Co-occurring keywords · papers mentioning both
offline reinforcement learning
(9)
markov decision process
(3)
distributionally robust optimization
(2)
reward function
(2)
model-based reinforcement learning
(2)
sample complexity
(2)
policy optimization
(2)
dynamic mechanism design
(1)
imitation learning from observation
(1)
robust supervised learning
(1)
Papers
Offline Multi-Objective Bandits: From Logged Data to Pareto-Optimal Policies
AAAI 2026
Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learning
ICML 2025
A Unified Principle of Pessimism for Offline Reinforcement Learning under Model Mismatch
NeurIPS 2024
Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity
JMLR 2024
Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective
ICML 2024
MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations
ICML 2023
Model-Based Offline Reinforcement Learning with Local Misspecification
AAAI 2023
Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning Approach
AISTATS 2023
VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function Approximation
ICLR 2023
Corruption-robust Offline Reinforcement Learning
AISTATS 2022
Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning
ICML 2022
Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning
NeurIPS 2021
The Importance of Pessimism in Fixed-Dataset Policy Optimization
ICLR 2021