eluder dimension

Taming the Monster Every Context: Complexity Measure and Unified Framework for Offline-Oracle Efficient Contextual Bandits COLT 2026 Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation ICML 2025 Contextual Bandits with Stage-wise Constraints JMLR 2025 Reliable Active Apprenticeship Learning ALT 2025 Second Order Bounds for Contextual Bandits with Function Approximation ICLR 2025 The Space Complexity of Learning-Unlearning Algorithms (extended abstract) COLT 2025 A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation NeurIPS 2024 Causal Bandits with General Causal Models and Interventions AISTATS 2024 Eluder-based Regret for Stochastic Contextual MDPs ICML 2024 How Does Variance Shape the Regret in Contextual Bandits? NeurIPS 2024 Near-continuous time Reinforcement Learning for continuous state-action spaces ALT 2024 On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation AISTATS 2024 The Star Number and Eluder Dimension: Elementary Observations About the Dimensions of Disagreement COLT 2024 A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning ICLR 2023 Experiment Planning with Function Approximation NeurIPS 2023 Low-Switching Policy Gradient with Exploration via Online Sensitivity Sampling ICML 2023 Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension UAI 2023 VOQL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation COLT 2023 Reinforcement Learning with Logarithmic Regret and Policy Switches NeurIPS 2022 Understanding the Eluder Dimension NeurIPS 2022 Provably Correct Optimization and Exploration with Non-linear Policies ICML 2021 Randomized Exploration in Reinforcement Learning with General Value Function Approximation ICML 2021 Representation Learning Beyond Linear Prediction Functions NeurIPS 2021 Agnostic Q-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity NeurIPS 2020 On Thompson Sampling for Smoother-than-Lipschitz Bandits AISTATS 2020