Jun'ichi Takeuchi's Publication List
Refereed Journal Papers

B. Sun, T. Ban, C. Han, T. Takahashi, K. Yoshioka,
J. Takeuchi, A. Sarrafzadeh, M. Qiu, & D. Inoue :
“
Leveraging Machine Learning Techniques to Identify Deceptive Decoy
Documents Associated With Targeted Email Attacks,
”
IEEE Access, vol. 9, pp. 87962  87971, May 19, 2021.

C. Han, J. Shimamura, T. Takahashi, D. Inoue, J. Takeuchi &
K. Nakao:
“
Realtime Detection of Global Cyberthreat Based on Darknet by
Estimating Anomalous Synchronization Using Graphical Lasso,”
IEICE transactions (D),
Vol. E103D, No. 10, pp. 21132124, Oct. 2020

M. Kawakita & J. Takeuchi:
“
Minimum Description Length Principle in Supervised
Learning with Application to Lasso,”
IEEE Transactions on Information Theory,
vol. 66, no. 7, pp. 42454269, July 2020.

Y. Takeishi & J. Takeuchi:
“
An Improved Analysis of Least Squares Superposition Codes
with Bernoulli Dictionary ,” Japanese
Journal of Statistics and Data Science, 2, pp. 591613, September 2019.

N. Takahashi, J. Katayama, M. Seki, & J. Takeuchi:
“
A unified global convergence analysis of multiplicative update rules for nonnegative matrix factorization,”
Computational Optimization and Applications, 71, pp. 221250, March 2018.

M. Kawakita & J. Takeuchi:
“A
Note on Model Selection for Small Sample Regression,”
Machine Learning,
Vol. 106, No. 11, pp. 18391862, November 2017.

Y. Takeishi, M. Kawakita, & J. Takeuchi:
“Least Squares Superposition Codes with Bernoulli Dictionary are
Still Reliable at Rates up to Capacity,”
IEEE Transactions on Information Theory,
Vol. 60, No. 5, pp. 27372750, May 2014.

M. Kawakita & J. Takeuchi:
“Safe Semisupervised Learning Based on Weighted Likelihood,”
Neural Networks,
Vol. 53, pp. 146164, May 2014.

Y. Feng, Y. Hori, K. Sakurai, & J. Takeuchi:
“A Behaviorbased Method for Detecting Distributed Scan Attacks in Darknets,”
Journal of Information Processing,
Vol. 21, No. 3, pp. 527538, July 2013.

J. Takeuchi, T. Kawabata & A. R. Barron:
“Properties of Jeffreys Mixture for Markov Sources,”
IEEE Transactions on Information Theory,
Vol. 59, No. 1, pp. 438457, January 2013.

J. Takeuchi:
“Stochastic complexity, channel capacity, and universal portfolio,”
Journal of Math for Industry,
JMI2010B10, pp.213225, 2010.

H. Matsuzoe, J. Takeuchi & S. Amari:
“Equiaffine structures on statistical manifolds and
Bayesian statistics,”
Differential Geometry and Its Applications,
vol. 24/6, pp. 567578, December 2006.

J. Takeuchi & K. Yamanishi:
“A Unifying Framework for Detecting Outliers and Change Points
from NonStationary Time Series Data,”
IEEE Transactions on Knowledge and Data
Engineering,
Vol. 18, No. 4, pp.482489, 2006.

J. Takeuchi & S. Amari:
“αParallel Prior and Its Properties,”
IEEE Transactions on Information Theory,
Vol. 51, No. 3, pp. 10111023, March 2005.

K. Yamanishi, J. Takeuchi, G. Williamas, & P. Milne:
“Online Unsupervised Oultlier Detection Using Finite
Mixtures with Discounting Learning Algorithms,”
Data Mining and Knowleged Discovery Journal, 8 (3): 275300, May 2004.

N. Abe, J. Takeuchi, & M. Warmuth :
“Polynomial Learnability of Stochastic Rules with
respect to the KLdivergence and Quadratic Distance,”
IEICE Transactions (D),
Vol.E84D No.3 pp. 299316, 2001.

