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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 11 of 11  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
CAS, SIP, VLD, MSS 2022-06-16
Aomori Hachinohe Institute of Technology
(Primary: On-site, Secondary: Online)
Equal Opportunity in Robust Optimization for Unit Commitment Problem Considering Suppression of Renewable Energy
Ichiro Toyoshima (TOSHIBA ESS), Pierre-Louis Poirion (RIKEN AIP), Tomohide Yamazaki, Kota Yaguchi, Masayuki Kubota, Ryota Mizutani (TOSHIBA ESS), Akiko Takeda (The University of Tokyo) CAS2022-2 VLD2022-2 SIP2022-33 MSS2022-2
 [more] CAS2022-2 VLD2022-2 SIP2022-33 MSS2022-2
IBISML 2020-10-20
Online Online IBISML2020-14  [more] IBISML2020-14
IBISML 2019-03-06
Tokyo RIKEN AIP Shapelet-based Multiple-Instance Learning
Daiki Suehiro, Kohei Hatano (Kyushu Univ./RIKEN AIP), Eiji Takimoto (Kyushu Univ.), Shuji Yamamoto, Kenichi Bannai (Keio Univ./RIKEN AIP), Akiko Takeda (The Univ. of Tokyo/RIKEN AIP) IBISML2018-112
 [more] IBISML2018-112
MSS, CAS, IPSJ-AL [detail] 2018-11-12
Shizuoka   [Invited Talk] Robust Optimization and its Application to Supervised Learning
Akiko Takeda (U.Tokyo) CAS2018-67 MSS2018-43
There are various uncertainties in real-world problems. When formulating them as mathematical optimization problems, we ... [more] CAS2018-67 MSS2018-43
IBISML 2017-11-10
Tokyo Univ. of Tokyo IBISML2017-85 We consider binary classification problems using local features of objects. One of motivating applications is time-serie... [more] IBISML2017-85
IBISML 2015-11-26
Ibaraki Epochal Tsukuba [Poster Presentation] Robustification of Learning Algorithms using Hinge-loss
Takafumi Kanamori (Nagoya Univ.), Shuhei Fujiwara (TopGate), Akiko Takeda (Univ. of Tokyo) IBISML2015-71
We propose a unified formation of robust learning methods for classification and regression problems.
In the learnin... [more]
IBISML 2015-11-27
Ibaraki Epochal Tsukuba [Poster Presentation] An Efficient Accelerated Proximal Gradient Method for Unified Binary Classification Model
Naoki Ito, Akiko Takeda (UTokyo), Kim-Chuan Toh (NUS) IBISML2015-93
In this paper, we develop an efficient general optimization algorithm for a unified formulation of various binary classi... [more] IBISML2015-93
IBISML 2014-11-17
Aichi Nagoya Univ. [Poster Presentation] Breakdown Point of Robust Support Vector Machine
Takafumi Kanamori (Nagoya Univ.), Shuhei Fujiwara, Akiko Takeda (Univ. of Tokyo) IBISML2014-41
The support vector machine (SVM) is one of the most successful learning methods for solving classification
problems. D... [more]
IBISML 2014-11-17
Aichi Nagoya Univ. [Poster Presentation] Exact SVM Training by Wolfe's Minimum Norm Point Algorithm
Masashi Kitamura, Akiko Takeda, Satoru Iwata (Univ. of Tokyo) IBISML2014-43
(Advance abstract in Japanese is available) [more] IBISML2014-43
IBISML 2013-11-12
Tokyo Tokyo Institute of Technology, Kuramae-Kaikan [Poster Presentation] Global Solvers for Variational Bayesian Low-rank Subspace Clustering
Shinichi Nakajima (Nikon), Akiko Takeda (Univ. of Tokyo), S. Derin Babacan (Google), Masashi Sugiyama (Tokyo Inst. of Tech.), Ichiro Takeuchi (Nagoya Inst. of Tech.) IBISML2013-37
Variational Bayesian (VB) learning, known to be a promising approximation method to Bayesian learning,
is generally per... [more]
IBISML 2011-03-28
Osaka Nakanoshima Center, Osaka Univ. Enumerating Feature-Sets with Submodularity
Yoshinobu Kawahara (Osaka Univ.), Koji Tsuda (AIST), Takashi Washio (Osaka Univ.), Akiko Takeda (Keio Univ.), Shin-ichi Minato (Hokkaido Univ.) IBISML2010-113
Selecting relevant features is a fundamental task in machine learning. Although many approaches have been investigated s... [more] IBISML2010-113
 Results 1 - 11 of 11  /   
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