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Committee Date Time Place Paper Title / Authors Abstract Paper #
IBISML 2017-03-07
Tokyo Tokyo Institute of Technology A study on minimizing size of sparse model optimization problem: exploiting safe rules for keeping and removing variables
Masayuki Karasuyama (NIT/NIMS/JST), Atsushi Shibagaki (NIT), Ichiro Takeuchi (NIT/NIMS/RIKEN/NIMS) IBISML2016-107
 [more] IBISML2016-107
IBISML 2016-11-17
Kyoto Kyoto Univ. Empirical risk minimization for interval data and its applications to privacy preservations
Hiroyuki Hanada, Toshiyuki Takada, Atsushi Shibagaki (NITech), Jun Sakuma (Univ. of Tsukuba), Ichiro Takeuchi (NITech) IBISML2016-89
In this research, for machine learning tasks, we consider that the values in the training data are given as intervals an... [more] IBISML2016-89
PRMU, IPSJ-CVIM, IBISML [detail] 2016-09-06
Toyama   A proposal on quick sensitivity analysis of empirical risk minimization problems
Hiroyuki Hanada, Atsushi Shibagaki (NITech), Jun Sakuma (Univ. of Tsukuba), Ichiro Takeuchi (NITech) PRMU2016-80 IBISML2016-35
For a training data set consisting of $n$ vectors of $d$ dimensions, we consider obtaining a training result from it by ... [more] PRMU2016-80 IBISML2016-35
NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] 2016-07-06
Okinawa Okinawa Institute of Science and Technology Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling
Atsushi Shibagaki, Masayuki Karasuyama (NIT), Kohei Hatano (Kyushu Univ.), Ichiro Takeuchi (NIT) IBISML2016-4
 [more] IBISML2016-4
IBISML 2015-03-06
Kyoto Kyoto University Model selection with approximate validation error guarantee for (L^2_2) regularized convex loss minimization problems
Atsushi Shibagaki, Yoshiki Suzuki, Ichiro Takeuchi (NIT) IBISML2014-96
In this paper we propose a new algorithm that can select an approximately optimal regularization parameter in a class of... [more] IBISML2014-96
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