J. Takeuchi, N. Abe, & S. Amari :
“The LobPass problem,”
Journal of Computer and System Sciences,
Vol. 61, No. 3, pp. 523557, 2000.

A. Nakamura, J. Takeuchi, & N. Abe :
“Efficient distributionfree population learning of simple concepts,”
Annals of Mathematics and Artificial Intelligence,
23, pp. 5382, 1998.

J. Takeuchi :
“Characterization of the Bayes estimator and the MDL
estimator for exponential families,”
IEEE Transactions on Information Theory,
Vol. 43, No. 4, pp. 11651174, 1997.

J. Takeuchi :
“Improved sample complexity bounds for parameter estimation,”
IEICE Transactions (D),
Vol. E78D, No. 5, pp. 526531, 1995.
Refereed Conference Papers (International)

C. Han, J. Takeuchi, T. Takahashi, & D. Inoue :
“Automated Detection of Malware Activities Using Nonnegative
Matrix Factorization,”
Proc. of The 20th IEEE International Conference on Trust, Security and
Privacy in Computing and Communications,
Shenyang, China, October 2022, 2021.

R. Kawasoe, C. Han, R. Isawa, T. Takahashi and J. Takeuchi :
“Investigating Behavioral Differences between IoT Malware via Function Call Sequence Graphs,”
Proc. of
the 36th ACM/SIGAPP Symposium On Applied Computing (SAC 2021),
March 2021.

K. Miyamoto & J. Takeuchi :
“On MDL Estimation for Simple Contaminated Gaussian Location
Families,”
Proc. of
the 2020 International Symposium on Information Theory and Its Applications
(ISITA 2020), Octeber 2020.

T. He, C. Han, R. Isawa, T. Takahashi, S. Kijima, J. Takeuchi :
& K. Nakao,
“A Fast Algorithm for Constructing Phylogenetic Trees with Application to IoT Malware Clustering,”
Proc. of the 2019 Data Mining and Cybersecurity Workshop,
associated with the 24th International Conference on Neural
Information Processing, December 2019.

