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papers

Publications (31)

cs.AI2017

Decoupling Learning Rules from Representations

Philip S. Thomas, Christoph Dann, Emma Brunskill

cs.LG2016

Latent Contextual Bandits and their Application to Personalized Recommendations for New Users

Li Zhou, Emma Brunskill

cs.LG2019

Policy Certificates: Towards Accountable Reinforcement Learning

Christoph Dann, Lihong Li, Wei Wei +1

stat.ML2013

Regret Bounds for Reinforcement Learning with Policy Advice

Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill

cs.LG2012

CORL: A Continuous-state Offset-dynamics Reinforcement Learner

Emma Brunskill, Bethany Leffler, Lihong Li +2

cs.LG2013

Sample Complexity of Multi-task Reinforcement Learning

Emma Brunskill, Lihong Li

cs.DL2018

Distilling Information from a Flood: A Possibility for the Use of Meta-Analysis and Systematic Review in Machine Learning Research

Peter Henderson, Emma Brunskill

cs.AI2017

On Ensuring that Intelligent Machines Are Well-Behaved

Philip S. Thomas, Bruno Castro da Silva, Andrew G. Barto +1

cs.LG2017

Sample Efficient Feature Selection for Factored MDPs

Zhaohan Daniel Guo, Emma Brunskill

cs.AI2014

Efficient Planning under Uncertainty with Macro-actions

Ruijie He, Emma Brunskill, Nicholas Roy

cs.AI2012

RAPID: A Reachable Anytime Planner for Imprecisely-sensed Domains

Emma Brunskill, Stuart Russell

cs.LG2018

Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning

Christoph Dann, Tor Lattimore, Emma Brunskill

stat.ML2016

Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning

Christoph Dann, Emma Brunskill

cs.LG2019

When Simple Exploration is Sample Efficient: Identifying Sufficient Conditions for Random Exploration to Yield PAC RL Algorithms

Yao Liu, Emma Brunskill

cs.AI2017

Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation

Zhaohan Daniel Guo, Philip S. Thomas, Emma Brunskill

cs.AI2017

Policy Gradient Methods for Reinforcement Learning with Function Approximation and Action-Dependent Baselines

Philip S. Thomas, Emma Brunskill

cs.AI2017

Sample Efficient Policy Search for Optimal Stopping Domains

Karan Goel, Christoph Dann, Emma Brunskill

cs.LG2016

Importance Sampling with Unequal Support

Philip S. Thomas, Emma Brunskill

cs.LG2019

Separating value functions across time-scales

Joshua Romoff, Peter Henderson, Ahmed Touati +3

cs.LG2016

Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning

Philip S. Thomas, Emma Brunskill

cs.GT2012

Incentive Decision Processes

Sashank J. Reddi, Emma Brunskill

cs.LG2019

PLOTS: Procedure Learning from Observations using Subtask Structure

Tong Mu, Karan Goel, Emma Brunskill

cs.AI2018

Fast Exploration with Simplified Models and Approximately Optimistic Planning in Model Based Reinforcement Learning

Ramtin Keramati, Jay Whang, Patrick Cho +1

cs.LG2018

Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters

Aniruddh Raghu, Omer Gottesman, Yao Liu +4

stat.ML2013

Sequential Transfer in Multi-armed Bandit with Finite Set of Models

Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill

cs.LG2019

Representation Balancing MDPs for Off-Policy Policy Evaluation

Yao Liu, Omer Gottesman, Aniruddh Raghu +4

cs.LG2015

The Online Coupon-Collector Problem and Its Application to Lifelong Reinforcement Learning

Emma Brunskill, Lihong Li

cs.LG2019

Directed Exploration for Reinforcement Learning

Zhaohan Daniel Guo, Emma Brunskill

cs.LG2019

Off-Policy Policy Gradient with State Distribution Correction

Yao Liu, Adith Swaminathan, Alekh Agarwal +1

cs.LG2016

A PAC RL Algorithm for Episodic POMDPs

Zhaohan Daniel Guo, Shayan Doroudi, Emma Brunskill

cs.CL2017

Generalized Grounding Graphs: A Probabilistic Framework for Understanding Grounded Commands

Thomas Kollar, Stefanie Tellex, Matthew Walter +8