133 citations · 282 across the 33 of their papers we have counts for
37 papers
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…
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…
The Value of Personalized Recommendations: Evidence from Netflix
Kevin Zielnicki, Guy Aridor, Aurélien Bibaut +3
Personalized recommendation systems shape much of user choice online, yet their targeted nature makes separating out the value of recommendation and the underlying goods challengin…
Does Weighting Improve Matrix Factorization for Recommender Systems?
Alex Ayoub, Samuel Robertson, Dawen Liang +2
Matrix factorization is a widely used approach for top-N recommendation and collaborative filtering. When implemented on implicit feedback data (such as clicks), a common heuristic…
Rank-GRPO: Training LLM-based Conversational Recommender Systems with Reinforcement Learning
Yaochen Zhu, Harald Steck, Dawen Liang +4
Large language models (LLMs) are reshaping the recommender system paradigm by enabling users to express preferences and receive recommendations through conversations. Yet, aligning…
DiFFPO: Training Diffusion LLMs to Reason Fast and Furious via Reinforcement Learning
Hanyang Zhao, Dawen Liang, Wenpin Tang +2
We propose DiFFPO, Diffusion Fast and Furious Policy Optimization, a unified framework for training masked diffusion large language models (dLLMs) to reason not only better (furiou…
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…
Nonparametric Instrumental Variable Inference with Many Weak Instruments
Lars van der Laan, Nathan Kallus, Aurélien Bibaut
We study inference on linear functionals in the nonparametric instrumental variable (NPIV) problem with a discretely-valued instrument under a many-weak-instruments asymptotic regi…
Interacting Chern insulator transition on the sphere: revealing the Gross-Neveu-Yukawa criticality
Zhi-Qiang Gao, Taige Wang, Dung-Hai Lee
In two spatial dimensions, the transition between topological and trivial Chern insulators exemplifies a class of beyond-Landau critical phenomena. We show that the interaction-dri…
Collaborative Retrieval for Large Language Model-based Conversational Recommender Systems
Yaochen Zhu, Chao Wan, Harald Steck +4
Conversational recommender systems (CRS) aim to provide personalized recommendations via interactive dialogues with users. While large language models (LLMs) enhance CRS with their…
Evaluating Decision Rules Across Many Weak Experiments
Winston Chou, Colin Gray, Nathan Kallus +2
Technology firms conduct randomized controlled experiments ("A/B tests") to learn which actions to take to improve business outcomes. In firms with mature experimentation platforms…
Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands
Lars van der Laan, Aurelien Bibaut, Nathan Kallus +1
We develop a unified framework for automatic debiased machine learning (autoDML) for inference on a broad class of statistical parameters. The framework applies to any smooth funct…
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…
Anytime-Valid Continuous-Time Confidence Processes for Inhomogeneous Poisson Processes
Michael Lindon, Nathan Kallus
Motivated by monitoring the arrival of incoming adverse events such as customer support calls or crash reports from users exposed to an experimental product change, we consider seq…