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07/05/2023
Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization
Simple regret minimization is a critical problem in learning optimal tre...
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06/26/2023
Supervised Pretraining Can Learn In-Context Reinforcement Learning
Large transformer models trained on diverse datasets have shown a remark...
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06/24/2023
Waypoint Transformer: Reinforcement Learning via Supervised Learning with Intermediate Targets
Despite the recent advancements in offline reinforcement learning via su...
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06/21/2023
Automated Reminders Reduce Incarceration for Missed Court Dates: Evidence from a Text Message Experiment
Millions of Americans must attend mandatory court dates every year. To b...
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04/11/2023
Reinforcement Learning Tutor Better Supported Lower Performers in a Math Task
Resource limitations make it hard to provide all students with one of th...
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02/19/2023
Estimating Optimal Policy Value in General Linear Contextual Bandits
In many bandit problems, the maximal reward achievable by a policy is of...
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01/26/2023
Model-based Offline Reinforcement Learning with Local Misspecification
We present a model-based offline reinforcement learning policy performan...
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11/16/2022
Giving Feedback on Interactive Student Programs with Meta-Exploration
Developing interactive software, such as websites or games, is a particu...
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11/03/2022
Oracle Inequalities for Model Selection in Offline Reinforcement Learning
In offline reinforcement learning (RL), a learner leverages prior logged...
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10/16/2022
Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data
Offline reinforcement learning (RL) can be used to improve future perfor...
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07/01/2022
Offline Policy Optimization with Eligible Actions
Offline policy optimization could have a large impact on many real-world...
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12/30/2021
Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster Learning
Online reinforcement learning (RL) algorithms are often difficult to dep...
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11/28/2021
Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation
Off-policy policy evaluation methods for sequential decision making can ...
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11/15/2021
Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects
There are a number of available methods that can be used for choosing wh...
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10/27/2021
Play to Grade: Testing Coding Games as Classifying Markov Decision Process
Contemporary coding education often presents students with the task of d...
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09/18/2021
Learning to be Fair: A Consequentialist Approach to Equitable Decision-Making
In the dominant paradigm for designing equitable machine learning system...
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08/19/2021
Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning
Actor-critic methods are widely used in offline reinforcement learning p...
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07/21/2021
Design of Experiments for Stochastic Contextual Linear Bandits
In the stochastic linear contextual bandit setting there exist several m...
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04/26/2021
Universal Off-Policy Evaluation
When faced with sequential decision-making problems, it is often useful ...
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11/19/2020
Online Model Selection for Reinforcement Learning with Function Approximation
Deep reinforcement learning has achieved impressive successes yet often ...
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08/18/2020
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration
There has been growing progress on theoretical analyses for provably eff...
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07/16/2020
Provably Good Batch Reinforcement Learning Without Great Exploration
Batch reinforcement learning (RL) is important to apply RL algorithms to...
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07/12/2020
Learning Abstract Models for Strategic Exploration and Fast Reward Transfer
Model-based reinforcement learning (RL) is appealing because (i) it enab...
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04/13/2020
Power-Constrained Bandits
Contextual bandits often provide simple and effective personalization in...
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04/02/2020
Value Driven Representation for Human-in-the-Loop Reinforcement Learning
Interactive adaptive systems powered by Reinforcement Learning (RL) have...
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03/12/2020
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding
When observed decisions depend only on observed features, off-policy pol...
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02/29/2020
Learning Near Optimal Policies with Low Inherent Bellman Error
We study the exploration problem with approximate linear action-value fu...
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02/10/2020
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
Off-policy evaluation in reinforcement learning offers the chance of usi...
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01/31/2020
Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning
Accurate reporting of energy and carbon usage is essential for understan...
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12/12/2019
Sublinear Optimal Policy Value Estimation in Contextual Bandits
We study the problem of estimating the expected reward of the optimal po...
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11/16/2019
Missingness as Stability: Understanding the Structure of Missingness in Longitudinal EHR data and its Impact on Reinforcement Learning in Healthcare
There is an emerging trend in the reinforcement learning for healthcare ...
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11/05/2019
Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy
While maximizing expected return is the goal in most reinforcement learn...
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11/03/2019
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs
In order to make good decision under uncertainty an agent must learn fro...
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10/15/2019
Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling
We establish a connection between the importance sampling estimators typ...
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06/18/2019
Directed Exploration for Reinforcement Learning
Efficient exploration is necessary to achieve good sample efficiency for...
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05/23/2019
Learning When-to-Treat Policies
Many applied decision-making problems have a dynamic component: The poli...
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05/14/2019
Combining Parametric and Nonparametric Models for Off-Policy Evaluation
We consider a model-based approach to perform batch off-policy evaluatio...
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04/17/2019
PLOTS: Procedure Learning from Observations using Subtask Structure
In many cases an intelligent agent may want to learn how to mimic a sing...
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04/17/2019
Off-Policy Policy Gradient with State Distribution Correction
We study the problem of off-policy policy optimization in Markov decisio...
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02/05/2019
Separating value functions across time-scales
In many finite horizon episodic reinforcement learning (RL) settings, it...
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01/01/2019
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds
Strong worst-case performance bounds for episodic reinforcement learning...
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12/03/2018
Distilling Information from a Flood: A Possibility for the Use of Meta-Analysis and Systematic Review in Machine Learning Research
The current flood of information in all areas of machine learning resear...
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11/07/2018
Policy Certificates: Towards Accountable Reinforcement Learning
The performance of a reinforcement learning algorithm can vary drastical...
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07/03/2018
Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters
In this work, we consider the problem of estimating a behaviour policy f...
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06/15/2018
Sample-Efficient Deep RL with Generative Adversarial Tree Search
We propose Generative Adversarial Tree Search (GATS), a sample-efficient...
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06/01/2018
Strategic Object Oriented Reinforcement Learning
Humans learn to play video games significantly faster than state-of-the-...
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05/23/2018
When Simple Exploration is Sample Efficient: Identifying Sufficient Conditions for Random Exploration to Yield PAC RL Algorithms
Efficient exploration is one of the key challenges for reinforcement lea...
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05/23/2018
Representation Balancing MDPs for Off-Policy Policy Evaluation
We study the problem of off-policy policy evaluation (OPPE) in RL. In co...
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02/13/2018
Efficient Exploration through Bayesian Deep Q-Networks
We propose Bayesian Deep Q-Network (BDQN), a practical Thompson sampling...
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11/29/2017