:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Chen, Weiqin, Squillante, Mark S., Wu, Chai Wah, Paternain, Santiago
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Methodology
Online Access:	https://arxiv.org/abs/2406.14753
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Random Policy Enables In-Context Reinforcement Learning within Trust Horizons
by: Chen, Weiqin, et al.
Published: (2024)

Probabilistic Constraint for Safety-Critical Reinforcement Learning
by: Chen, Weiqin, et al.
Published: (2023)

Filtering Learning Histories Enhances In-Context Reinforcement Learning
by: Chen, Weiqin, et al.
Published: (2025)

Provable Domain Adaptation for Offline Reinforcement Learning with Limited Samples
by: Chen, Weiqin, et al.
Published: (2024)

A Relative Ignorability Framework for Decision-Relevant Observability in Control Theory and Reinforcement Learning
by: Bleile, MaryLena, et al.
Published: (2025)

Enhancing Reinforcement Learning for Radiology Report Generation with Evidence-aware Rewards and Self-correcting Preference Learning
by: Zhou, Qin, et al.
Published: (2026)

Medical Knowledge Integration into Reinforcement Learning Algorithms for Dynamic Treatment Regimes
by: Yazzourh, Sophia, et al.
Published: (2024)

CAP: A General Algorithm for Online Selective Conformal Prediction with FCR Control
by: Bao, Yajie, et al.
Published: (2024)

Statistical Theory of Multi-stage Newton Iteration Algorithm for Online Continual Learning
by: Lu, Xinjia, et al.
Published: (2025)

A Meta-Learning Approach to Bayesian Causal Discovery
by: Dhir, Anish, et al.
Published: (2024)

Vector-Valued Distributional Reinforcement Learning Policy Evaluation: A Hilbert Space Embedding Approach
by: Mohammadi, Mehrdad, et al.
Published: (2026)

A Unified Framework for Inference with General Missingness Patterns and Machine Learning Imputation
by: Chen, Xingran, et al.
Published: (2025)

Designing Time Series Experiments in A/B Testing with Transformer Reinforcement Learning
by: Wu, Xiangkun, et al.
Published: (2026)

When Is Generalized Bayes Bayesian? A Decision-Theoretic Characterization of Loss-Based Updating
by: McAlinn, Kenichiro, et al.
Published: (2026)

A Dynamic, Ordinal Gaussian Process Item Response Theoretic Model
by: Chen, Yehu, et al.
Published: (2025)

MMDCP: A Distribution-free Approach to Outlier Detection and Classification with Coverage Guarantees and SCW-FDR Control
by: Lin, Youwu, et al.
Published: (2025)

Robust Tensor Regression with Nonconvexity: Algorithmic and Statistical Theory
by: Song, Zihao, et al.
Published: (2026)

STEEL: Singularity-aware Reinforcement Learning
by: Chen, Xiaohong, et al.
Published: (2023)

Teleporter Theory: A General and Simple Approach for Modeling Cross-World Counterfactual Causality
by: Li, Jiangmeng, et al.
Published: (2024)

ConstrainedSQL: Training LLMs for Text2SQL via Constrained Reinforcement Learning
by: Chen, Weiqin, et al.
Published: (2025)

Adaptive Primal-Dual Method for Safe Reinforcement Learning
by: Chen, Weiqin, et al.
Published: (2024)

Federated Offline Reinforcement Learning
by: Zhou, Doudou, et al.
Published: (2022)

A New Causal Rule Learning Approach to Interpretable Estimation of Heterogeneous Treatment Effect
by: Wu, Ying, et al.
Published: (2023)

A Statistical Decision-Theoretical Perspective on the Two-Stage Approach to Parameter Estimation
by: Lakshminarayanan, Braghadeesh, et al.
Published: (2022)

Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments
by: Zhu, Hongtao, et al.
Published: (2024)

Position: Benchmarking is Limited in Reinforcement Learning Research
by: Jordan, Scott M., et al.
Published: (2024)

A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor Analysis
by: Urban, Christopher J., et al.
Published: (2020)

Measure-Theoretic Anti-Causal Representation Learning
by: Behnam, Arman, et al.
Published: (2025)

Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice
by: Machlanski, Damian, et al.
Published: (2023)

Estimating Treatment Effects under Algorithmic Interference: A Structured Neural Networks Approach
by: Zhan, Ruohan, et al.
Published: (2024)

Reinforcement Learning for Causal Discovery without Acyclicity Constraints
by: Duong, Bao, et al.
Published: (2024)

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding
by: Li, Yuhan, et al.
Published: (2025)

CFLight: Enhancing Safety with Traffic Signal Control through Counterfactual Learning
by: Li, Mingyuan, et al.
Published: (2025)

Bootstrapped Control Limits for Score-Based Concept Drift Control Charts
by: Wu, Jiezhong, et al.
Published: (2025)

Controllable Generative Sandbox for Causal Inference
by: Zhang, Qi, et al.
Published: (2026)

SMART: A Spectral Transfer Approach to Multi-Task Learning
by: Zhao, Boxin, et al.
Published: (2026)

Action Shapley: A Training Data Selection Metric for World Model in Reinforcement Learning
by: Ghosh, Rajat, et al.
Published: (2026)

POLAR: A Pessimistic Model-based Policy Learning Algorithm for Dynamic Treatment Regimes
by: Zhang, Ruijia, et al.
Published: (2025)

SMART Fine-tuning Factor Augmented Neural Lasso
by: Chai, Jinhang, et al.
Published: (2026)

Counterfactual Generative Models for Time-Varying Treatments
by: Wu, Shenghao, et al.
Published: (2023)