1 code implementation • 23 Nov 2020 • Tsung-Yu Hsieh, Suhang Wang, Yiwei Sun, Vasant Honavar
Time series data is prevalent in a wide variety of real-world applications and it calls for trustworthy and explainable models for people to understand and fully trust decisions made by AI solutions.
no code implementations • 14 Sep 2019 • Yiwei Sun, Suhang Wang, Xianfeng Tang, Tsung-Yu Hsieh, Vasant Honavar
Real-world graph applications, such as advertisements and product recommendations make profits based on accurately classify the label of the nodes.
no code implementations • 20 Aug 2019 • Yiwei Sun, Suhang Wang, Tsung-Yu Hsieh, Xianfeng Tang, Vasant Honavar
Data from many real-world applications can be naturally represented by multi-view networks where the different views encode different types of relationships (e. g., friendship, shared interests in music, etc.)
no code implementations • 26 May 2019 • Chin-Teng Lin, Kuan-Chih Huang, Yu-Ting Liu, Yang-Yin Lin, Tsung-Yu Hsieh, Nikhil R. Pal, Shang-Lin Wu, Chieh-Ning Fang, Zehong Cao
This investigation extends that study, clarifies some issues related to our earlier work, provides the algorithm for generation of the oversamples, applies the method on several benchmark data sets, and makes application to three Brain Computer Interface (BCI) applications.
no code implementations • 6 Nov 2018 • Yiwei Sun, Ngot Bui, Tsung-Yu Hsieh, Vasant Honavar
Our experiments with several benchmark real-world single view networks show that GFC-based SVNE yields network embeddings that are competitive with or superior to those produced by the state-of-the-art single view network embedding methods when the embeddings are used for labeling unlabeled nodes in the networks.
no code implementations • 4 Sep 2018 • Tsung-Yu Hsieh, Yasser EL-Manzalawy, Yiwei Sun, Vasant Honavar
Many machine learning, statistical inference, and portfolio optimization problems require minimization of a composition of expected value functions (CEVF).