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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| MI, MICT | 2017-11-06 09:20 |
Kagawa | Sunport Hall Takamatsu |  MICT2017-25 MI2017-47 |
(To be available after the conference date) [more] | MICT2017-25 MI2017-47 pp.1-2 |
| MI | 2017-09-25 16:00 |
Chiba | Chiba Univ. | MI2017-45 |
(To be available after the conference date) [more] | MI2017-45 pp.23-24 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2017-09-15 10:00 |
Tokyo | Quantum-Inspired Regression Forest Zeke Xie, Issei Sato (UTokyo) PRMU2017-40 IBISML2017-12 |
We propose a Quantum-Inspired Subspace(QIS) Ensemble Method for generating feature ensembles based on feature selections... [more] | PRMU2017-40 IBISML2017-12 pp.7-17 |
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| NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] | 2017-06-24 10:20 |
Okinawa | Okinawa Institute of Science and Technology | Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags Han Bao (Univ. of Tokyo), Tomoya Sakai, Issei Sato (Univ. of Tokyo/RIKEN), Masashi Sugiyama (RIKEN/Univ. of Tokyo) IBISML2017-3 |
Multiple instance learning (MIL) is a variation of traditional supervised learning problems where data (referred to as b... [more] | IBISML2017-3 pp.55-62 |
| NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] | 2017-06-25 09:30 |
Okinawa | Okinawa Institute of Science and Technology | Expectation Propagation for t-Exponential Family Futoshi Futami, Issei Sato (Univ. of Tokyo/RIKEN), Masashi Sugiyama (RIKEN/Univ. of Tokyo) IBISML2017-6 |
Exponential family distributions are highly useful in machine learning since their calculation can be performed efficien... [more] | IBISML2017-6 pp.179-184 |
| NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] | 2017-06-25 09:55 |
Okinawa | Okinawa Institute of Science and Technology | Stochastic Divergence Minimization for Biterm Topic Model Zhenghang Cui (Univ. of Tokyo), Issei Sato (Univ. of Tokyo/RIKEN), Masashi Sugiyama (RIKEN/Univ. of Tokyo) IBISML2017-7 |
Inferring latent topics of collected short texts is useful for understanding its hidden structure and predicting new con... [more] | IBISML2017-7 pp.185-192 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | Robust supervised learning under uncertainty in dataset shift Weihua Hu, Issei Sato (UTokyo), Masashi Sugiyama (RIKEN/UTokyo) IBISML2016-50 |
When machine learning is deployed in the real world, its performance can be significantly undermined because test data m... [more] | IBISML2016-50 pp.37-44 |
| NC, IPSJ-BIO, IBISML, IPSJ-MPS (Joint) [detail] |
2015-06-23 11:10 |
Okinawa | Okinawa Institute of Science and Technology | Corpus and Topic Scalable Topic Model Soma Yokoi, Issei Sato, Hiroshi Nakagawa (UTokyo) IBISML2015-5 |
It is known that topic model with high dimensional topics improves IR performance like search engines and online adverti... [more] | IBISML2015-5 pp.27-31 |
| NC, IPSJ-BIO, IBISML, IPSJ-MPS (Joint) [detail] |
2015-06-23 13:00 |
Okinawa | Okinawa Institute of Science and Technology | Differential Privacy and Pseudo-Bayesian Posterior Kentaro Minami, Hiromi Arai, Issei Sato, Hiroshi Nakagawa (The University of Tokyo) IBISML2015-7 |
We investigate relationship between differential privacy and pseudo-Bayesian posterior distributions. Recently, Wang, et... [more] | IBISML2015-7 pp.39-46 |
| QIT (2nd) |
2015-05-25 11:20 |
Osaka | Osaka University | Clustering by using quantum annealing Shu Tanaka (Waseda Univ.), Issei Sato (Univ. of Tokyo), Kenichi Kurihara (Google), Seiji Miyashita, Hiroshi Nakagawa (Univ. of Tokyo) |
[more] | |
| IBISML | 2014-11-18 15:00 |
Aichi | Nagoya Univ. | [Poster Presentation]
Asymptotic Analysis of Variational Bayesian Latent Dirichlet Allocation Shinichi Nakajima (TU Berlin), Issei Sato, Masashi Sugiyama (Univ. of Tokyo), Kazuho Watanabe (Toyohashi Univ. of Tech.), Hiroko Kobayashi (Nikon) IBISML2014-64 |
Latent Dirichlet allocation (LDA) is a popular generative model of various objects such as texts and images, where an ... [more] |
IBISML2014-64 pp.219-226 |
| NLC | 2012-12-19 15:55 |
Tokyo | Ookayama Campasu, Tokyo Institute of Technology | Extracting location specific expressions from social media texts Hironori Kato, Eiji Aramaki, Mai Miyabe, Minoru Yoshida, Issei Sato, Hiroshi Nakagawa (Tokyo Univ.) NLC2012-38 |
Recently, social media has attracted attention in many elds, computer science, social science, market- ing and so on. ... [more] |
NLC2012-38 pp.29-34 |
| IBISML | 2011-06-21 11:00 |
Tokyo | Takeda Hall | Quantum annealing for Infinite Mixture Models Issei Sato (Tokyo Univ.), Kenichi Kurihara (Google), Shu Tanaka, Seiji Miyashita, Hiroshi Nakagawa (Tokyo Univ.) IBISML2011-16 |
We develope quantum annealing (QA) for the infinite mixture models. The QA is regarded as a parallelized extension of s... [more] |
IBISML2011-16 pp.111-117 |
| IBISML | 2010-06-14 15:45 |
Tokyo | Takeda Hall, Univ. Tokyo | Hierarchical Pitman-Yor Topic Model Issei Sato, Hiroshi Nakagawa (Univ. of Tokyo.) IBISML2010-7 |
(Advance abstract in Japanese is available) [more] | IBISML2010-7 pp.33-39 |
| DE | 2006-07-13 11:25 |
Niigata | HOTEL SENKEI | Mining Semi-structure for Text with Dependency Structure Issei Sato, Hiroshi Nakagawa (Tokyo Univ.) |
[more] | DE2006-77 pp.161-166 |
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