Cover Image

High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yin, Yuxuan, Wang, Yu, Li, Peng
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Collaborative Unlabeled Data Optimization
by: Shang, Xinyi, et al.
Published: (2025)

AllMatch: Exploiting All Unlabeled Data for Semi-Supervised Learning
by: Wu, Zhiyu, et al.
Published: (2024)

Integrating Distribution Matching into Semi-Supervised Contrastive Learning for Labeled and Unlabeled Data
by: Nakayama, Shogo, et al.
Published: (2026)

Learning Unlabeled Clients Divergence for Federated Semi-Supervised Learning via Anchor Model Aggregation
by: Elbatel, Marawan, et al.
Published: (2024)

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

Incorporating Unlabelled Data into Bayesian Neural Networks
by: Sharma, Mrinank, et al.
Published: (2023)

Semi-Supervised Preference Optimization with Limited Feedback
by: Lee, Seonggyun, et al.
Published: (2025)

IPBC: An Interactive Projection-Based Framework for Human-in-the-Loop Semi-Supervised Clustering of High-Dimensional Data
by: Zare, Mohammad
Published: (2026)

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

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

Robust Semi-Supervised Learning in Open Environments
by: Guo, Lan-Zhe, et al.
Published: (2024)

A High-Dimensional Statistical Method for Optimizing Transfer Quantities in Multi-Source Transfer Learning
by: Zhang, Qingyue, et al.
Published: (2025)

Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data
by: Takahashi, Hiroshi, et al.
Published: (2024)

Learning from M-Tuple Dominant Positive and Unlabeled Data
by: Qin, Jiahe, et al.
Published: (2025)

Offline Multi-Agent Reinforcement Learning via In-Sample Sequential Policy Optimization
by: Liu, Zongkai, et al.
Published: (2024)

Towards Instance-wise Personalized Federated Learning via Semi-Implicit Bayesian Prompt Tuning
by: Ye, Tiandi, et al.
Published: (2025)

Diffusion Model-Based Data Synthesis Aided Federated Semi-Supervised Learning
by: Wang, Zhongwei, et al.
Published: (2025)

Learning Constraint Network from Demonstrations via Positive-Unlabeled Learning with Memory Replay
by: Peng, Baiyu, et al.
Published: (2024)

Augmenting Offline RL with Unlabeled Data
by: Wang, Zhao, et al.
Published: (2024)

CHGNN: A Semi-Supervised Contrastive Hypergraph Learning Network
by: Song, Yumeng, et al.
Published: (2023)

Positive Unlabeled Contrastive Learning
by: Acharya, Anish, et al.
Published: (2022)

Semi-Supervised Learning for Anomaly Traffic Detection via Bidirectional Normalizing Flows
by: Dang, Zhangxuan, et al.
Published: (2024)

From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
by: Wang, Zheng, et al.
Published: (2023)

LASER: Language Model Regression for Semi-Structured Workflow Resource and Runtime Estimation
by: Yin, Yuxuan, et al.
Published: (2025)

Resilient Class-Incremental Learning: on the Interplay of Drifting, Unlabelled and Imbalanced Data Streams
by: Li, Jin, et al.
Published: (2026)

Transfer Learning for Bayesian Optimization on Heterogeneous Search Spaces
by: Fan, Zhou, et al.
Published: (2023)

Towards Synthesizing High-Dimensional Tabular Data with Limited Samples
by: Li, Zuqing, et al.
Published: (2025)

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

Optimistic Gradient Learning with Hessian Corrections for High-Dimensional Black-Box Optimization
by: Kfir, Yedidya, et al.
Published: (2025)

From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimization
by: Chen, Xinjie, et al.
Published: (2025)

Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs
by: Suwandi, Richard Cornelius, et al.
Published: (2025)

Reinforcement Learning-Guided Semi-Supervised Learning
by: Heidari, Marzi, et al.
Published: (2024)

Transfer Learning for Minimum Operating Voltage Prediction in Advanced Technology Nodes: Leveraging Legacy Data and Silicon Odometer Sensing
by: Yin, Yuxuan, et al.
Published: (2025)

Uncertain Multi-Objective Recommendation via Orthogonal Meta-Learning Enhanced Bayesian Optimization
by: Wang, Hongxu, et al.
Published: (2025)

Semi-Supervised Federated Learning via Dual Contrastive Learning and Soft Labeling for Intelligent Fault Diagnosis
by: Dai, Yajiao, et al.
Published: (2025)

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

Federated Learning of Dynamic Bayesian Network via Continuous Optimization from Time Series Data
by: Chen, Jianhong, et al.
Published: (2024)

High-Dimensional Search, Low-Dimensional Solution: Decoupling Optimization from Representation
by: Kalyoncuoglu, Yusuf, et al.
Published: (2025)

Beyond Myopia: Learning from Positive and Unlabeled Data through Holistic Predictive Trends
by: Wang, Xinrui, et al.
Published: (2023)

An Optimization Algorithm for Multimodal Data Alignment
by: Zhang, Wei, et al.
Published: (2025)