1 citations · 1 across the 12 of their papers we have counts for
12 papers
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…
Fitted Occupancy-Ratio Evaluation without Bellman Completeness
Lars van der Laan, Nathan Kallus
Occupancy ratios correct distribution shift in offline reinforcement learning and are central to off-policy evaluation. Existing primal-dual and minimax methods typically estimate…
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…
Efficient Inference for Inverse Reinforcement Learning and Dynamic Discrete Choice Models
Lars van der Laan, Aurélien Bibaut, Nathan Kallus
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…
Soft Fitted Q-Iteration without Bellman Completeness: Occupancy Reweighting and Temperature Annealing
Lars van der Laan, Nathan Kallus
Fitted \(Q\)-iteration (FQI) is a standard regression-based method for optimal control in offline reinforcement learning, but its stability under function approximation often relie…
Fitted Q-Evaluation without Bellman Completeness via Occupancy Weighting
Lars van der Laan, Nathan Kallus
Fitted \(Q\)-evaluation (FQE) is a standard regression-based method for off-policy evaluation, but under distribution shift, value-function realizability alone does not ensure conv…
Bellman Calibration for -Learning in Offline Reinforcement Learning
Lars van der Laan, Nathan Kallus
Reliable long-horizon value prediction is difficult in offline reinforcement learning because fitted value methods combine bootstrapping, function approximation, and distribution s…
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…
Semiparametric Double Reinforcement Learning with Applications to Long-Term Causal Inference
Lars van der Laan, David Hubbard, Allen Tran +2
Double reinforcement learning (DRL) provides efficient off-policy inference for policy values in nonparametric Markov decision processes (MDPs), but fully nonparametric estimators…
Self-Calibrating Conformal Prediction
Lars van der Laan, Ahmed M. Alaa
In machine learning, model calibration and predictive inference are essential for producing reliable predictions and quantifying uncertainty to support decision-making. Recognizing…
Targeted Maximum Likelihood Based Estimation for Longitudinal Mediation Analysis
Zeyi Wang, Lars van der Laan, Maya Petersen +3
Causal mediation analysis with random interventions has become an area of significant interest for understanding time-varying effects with longitudinal and survival outcomes. To ta…