S. Kitazaki, M. Kawakita, Y. Jitsumatsu, S. Kuhara, A. Hiwatashi,
& J. Takeuchi :
“Magnetic Resonance Angiography Image Restoration by Super Resolution Based on Deep Learning,”
Proc. of 2019 World Congress of
European Society for Magnetic Resonance in Medicine and Biology
(ESMRMB Congress 2019),
pp. S322S323, October 2019.
 K. Mimura & J. Takeuchi:
“Dynamics of Damped Approximate Message Passing Algorithms,”
Proc. of 2019 IEEE Information Theory Workshop,
Visby, Gotland, Sweden, August 2528, 2019.
 C. Han, J. Shimamura, T. Takahashi, D. Inoue, M. Kawakita, J. Takeuchi & K. Nakao:
“RealTime Detection of Malware Activities by Analyzing
Darknet Traffic Using Graphical Lasso,”
Proc. The 18th IEEE International Conference on Trust, Security and Privacy in
Computing and Communications,
Rotorua, New Zealand, August 58, 2019.
 K. Miyamoto, A. R. Barron & J. Takeuchi:
“Improved MDL Estimators Using Local Exponential Family Bundles Applied to Mixture Families,”
Proc. 2019 IEEE International Symposium on Information Theory,
pp. 1442  1446, Paris, France, July 712, 2019.
 M. Eto, M. Kawakita & J. Takeuchi:
“Asymptotic Behavior of Typical Sets and the Smallest High Probability Set,”
Proc. of the International Symposium on Information Theory and Its Applications 2018,
Singapore, October 2831, 2018.
 J. Takeuchi & H. Nagaoka:
“Information Geometry of The Family of Markov Kernels Defined by A Context Tree,”
Proc. of 2017 IEEE Information Theory Workshop,
Kaohsiung, Taiwan, November 610, 2017.
 S. Tanaka, Y. Kawamura, M. Kawakita, N. Murata, & J. Takeuchi:
“MDL criterion for NMF with Application to Botnet Detection,”
the 2016 Data Mining and Cybersecurity Workshop,
associated with
the 23rd International Conference on Neural Information Processing,
Kyoto, Japan, Oct. 1621, 2016.
 C. Han, K. Kono, M. Kawakita, & J. Takeuchi:
“Botnet Detection Using Graphical Lasso with Graph Density,”
the 2016 Data Mining and Cybersecurity Workshop,
associated with
the 23rd International Conference on Neural Information Processing,
Kyoto, Japan, Oct. 1621, 2016.
 Y. Takeishi & J. Takeuchi:
“An Improved Upper Bound on Block Error Probability of Least Squares Superposition Codes with Unbiased Bernoulli Dictionary,”
Proc. of 2016 IEEE International Symposium on Information Theory,
pp. 1168  1172, Barcelona, Spain, July 1015, 2016.
 M. Kawakita & J. Takeuchi:
“Barron and Cover's Theory in Supervised Learning and its Application to Lasso,”
The 33rd International Conference on Machine Learning,
New York, New York, USA, June 1924, 2016.
 J. Takeuchi & A. R. Barron:
“Stochastic complexity for tree models,”
Proc. of 2014 IEEE Information Theory Workshop,
pp. 223227, Hobart, Tasmania, Australia, November 25, 2014.
 N. Takahashi, J. Katayama, & J. Takeuchi:
“A Generalized Sufficient Condition for Global Convergence of Modified Multiplicative Updates for NMF,”
Proc. of 2014 International Symposium on Nonlinear Theory and its Applications,
(NOLTA2014),,
pp. 4447, Luzern, Switzerland, September 1418, 2014.
 J. Takeuchi & A. R. Barron:
“Asymptotically minimax regret for models with hidden variables,”
Proc. of 2014 IEEE International Symposium on Information Theory,
pp. 30373041,
Honolulu, HI, USA, June 29  July 4, 2014.
 J. Katayama, N. Takahashi, & J. Takeuchi:
“Boundedness of modified multiplicative updates for nonnegative matrix factorization,”
Proc. of The Fifth IEEE International Workshop on
Computational Advances in MultiSensor Adaptive Processing
(CAMSAP2013), pp.252255, December 2013.
 J. Takeuchi & A. R. Barron:
“Asymptotically Minimax Regret by Bayes Mixtures for
Nonexponential Families,”
Proc. of 2013 IEEE Information Theory Workshop,
pp. 204208, Sevilie, Spain, September 913, 2013.
 Y. Takeishi, M. Kawakita, & J. Takeuchi:
“Least Squares Superposition Codes with Bernoulli Dictionary are
Still Reliable at Rates up to Capacity,”
Proc. of 2013 IEEE International Symposium on Information Theory,
pp. 13961400, Istanbul, Turkey, July 712, 2013.
 S. Yamauchi, M. Kawakita, & J. Takeuchi:
“Botnet Detection based on Nonnegative Matrix
Factorization and the MDL Principle,”
Proc. of
the 19th International Conference on Neural Information Processing,
Doha, Qatar, Nov. 1215, 2012.

K. Yamaguchi, M. Kawakita, N. Takahashi, & J. Takeuchi:
“Information Theoretic Limit of Single Frame Superresolution,”
Proc. of
The Third International Conference on Emerging Security Technologies,
Lisbon, Portugal, Sep. 911, 2012.
 M. Tsurusaki & J. Takeuchi :
“Constant Markov Portfolio and Its Application to Universal
Portfolio with Side Information,”
Proc. of 2012 IEEE International Symposium on Information
Theory, Boston, USA, July 16, 2012.

M. Kawakita, R. Izumi, J. Takeuchi, Y. Hu, T. Takamori, &
H. Kameyama :
“Acceleration Technique for Boosting Classification and Its
Application to Face Detection,”
Proc. of The Third Asian Conference on Machine Learning ,
Taoyuan, Taiwan, Nov. 1315, 2011

M. Tsurusaki & J. Takeuchi :
“Stochastic Interpretation of Universal Portfolio and Generalized
Target Classes,”
Proc. of 2011 IEEE International Symposium on Information
Theory,
pp. 494498, SaintPetersburg, Russia, July 31  Aug. 5, 2011.

