セミナー情報

Speaker: Prof. Jiayu Zhou (Michigan State University)

Title: Recent Advances in Multi-Task Learning

Time and Place: 11:00--12:00, Aug.25th 2017,  14th Eng.Bld.Room 522

Abstract: The recent decade has witnessed a surging demand in data analysis, where we built machine learning models for various data analysis tasks. The multi-task learning is a machine learning paradigm that bridges related learning tasks and transfers knowledge among the tasks. The seminar reviews multi-task learning basics and recent advances, including distributed multi-task learning that provides efficient and privacy-preserving learning on distributed data sources; and interactive multi-task learning that solicits and integrates domain knowledge in multi-task learning, including human in the learning loop. The seminar is concluded by a discussion of future directions of multi-task learning. 

 

Biography: Jiayu Zhou is currently an Assistant Professor in the Department of Computer Science and Engineering at Michigan State University. He received his Ph.D. degree in computer science from Arizona State University in 2014. He has a broad research interest in large-scale machine learning and data mining, and biomedical informatics. He served as technical program committee members of premier conferences such as NIPS, ICML, and SIGKDD. Jiayu’s research is supported by National Science Foundation and Office of Naval Research. His papers received the Best Student Paper Award in 2014 IEEE International Conference on Data Mining (ICDM), the Best Student Paper Award at 2016 International Symposium on Biomedical Imaging (ISBI), and Best Paper Award at 2016 IEEE International Conference on Big Data (BigData). 

 


タイトル

情報幾何による順序構造上の統計解析

日時

2016/3/22 16:00--17:30

場所

東京大学工学部6号館セミナー室AD

講演者

杉山磨人

大阪大学産業科学研究所

http://mahito.info/

アブストラクト

順序構造は,数学や計算機科学における本質的な構造のひとつであり,集合の包含,文字列の接頭辞,部分グラフ,有向非巡回グラフにおける到達可能性などの関係が順序構造をなす.本講演では,順序構造をもつ変数集合に情報幾何を導入し,KLダイバージェンスやエントロピーといった情報量の非負直交分解を紹介する.この手法によって,これまで困難だった大規模データにおける変数間の高次の統計的関連が解析できるようになり,ニューロンの発火パターンや遺伝子間相互作用の解析などの幅広い応用分野に適用できる.

 


 

Applying Data Mining to Real-life Crime Investigation

日時

2015/6/29

場所

東京大学工学部14号館522号室

講演者

Prof.  Benjamin Fung

Canada Research Chair in Data Mining for Cybersecurity Associate Professor, School of Information Studies McGill University, Montreal, Canada

http://dmas.lab.mcgill.ca/fung

アブストラクト

Data mining has demonstrated a lot of success in many domains, from direct marking to bioinformatics. Yet, limited research has been conducted to leverage the power of data mining in real-life crime investigation. In this presentation, I will discuss two data mining methods for crime investigation. The first method aims at identifying the true author of anonymous e-mail; I will provide a live software demonstration. The second method is a subject-based search engine that can help investigators retrieve criminal and social network information from a large collection of textual documents.

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