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All Technical Committee Conferences (Searched in: All 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 # |
IBISML |
2020-03-11 15:35 |
Kyoto |
Kyoto University (Cancelled but technical report was issued) |
Regret analysis of Thompson sampling using a general beta prior Yuto Kawamura, Toshiyuki Tanaka (Kyoto Univ.) IBISML2019-49 |
For Bernoulli bandits, the asymptotic optimality of Thompson sampling with the uniform prior in terms of the regret has ... [more] |
IBISML2019-49 pp.107-112 |
IT, ISEC, WBS |
2019-03-07 09:30 |
Tokyo |
University of Electro-Communications |
Analysis of Zero-Redundancy Estimator with a Finite Window for Markovian Source
-- When the Statewise Alphabets are Unknown -- Yusuke Hashimoto, Tstutomu Kawabata (Univ. of Electro-Comm.) IT2018-89 ISEC2018-95 WBS2018-90 |
A Bayesian (Laplace or Krichevski-Trofimov) estimator for Markov source can be used to build a lossless
source code. Ho... [more] |
IT2018-89 ISEC2018-95 WBS2018-90 pp.85-90 |
IBISML |
2019-03-06 11:30 |
Tokyo |
RIKEN AIP |
Optimal Kernel for Mode Estimation via Kernel Density Estimation Ryoya Yamasaki, Toshiyuki Tanaka (Kyoto Univ.) IBISML2018-113 |
We have derived kernel functions that minimize the asymptotic mean squared error of the mode estimate, which is defined ... [more] |
IBISML2018-113 pp.59-64 |
IT, ISEC, WBS |
2013-03-07 11:45 |
Osaka |
Kwansei Gakuin Univ., Osaka-Umeda Campus |
Order Estimator of Stationary Markov Sources Using Codeword Length of Universal Code Shinji Kanazawa, Tomohiko Uyematsu (Tokyo Inst. of Tech.) IT2012-65 ISEC2012-83 WBS2012-51 |
Merhav et al. studied the problem of estimating the order of stationary Markov sources. They proposed an order estimator... [more] |
IT2012-65 ISEC2012-83 WBS2012-51 pp.19-24 |
IBISML |
2013-03-05 11:00 |
Aichi |
Nagoya Institute of Technology |
Non-Achievability of Asymptotic Minimax Regret without Knowledge of the Sample Size Kazuho Watanabe (NAIST), Teemu Roos, Petri Myllymaki (Helsinki Inst. for Information Tech.) IBISML2012-101 |
The normalized maximum likelihood (NML) model achieves the minimax regret for coding data of fixed sample size $n$. It i... [more] |
IBISML2012-101 pp.61-67 |
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