M. Kawakita, Y. Oie, & J. Takeuchi:
“A Note on Model Selection for Small Sample Regression,”
Proc. of 2010 International Symposium on Information Theory and Its
Applications, Taichung, Taiwan, Oct. 2010.

J. Takeuchi :
“Fisher Information Determinant and Stochastic complexity
for Markov Models,”
Proc. of 2009 IEEE International Symposium on Information
Theory, pp. 18941898, Seoul, Korea, 2009.

J. Takeuchi & T. Kawabata :
“Exponential Curvature of Markov Models,”
Proc. of 2007 IEEE International Symposium on Information Theory,
pp. 28912895, Nice, France, 2007.

T. Nakata & J. Takeuchi:
“Mining Traffic Data from ProbeCar System for Travel Time Prediction,”
Proc. of the tenth ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining, ACM Press
(KDD2004), 2004.

S. Morinaga, K. Yamanishi, & J. Takeuchi:
“Distributed Cooperative Mining for Information Consortia,”
Proc. of the Nineth ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining, ACM
Press
(KDD2003), 2003.

K. Yamanishi & J. Takeuchi:
“A Unifying Framework for Detecting Outliers and Change Points
from NonStationary Time Series Data,”
Proc. of the Eighth ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining, ACM
Press
(KDD2002), 2002.

K. Yamanishi & J. Takeuchi:
“Discovering Outlier Filtering Rules from Unlabeled Data
Combining Supervised Learners with Unsupervised Learners,”
Proc. of the Seventh ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining, ACM
Press,
(KDD2001), 2001.

K. Yamanishi, J. Takeuchi, G. Williams, & P. Milne:
“Online Unsupervised Oultlier Detection
Using Finite Mixtures with Discounting Learning Algorithms,”
Proc. of the Sixth ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining, ACM
Press
(KDD2000), pp:320324, 2000.

J. Takeuchi & A. R. Barron :
“Asymptotically minimax regret by Bayes mixtures,”
Proc. of 1998 IEEE International Symposium on Information
Theory, 1998.

J. Takeuchi & T. Kawabata :
“Approximation of Bayes code for Markov sources,”
Proc. of 1995 IEEE International Symposium on Information
Theory, p.391, 1995.

J. Takeuchi :
“Characterization of the Bayes estimator and the MDL
estimator for exponential families,”
Proc. of 1995 IEEE International Symposium on Information
Theory (long presentation), p.228, 1995.

A. Nakamura, N. Abe & J. Takeuchi :
“Efficient distributionfree population learning of simple concepts,”
Proc. of the 5th
International Workshop on Algorithmic Learning Theory,
pp. 500515, 1994.

N. Abe & J. Takeuchi :
“The `LobPass' problem and
an online learning model of rational choice,”
Proc. of the 7th Annual Conference on
Computational Learning Theory, pp. 422428, 1993.

J. Takeuchi :
“Some improved sample complexity bounds
in the probabilistic PAC learning model,”
Proc. of the 3rd Workshop on Algorithmic Learning Theory,
pp. 208219, 1992.

N. Abe, J. Takeuchi, & M. Warmuth :
“Polynomial learnability of probabilistic concepts
with respect to the KullbackLeibler divergence,”
Proc. of the 4th annual Workshop on Computational
Learning Theory, pp. 277289, 1991.
Refereed Conference Papers (Japan domestic)

T. Nakata & J. Takeuchi:
“Hierarchical State Space Model for Longterm Prediction
(in Japanese),”
Proc. of the Eighth Workshop on InformationBased Induction Sciences
(IBIS2005), 2005.

