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11/07/2022
Policy evaluation from a single path: Multi-step methods, mixing and mis-specification
We study non-parametric estimation of the value function of an infinite-...
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03/11/2022
Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism
Offline reinforcement learning, which seeks to utilize offline/historica...
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02/10/2022
Adaptive and Robust Multi-task Learning
We study the multi-task learning problem that aims to simultaneously ana...
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09/24/2021
Optimal policy evaluation using kernel-based temporal difference methods
We study methods based on reproducing kernel Hilbert spaces for estimati...
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07/13/2021
PU-Flow: a Point Cloud Upsampling Networkwith Normalizing Flows
Point cloud upsampling aims to generate dense point clouds from given sp...
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05/03/2021
Learning Good State and Action Representations via Tensor Decomposition
The transition kernel of a continuous-state-action Markov decision proce...
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03/25/2021
Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning
This paper considers batch Reinforcement Learning (RL) with general valu...
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02/06/2021
Bootstrapping Statistical Inference for Off-Policy Evaluation
Bootstrapping provides a flexible and effective approach for assessing t...
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11/08/2020
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient
This paper provides a statistical analysis of high-dimensional batch Rei...
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02/21/2020
Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation
This paper studies the statistical theory of batch data reinforcement le...
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06/01/2019
Learning low-dimensional state embeddings and metastable clusters from time series data
This paper studies how to find compact state embeddings from high-dimens...
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11/06/2018
State Aggregation Learning from Markov Transition Data
State aggregation is a model reduction method rooted in control theory a...
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10/14/2018