3 citations · 5 across the 17 of their papers we have counts for
5 papers · 1 filter
Bellman Calibration for Marginalized Importance Weighting in Offline Reinforcement Learning
Lars van der Laan, Nathan Kallus
Marginalized importance weighting evaluates a target policy by reweighting offline state-action samples with its discounted occupancy ratio, characterized by an adjoint Bellman equ…
Reward Transfer from Inverse Reinforcement Learning: A Coupled Minimax Approach
Guang-Yuan Hao, Lars van der Laan, Aurélien Bibaut +1
We study the transfer of rewards learned using inverse reinforcement learning from expert demonstrations in one environment to reinforcement learning in a new, different environmen…
Inverse Reinforcement Learning with Just Classification and a Few Regressions
Lars van der Laan, Nathan Kallus, Aurelien Bibaut
Inverse reinforcement learning (IRL) aims to infer rewards from observed behavior, but rewards are not identified from the policy alone: many reward--value pairs can rationalize th…
Hybrid Meta-learners for Estimating Heterogeneous Treatment Effects
Zhongyuan Liang, Lars van der Laan, Ahmed Alaa
Estimating conditional average treatment effects (CATE) from observational data involves modeling decisions that differ from supervised learning, particularly concerning how to reg…
Efficient Inference for Inverse Reinforcement Learning and Dynamic Discrete Choice Models
Lars van der Laan, Aurelien Bibaut, Aurélien Bibaut +1
In many sequential decision-making problems, researchers observe actions but not the rewards that drive behavior, yet still wish to evaluate and compare counterfactual policies. In…