J. Takeuchi & T. Kawabata:
“
Exponential Curvature and Jeffreys
Mixture Prediction Strategy for Markov Model (in Japanese),”
Proc. of the Seventh Workshop on InformationBased Induction Sciences
(IBIS2004), 2004.
 J. Takeuchi, T. Kawabata, & A. R. Barron:
“
Properties of Jeffreys mixture for Markov sources,”
Proc. of the fourth Workshop on InformationBased
Induction Sciences
(IBIS2001),
pp. 327332, 2001.

J. Takeuchi :
“
On minimax regret with respect to families of
stationary stochastic processes (in Japanese),”
Proc. of the third Workshop on Information Based
Induction Sciences, 2000.
Books and Articles

J. Takeuchi:
“Mdl principle and stochastic complexity,”
SUUGAKU Seminar, vol. 8¡¤pp.3843, 2016.

J. Takeuchi:
“An introduction to the minimum description length principle,”
in A Mathematical Approach to Research Problems of Science and
Technology, pp. 279296, Springer, 2014. (book chapter)

S. Konishi & J. Takeuchi (authors)¡¤M. Wakayama (editor):
Statistical Modeling/Information Theory and Learning Theory,
Kodansha, 2008. (in Japanese)

J. Takeuchi, A. R. Barron & T. Kawabata:
“
Statistical curvature and stochastic complexity,”
Proc. of the 2nd Symposium on
Information Geometry and Its Applications,
pp. 2936, 2006.

K. Yamanishi, J. Takeuchi, Y. Maruyama:
“Three Methods for Statistical Anomaly Detection (in Japanese),”
IPSJ Magazine (Joho Shori),
Vol. 46, No. 1, pp. 3440, 2005.

N. Abe, K. Yamanishi, A. Nakamura, H. Mamitsuka, J.Takeuchi,
& H. Li:
“Distributed and Active Learning,”
The Foundations of RealWorld Intelligendce, Oct. 2001.

J. Takeuchi & K. Yamanishi:
“
Statistical outlier detection in data mining (in Japanese),”
Bulletin of the Japan Society for Industrial and Applied
Mathnematics (Ouyou Suuri), Vol. 10, No. 3, 2001.

J. Takeuchi:
“Asymptotically Minimax Codes by Bayes Procedures (in Japanese),”
Proc. of IEICE Society Conference, October 1998.

J. Takeuchi:
“
Stochastic complexity and Jeffreys mixture prediction
strategies (in Japanese),”
Proc. of the first Workshop on Information Based
Induction Sciences, pp. 916, 1998.

A. R. Barron & J. Takeuchi:
“Mixture models achieving optimal coding regret,”
Proc. of 1998 IEEE Inform. Theory Workshop, 1998.
Other Conference Papers (selected)
 J. Takeuchi:
“
Geometry of Markov Chains, Finite State Machines, and Tree Models,”
IEICE Technical Reports (IT201440), pp. 159164, 2014.
 J. Takeuchi & T. Kawabata:
“
Tree Source and Stochastic Complexity, (in Japanese)”
Proc. of Shannon Theory Workshop 2006,
September 2006.
 J. Takeuchi & A. R. Barron:
“
Robustly minimax codes for universal data compression,”
Proc. of
the 21st Symposium on Information Theory and its Applications
(SITA'98), 1998.
 J. Takeuchi & A. R. Barron:
“
Asymptotically minimax regret for exponential families,”
Proc. of
the 20th Symposium on Information Theory and its Applications
(SITA'97), pp. 665668, 1997.
One of best papers at SITA'97.
 J. Takeuchi & K. Kawabata:
“On data compression algorithms by Bayes coding
for Markov sources (in Japanese),”
Proc. of the 17th Symposium on Information Theory
and its Applications (SITA'94),
pp.513516, 1994.
Technical Memos
 J. Takeuchi & H. Nagaoka:
“
On Asymptotic Exponential Family of Markov Sources and Exponential Family of
Markov Kernels, ”
May 2017.
 J. Takeuchi & A. R. Barron:
“
Some Inequality for Models with Hidden Variables, ”
January 2014.
 J. Takeuchi & A. R. Barron:
“
Some Inequality for Mixture Families, ”
October 2013.
Last modified: Thu Oct 28 10:54:10 JST 2021