169 citations · 437 across the 13 of their papers we have counts for
6 papers · 1 filter
Graph Domain Adaptation: A Generative View
Ruichu Cai, Fengzhu Wu, Zijian Li +3
Recent years have witnessed tremendous interest in deep learning on graph-structured data. Due to the high cost of collecting labeled graph-structured data, domain adaptation is im…
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation
Chaochao Lu, Biwei Huang, Ke Wang +3
Reinforcement learning (RL) algorithms usually require a substantial amount of interaction data and perform well only for specific tasks in a fixed environment. In some scenarios s…
Modelling EHR timeseries by restricting feature interaction
Kun Zhang, Yuan Xue, Gerardo Flores +3
Time series data are prevalent in electronic health records, mostly in the form of physiological parameters such as vital signs and lab tests. The patterns of these values may be s…
Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs
AmirEmad Ghassami, Alan Yang, Negar Kiyavash +1
The main approach to defining equivalence among acyclic directed causal graphical models is based on the conditional independence relationships in the distributions that the causal…
On Learning Invariant Representation for Domain Adaptation
Han Zhao, Remi Tachet des Combes, Kun Zhang +1
Due to the ability of deep neural nets to learn rich representations, recent advances in unsupervised domain adaptation have focused on learning domain-invariant features that achi…
Causality Refined Diagnostic Prediction
Marcus Klasson, Kun Zhang, Bo C. Bertilson +2
Applying machine learning in the health care domain has shown promising results in recent years. Interpretable outputs from learning algorithms are desirable for decision making by…