← Back to papers

2025 ICML ICML 2025

Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation

Authors

Taehyun Cho , Seungyub Han , Seokhun Ju , Dohyeong Kim , Kyungjae Lee , Jungwoo Lee

Related papers

Scaling Sparse Feature Circuits For Studying In-Context Learning 2025

Incremental Gradient Descent with Small Epoch Counts is Surprisingly Slow on Ill-Conditioned Problems 2025

SToFM: a Multi-scale Foundation Model for Spatial Transcriptomics 2025

Batch List-Decodable Linear Regression via Higher Moments 2025

GS-Bias: Global-Spatial Bias Learner for Single-Image Test-Time Adaptation of Vision-Language Models 2025