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03/30/2022
Factored Adaptation for Non-Stationary Reinforcement Learning
Dealing with non-stationarity in environments (i.e., transition dynamics...
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10/12/2021
Action-Sufficient State Representation Learning for Control with Structural Constraints
Perceived signals in real-world scenarios are usually high-dimensional a...
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07/06/2021
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning
Most approaches in reinforcement learning (RL) are data-hungry and speci...
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03/26/2021
FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders
We consider the problem of estimating a particular type of linear non-Ga...
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12/16/2020
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation
Reinforcement learning (RL) algorithms usually require a substantial amo...
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10/10/2020
Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Graphs
Causal discovery aims to recover causal structures or models underlying ...
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02/09/2020
Domain Adaptation As a Problem of Inference on Graphical Models
This paper is concerned with data-driven unsupervised domain adaptation,...
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05/26/2019
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models
In many scientific fields, such as economics and neuroscience, we are of...
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03/05/2019
Causal Discovery and Hidden Driving Force Estimation from Nonstationary/Heterogeneous Data
It is commonplace to encounter nonstationary or heterogeneous data. Such...
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01/27/2019
Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data
Autism spectrum disorder (ASD) is one of the major developmental disorde...
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04/12/2018
Causal Generative Domain Adaptation Networks
We propose a new generative model for domain adaptation, in which traini...
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09/27/2015
