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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 9 of 9  /   
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)
 [more]
 Results 1 - 9 of 9  /   
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