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Eiji Takimoto

Name: dblp XML data dump
Creator: Schloss Dagstuhl - Leibniz Center for Informatics
Published: 1993
License: https://creativecommons.org/publicdomain/zero/1.0/
Keywords: dblp, XML, computer science, scholarly publications, metadata

◀ ▶ joint publications with Ken-ichiro Moridomi

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Publications

2021
[c54]
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  dblp key:
  - conf/acml/LiuMHT21
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  - https://dblp.org/rec/conf/acml/LiuMHT21
Yaxiong Liu, Ken-ichiro Moridomi, Kohei Hatano, Eiji Takimoto:
An online semi-definite programming with a generalised log-determinant regularizer and its applications. ACML 2021: 1113-1128
2020
[i4]
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  dblp key:
  - journals/corr/abs-2012-05632
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  - https://dblp.org/rec/journals/corr/abs-2012-05632
Yaxiong Liu, Ken-ichiro Moridomi, Kohei Hatano, Eiji Takimoto:
A generalised log-determinant regularizer for online semi-definite programming and its applications. CoRR abs/2012.05632 (2020)
2018
[j23]
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  dblp key:
  - journals/ieicet/MoridomiHT18
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  - https://dblp.org/rec/journals/ieicet/MoridomiHT18
Ken-ichiro Moridomi, Kohei Hatano, Eiji Takimoto:
Online Linear Optimization with the Log-Determinant Regularizer. IEICE Trans. Inf. Syst. 101-D(6): 1511-1520 (2018)
[j22]
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  - journals/ieicet/MoridomiHT18a
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Ken-ichiro Moridomi, Kohei Hatano, Eiji Takimoto:
Tighter Generalization Bounds for Matrix Completion Via Factorization Into Constrained Matrices. IEICE Trans. Inf. Syst. 101-D(8): 1997-2004 (2018)
2017
[i1]
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  dblp key:
  - journals/corr/abs-1710-10002
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  - https://dblp.org/rec/journals/corr/abs-1710-10002
Ken-ichiro Moridomi, Kohei Hatano, Eiji Takimoto:
Online linear optimization with the log-determinant regularizer. CoRR abs/1710.10002 (2017)
2016
[c43]
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  dblp key:
  - conf/alt/NakazonoMHT16
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  - https://dblp.org/rec/conf/alt/NakazonoMHT16
Takumi Nakazono, Ken-ichiro Moridomi, Kohei Hatano, Eiji Takimoto:
A Combinatorial Metrical Task System Problem Under the Uniform Metric. ALT 2016: 276-287
2014
[c38]
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  dblp key:
  - conf/acml/MoridomiHTT14
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Ken-ichiro Moridomi, Kohei Hatano, Eiji Takimoto, Koji Tsuda:
Online matrix prediction for sparse loss matrices. ACML 2014

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