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Found 13 papers, 3 papers with code
Multiplier Bootstrap-based Exploration
Despite the great interest in the bandit problem, designing efficient algorithms for complex models remains challenging, as there is typically no analytical way to quantify uncertainty.
STEEL: Singularity-aware Reinforcement Learning
In this paper, we propose a new batch RL algorithm without requiring absolute continuity in the setting of an infinite-horizon Markov decision process with continuous states and actions.
Heterogeneous Synthetic Learner for Panel Data
In the new era of personalization, learning the heterogeneous treatment effect (HTE) becomes an inevitable trend with numerous applications.
Mining the Factor Zoo: Estimation of Latent Factor Models with Sufficient Proxies
Latent factor model estimation typically relies on either using domain knowledge to manually pick several observed covariates as factor proxies, or purely conducting multivariate analysis such as principal component analysis.
Safe Exploration for Efficient Policy Evaluation and Comparison
High-quality data plays a central role in ensuring the accuracy of policy evaluation.
Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework
In this paper, we propose a unified meta-learning framework for a general class of structured bandit problems where the parameter space can be factorized to item-level.
A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Markets
In this paper we consider large-scale fleet management in ride-sharing companies that involve multiple units in different areas receiving sequences of products (or treatments) over time.
Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models
How to explore efficiently is a central problem in multi-armed bandits.
Pattern Transfer Learning for Reinforcement Learning in Order Dispatching
Order dispatch is one of the central problems to ride-sharing platforms.
Deeply-Debiased Off-Policy Interval Estimation
Off-policy evaluation learns a target policy's value with a historical dataset generated by a different behavior policy.
Multi-Objective Model-based Reinforcement Learning for Infectious Disease Control
Severe infectious diseases such as the novel coronavirus (COVID-19) pose a huge threat to public health.
Batch Policy Learning in Average Reward Markov Decision Processes
The performance of the method is illustrated by simulation studies and an analysis of a mobile health study promoting physical activity.
Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making
The Markov assumption (MA) is fundamental to the empirical validity of reinforcement learning.
