Series Forecasting
by: Ao, Xiang, et al.
Published: (2026)

Exploratory Machine Learning with Unknown Unknowns
by: Zhao, Peng, et al.
Published: (2020)

Sample-Efficient Bayesian Optimization with Transfer Learning for Heterogeneous Search Spaces
by: Deshwal, Aryan, et al.
Published: (2024)

Turning Stale Gradients into Stable Gradients: Coherent Coordinate Descent with Implicit Landscape Smoothing for Lightweight Zeroth-Order Optimization
by: Liang, Chen, et al.
Published: (2026)

Poisson Process for Bayesian Optimization
by: Wang, Xiaoxing, et al.
Published: (2024)

Inducing Systematicity in Transformers by Attending to Structurally Quantized Embeddings
by: Jiang, Yichen, et al.
Published: (2024)

A Shared Valence Axis Across Modern LLMs and Human EEG: The Saturation Regularity
by: Radwan, Yousef A., et al.
Published: (2026)

OpenHAIV: A Framework Towards Practical Open-World Learning
by: Xiang, Xiang, et al.
Published: (2025)

Optimistic Thompson Sampling for No-Regret Learning in Unknown Games
by: Li, Yingru, et al.
Published: (2024)