Author name cluster

Ching-Yung Lin

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5 papers

1 author row

AAMAS Conference 2018 Conference Paper

Interpretable Robust Decision Making

Hal James Cooper
Garud Iyengar
Ching-Yung Lin

Interpretable decision making frameworks allow us to easily endow agents with specific goals, risk tolerances, and understanding. Existing decision making systems either forgo interpretability, or pay for it with severely reduced efficiency and large memory requirements. In this paper, we outline DeepID, a neural network approximation of Influence Diagrams, that avoids both pitfalls.

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IJCAI Conference 2013 Conference Paper

One-Class Conditional Random Fields for Sequential Anomaly Detection

Yale Song
Zhen Wen
Ching-Yung Lin
Randall Davis

Sequential anomaly detection is a challenging problem due to the one-class nature of the data (i. e. , data is collected from only one class) and the temporal dependence in sequential data. We present One-Class Conditional Random Fields (OCCRF) for sequential anomaly detection that learn from a one-class dataset and capture the temporal dependence structure, in an unsupervised fashion. We propose a hinge loss in a regularized risk minimization framework that maximizes the margin between each sequence being classified as “normal” and “abnormal. ” This allows our model to accept most (but not all) of the training data as normal, yet keeps the solution space tight. Experimental results on a number of real-world datasets show our model outperforming several baselines. We also report an exploratory study on detecting abnormal organizational behavior in enterprise social networks.

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TIST Journal 2013 Journal Article

Relational term-suggestion graphs incorporating multipartite concept and expertise networks

Jyh-Ren Shieh
Ching-Yung Lin
Shun-Xuan Wang
Ja-Ling Wu

Term suggestions recommend query terms to a user based on his initial query. Suggesting adequate terms is a challenging issue. Most existing commercial search engines suggest search terms based on the frequency of prior used terms that match the leading alphabets the user types. In this article, we present a novel mechanism to construct semantic term-relation graphs to suggest relevant search terms in the semantic level. We built term-relation graphs based on multipartite networks of existing social media, especially from Wikipedia. The multipartite linkage networks of contributor-term, term-category, and term-term are extracted from Wikipedia to eventually form term relation graphs. For fusing these multipartite linkage networks, we propose to incorporate the contributor-category networks to model the expertise of the contributors. Based on our experiments, this step has demonstrated clear enhancement on the accuracy of the inferred relatedness of the term-semantic graphs. Experiments on keyword-expanded search based on 200 TREC-5 ad-hoc topics showed obvious advantage of our algorithms over existing approaches.

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IJCAI Conference 2011 Conference Paper

Mining Longitudinal Network for Predicting Company Value

Yingzi Jin
Ching-Yung Lin
Yutaka Matsuo
Mitsuru Ishizuka

Real-world social networks are dynamic in nature. Companies continue to collaborate, align strategically, acquire, and merge over time, and receive positive/negative impact from other companies. Consequently, their performance changes with time. If one can understand what types of network changes affect a company's value, he/she can predict the future value of the company, grasp industry innovations, and make business more successful. However, it often requires continuous records of relational changes, which are often difficult to track for companies, and the models of mining longitudinal network are quite complicated. In this study, we developed algorithms and a system to infer large-scale evolutionary company networks from public news during 1981--2009. Then, based on how networks change over time, as well as the financial information of the companies, we predicted company profit growth. This is the first study of longitudinal network-mining-based company performance analysis in the literature.

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AAAI Conference 2011 Conference Paper

Towards Evolutionary Nonnegative Matrix Factorization

Fei Wang
Hanghang Tong
Ching-Yung Lin

Nonnegative Matrix Factorization (NMF) techniques has aroused considerable interests from the field of artificial intelligence in recent years because of its good interpretability and computational efficiency. However, in many real world applications, the data features usually evolve over time smoothly. In this case, it would be very expensive in both computation and storage to rerun the whole NMFprocedure after each time when the data feature changing. In this paper, we propose Evolutionary Nonnegative Matrix Factorization (eNMF), which aims to incrementally update the factorized matrices in a computation and space efficient manner with the variation of the data matrix. We devise such evolutionary procedure for both asymmetric and symmetric NMF. Finally we conduct experiments on several real world data sets to demonstrate the efficacy and efficiency of eNMF.

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