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All Technical Committee Conferences (Searched in: Recent 10 Years)
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Search Results: Conference Papers |
Conference Papers (Available on Advance Programs) (Sort by: Date Descending) |
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Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
MI |
2017-09-25 16:00 |
Chiba |
Chiba Univ. (Chiba) |
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 |
(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 |
NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] |
2017-06-24 10:20 |
Okinawa |
Okinawa Institute of Science and Technology (Okinawa) |
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 (Okinawa) |
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 (Okinawa) |
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. (Kyoto) |
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 (Okinawa) |
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 (Okinawa) |
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 (Osaka) |
Clustering by using quantum annealing Shu Tanaka (Waseda Univ.), Issei Sato (Univ. of Tokyo), Kenichi Kurihara (Google), Seiji Miyashita, Hiroshi Nakagawa (Univ. of Tokyo) |
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