Image de couverture de livre

BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settings

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Karine, Karine, Murphy, Susan A., Marlin, Benjamin M.
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Combining LLM decision and RL action selection to improve RL policy for adaptive interventions
par: Karine, Karine, et autres
Publié: (2025)

StepCountJITAI: simulation environment for RL with application to physical activity adaptive intervention
par: Karine, Karine, et autres
Publié: (2024)

Enhancing Adaptive Behavioral Interventions with LLM Inference from Participant-Described States
par: Karine, Karine, et autres
Publié: (2025)

Batch Bayesian Active Learning with Partial Batch Label Sampling
par: Hu, Kangping, et autres
Publié: (2025)

Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning
par: Moradipari, Ahmadreza, et autres
Publié: (2023)

Missing Data Multiple Imputation for Tabular Q-Learning in Online RL
par: Chasalow, Kyla, et autres
Publié: (2025)

Graph Neural Thompson Sampling
par: Wu, Shuang, et autres
Publié: (2024)

Heteroscedastic Temporal Variational Autoencoder For Irregular Time Series
par: Shukla, Satya Narayan, et autres
Publié: (2021)

Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning
par: Namkoong, Hongseok, et autres
Publié: (2020)

Trust the Batch, On- or Off-Policy: Adaptive Policy Optimization for RL Post-Training
par: Fakoor, Rasool, et autres
Publié: (2026)

Pareto Front-Diverse Batch Multi-Objective Bayesian Optimization
par: Ahmadianshalchi, Alaleh, et autres
Publié: (2024)

Batch Acquisition Function Evaluations and Decouple Optimizer Updates for Faster Bayesian Optimization
par: Irie, Kaichi, et autres
Publié: (2025)

Adaptive Batch-Wise Sample Scheduling for Direct Preference Optimization
par: Huang, Zixuan, et autres
Publié: (2025)

Deep reinforcement learning for irrigation scheduling using high-dimensional sensor feedback
par: Saikai, Yuji, et autres
Publié: (2023)

Polynomial Threshold Functions of Bounded Tree-Width: Some Explainability and Complexity Aspects
par: Chubarian, Karine, et autres
Publié: (2025)

Momentum Boosted Episodic Memory for Improving Learning in Long-Tailed RL Environments
par: Fernandes, Dolton, et autres
Publié: (2025)

Thompson Sampling via Fine-Tuning of LLMs
par: Menet, Nicolas, et autres
Publié: (2025)

Contextual Thompson Sampling via Generation of Missing Data
par: Zhang, Kelly W., et autres
Publié: (2025)

MINTS: Minimalist Thompson Sampling
par: Wang, Kaizheng
Publié: (2026)

A Simple and Efficient Approach to Batch Bayesian Optimization
par: Zhan, Dawei, et autres
Publié: (2024)

Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo
par: Zheng, Haoyang, et autres
Publié: (2024)

AscendOptimizer: Episodic Agent for Ascend NPU Operator Optimization
par: Wu, Jiehao, et autres
Publié: (2026)

Neural Nonmyopic Bayesian Optimization in Dynamic Cost Settings
par: Truong, Sang T., et autres
Publié: (2026)

FedRTS: Federated Robust Pruning via Combinatorial Thompson Sampling
par: Huang, Hong, et autres
Publié: (2025)

Rethinking Langevin Thompson Sampling from A Stochastic Approximation Perspective
par: Wang, Weixin, et autres
Publié: (2025)

Novel Dynamic Batch-Sensitive Adam Optimiser for Vehicular Accident Injury Severity Prediction
par: Kyei, Daniel Asare, et autres
Publié: (2026)

Extending Differential Temporal Difference Methods for Episodic Problems
par: De Asis, Kris, et autres
Publié: (2026)

Improving Thompson Sampling via Information Relaxation for Budgeted Multi-armed Bandits
par: Jeong, Woojin, et autres
Publié: (2024)

reBandit: Random Effects based Online RL algorithm for Reducing Cannabis Use
par: Ghosh, Susobhan, et autres
Publié: (2024)

Extending Test-Time Scaling: A 3D Perspective with Context, Batch, and Turn
par: Yu, Chao, et autres
Publié: (2025)

Scaling In-Context Online Learning Capability of LLMs via Cross-Episode Meta-RL
par: Lin, Xiaofeng, et autres
Publié: (2026)

Large-Batch, Iteration-Efficient Neural Bayesian Design Optimization
par: Ansari, Navid, et autres
Publié: (2023)

AIS: Adaptive Importance Sampling for Quantized RL
par: Zhou, Jiajun, et autres
Publié: (2026)

FASTER: Value-Guided Sampling for Fast RL
par: Dong, Perry, et autres
Publié: (2026)

SamBaTen: Sampling-based Batch Incremental Tensor Decomposition
par: Gujral, Ekta, et autres
Publié: (2017)

Time Transfer: On Optimal Learning Rate and Batch Size In The Infinite Data Limit
par: Filatov, Oleg, et autres
Publié: (2024)

ACE and Diverse Generalization via Selective Disagreement
par: Daniels, Oliver, et autres
Publié: (2025)

Sample-Efficient Bayesian Optimization with Transfer Learning for Heterogeneous Search Spaces
par: Deshwal, Aryan, et autres
Publié: (2024)

Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity
par: Johnson, Emmeran, et autres
Publié: (2023)

High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling
par: Yin, Yuxuan, et autres
Publié: (2023)