133 citations · 282 across the 33 of their papers we have counts for
8 papers · 1 filter
Treatment Effect Modification by Latent Classes
Hui Lan, Nathan Kallus, Winston Chou
Many treatments have effects that plausibly vary by latent classes. For example, the effect of personalized video recommendations may be stronger for users who seek a new discovery…
Policy Learning with Weak Signals
Benedikt Koch, Winston Chou, Aurélien Bibaut +1
Policy learning in digital experimentation faces three challenges: weak signal-to-noise ratios, rich covariate spaces, and massive data volumes. We formalize this regime by modelin…
Scalable Multi-Task Inverse Reinforcement Learning
Allen Tran, Jia Wan, Nathan Kallus +1
By learning transferable rewards, inverse reinforcement learning (IRL) enables counterfactual evaluation of agents under modified environments. Such transfer places strict requirem…
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…
Recommendation Quality and the Concentration of Consumption: Experimental Evidence from Netflix
Guy Aridor, Winston Chou, Nathan Kallus +3
We study an experiment with 8.5 million users on Netflix's recommender system to measure how improvements in recommendation technology affect the set of products that get consumed.…
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…
Semiparametric inference on identification sets in choice modeling
Antoine Scheid, Jia Wan, Guy Aridor +2
In a discrete choice model, choice probabilities observed for a finite collection of choice sets may not identify a counterfactual choice probability under an unobserved choice set…
Mult-DPO: Multinomial Direct Preference Optimization for Recommender Systems
Yaochen Zhu, Harald Steck, James McInerney +4
Direct preference optimization (DPO) is a simple and effective alignment strategy for large language models (LLMs) based on pairwise preferences. In recommender systems, however, u…