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08/19/2023
Large Language Models as Zero-Shot Conversational Recommenders
In this paper, we present empirical studies on conversational recommenda...
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07/25/2023
Source Condition Double Robust Inference on Functionals of Inverse Problems
We consider estimation of parameters defined as linear functionals of so...
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07/21/2023
JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning
In this paper, we present JoinGym, an efficient and lightweight query op...
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05/25/2023
The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning
While distributional reinforcement learning (RL) has demonstrated empiri...
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05/24/2023
Provable Offline Reinforcement Learning with Human Feedback
In this paper, we investigate the problem of offline reinforcement learn...
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04/20/2023
B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding
Estimating heterogeneous treatment effects from observational data is a ...
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02/10/2023
Minimax Instrumental Variable Regression and L_2 Convergence Guarantees without Identification or Closedness
In this paper, we study nonparametric estimation of instrumental variabl...
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02/07/2023
Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR
In this paper, we study risk-sensitive Reinforcement Learning (RL), focu...
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02/05/2023
Refined Value-Based Offline RL under Realizability and Partial Coverage
In offline reinforcement learning (RL) we have no opportunity to explore...
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01/29/2023
Smooth Non-Stationary Bandits
In many applications of online decision making, the environment is non-s...
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12/29/2022
Near-Optimal Non-Parametric Sequential Tests and Confidence Sequences with Possibly Dependent Observations
Sequential testing, always-valid p-values, and confidence sequences prom...
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12/13/2022
A Review of Off-Policy Evaluation in Reinforcement Learning
Reinforcement learning (RL) is one of the most vibrant research frontier...
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11/11/2022
The Implicit Delta Method
Epistemic uncertainty quantification is a crucial part of drawing credib...
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10/26/2022
Provable Safe Reinforcement Learning with Binary Feedback
Safety is a crucial necessity in many applications of reinforcement lear...
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08/17/2022
Debiased Inference on Identified Linear Functionals of Underidentified Nuisances via Penalized Minimax Estimation
We study generic inference on identified linear functionals of nonunique...
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07/26/2022
Future-Dependent Value-Based Off-Policy Evaluation in POMDPs
We study off-policy evaluation (OPE) for partially observable MDPs (POMD...
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07/12/2022
Learning Bellman Complete Representations for Offline Policy Evaluation
We study representation learning for Offline Reinforcement Learning (RL)...
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06/24/2022
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings
We study reinforcement learning with function approximation for large-sc...
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06/24/2022
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems
We study Reinforcement Learning for partially observable dynamical syste...
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05/23/2022
Robust and Agnostic Learning of Conditional Distributional Treatment Effects
The conditional average treatment effect (CATE) is the best point predic...
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05/20/2022
What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment
The fundamental problem of causal inference – that we never observe coun...
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04/13/2022
Estimating Structural Disparities for Face Models
In machine learning, disparity metrics are often defined by measuring th...
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02/19/2022
Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning
Off-policy evaluation and learning (OPE/L) use offline observational dat...
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02/15/2022
Long-term Causal Inference Under Persistent Confounding via Data Combination
We study the identification and estimation of long-term treatment effect...
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01/15/2022
Treatment Effect Risk: Bounds and Inference
Since the average treatment effect (ATE) measures the change in social w...
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12/21/2021
Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confounding
We study the problem of constructing bounds on the average treatment eff...
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11/16/2021
An Empirical Evaluation of the Impact of New York's Bail Reform on Crime Using Synthetic Controls
We conduct an empirical evaluation of the impact of New York's bail refo...
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10/28/2021
Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processes
In applications of offline reinforcement learning to observational data,...
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10/19/2021
Stateful Offline Contextual Policy Evaluation and Learning
We study off-policy evaluation and learning from sequential data in a st...
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10/06/2021
Residual Overfit Method of Exploration
Exploration is a crucial aspect of bandit and reinforcement learning alg...
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08/09/2021
Controlling for Unmeasured Confounding in Panel Data Using Minimal Bridge Functions: From Two-Way Fixed Effects to Factor Models
We develop a new approach for identifying and estimating average causal ...
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06/15/2021
Control Variates for Slate Off-Policy Evaluation
We study the problem of off-policy evaluation from batched contextual ba...
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06/03/2021
Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning
Empirical risk minimization (ERM) is the workhorse of machine learning, ...
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06/01/2021
Post-Contextual-Bandit Inference
Contextual bandit algorithms are increasingly replacing non-adaptive A/B...
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03/25/2021
Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach
We study the estimation of causal parameters when not all confounders ar...
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02/05/2021
Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency
We offer a theoretical characterization of off-policy evaluation (OPE) i...
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01/31/2021
Fast Rates for the Regret of Offline Reinforcement Learning
We study the regret of reinforcement learning from offline data generate...
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12/21/2020
Fairness, Welfare, and Equity in Personalized Pricing
We study the interplay of fairness, welfare, and equity considerations i...
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12/17/2020
The Variational Method of Moments
The conditional moment problem is a powerful formulation for describing ...
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12/05/2020
Rejoinder: New Objectives for Policy Learning
I provide a rejoinder for discussion of "More Efficient Policy Learning ...
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11/05/2020
Fast Rates for Contextual Linear Optimization
Incorporating side observations of predictive features can help reduce u...
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10/21/2020
Optimal Off-Policy Evaluation from Multiple Logging Policies
We study off-policy evaluation (OPE) from multiple logging policies, eac...
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08/17/2020
Stochastic Optimization Forests
We study conditional stochastic optimization problems, where we leverage...
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07/27/2020
Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders
Off-policy evaluation (OPE) in reinforcement learning is an important pr...
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06/06/2020
Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies
Offline reinforcement learning, wherein one uses off-policy data logged ...
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06/06/2020
Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning
We study the efficient off-policy evaluation of natural stochastic polic...
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05/06/2020
On the Optimality of Randomization in Experimental Design: How to Randomize for Minimax Variance and Design-Based Inference
I study the minimax-optimal design for a two-arm controlled experiment w...
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05/06/2020
DTR Bandit: Learning to Make Response-Adaptive Decisions With Low Regret
Dynamic treatment regimes (DTRs) for are personalized, sequential treatm...
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04/06/2020
Comment: Entropy Learning for Dynamic Treatment Regimes
I congratulate Profs. Binyan Jiang, Rui Song, Jialiang Li, and Donglin Z...
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03/27/2020
