Jun'ichi Takeuchi's Publication List

Refereed Journal Papers

1. K. Miyamoto, M. Iida, C. Han, T. Ban, T. Takahashi, & J. Takeuchi :
   “ Consolidating Packet-Level Features for Effective Network Intrusion Detection: A Novel Session-Level Approach,” IEEE Access, early access, November 2023.
2. T. Sultana, S. Kurosaka, Y. Jitsumatsu, S. Kuhara, & J. Takeuchi:
   “ Brain Tumor Classification using Under-Sampled k-Space Data: A Deep Learning Approach,” IEICE transactions (D), Vol. E106-D, No. 11, pp. 1831-1841, November 2023.
3. Y. Takeishi, M. Iida, & J. Takeuchi :
   “ Approximate Spectral Decomposition of Fisher Information Matrix for Simple ReLU Networks,” Neural Networks, Volume 164, pp. 691-706, July 2023.
4. T. He, C. Han, R. Isawa, T. Takahashi, S. Kijima, & J. Takeuchi :
   “ Scalable and Fast Algorithm for Constructing Phylogenetic Trees with Application to IoT Malware Clustering,” IEEE Access, vol. 11, pp. 8240-8253, January 2023.
5. R. Ishibashi, K. Miyamoto, C. Han, T. Ban, T. Takahashi & J. Takeuchi :
   “ Generating Labeled Training Datasets Towards Unified Network Intrusion Detection Systems,” IEEE Access, vol. 10, pp. 53972-53986, May 2022.
6. C. Han, J. Takeuchi, T. Takahashi, & D. Inoue ;
   “ Dark-TRACER: Early Detection Framework for Malware Activity Based on Anomalous Spatiotemporal Patterns” IEEE Access, vol. 10, pp. 13038-13058, February 2022.
7. 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.
8. C. Han, J. Shimamura, T. Takahashi, D. Inoue, J. Takeuchi, & K. Nakao:
   “ Real-time Detection of Global Cyberthreat Based on Darknet by Estimating Anomalous Synchronization Using Graphical Lasso,” IEICE transactions (D), Vol. E103-D, No. 10, pp. 2113-2124, Oct. 2020
9. 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. 4245-4269, July 2020.
10. Y. Takeishi & J. Takeuchi:
    “ An Improved Analysis of Least Squares Superposition Codes with Bernoulli Dictionary ,” Japanese Journal of Statistics and Data Science, 2, pp. 591-613, September 2019.
11. 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. 221-250, March 2018.
12. M. Kawakita & J. Takeuchi:
    “A Note on Model Selection for Small Sample Regression,” Machine Learning, Vol. 106, No. 11, pp. 1839-1862, November 2017.
13. 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. 2737-2750, May 2014.
14. M. Kawakita & J. Takeuchi:
    “Safe Semi-supervised Learning Based on Weighted Likelihood,” Neural Networks, Vol. 53, pp. 146-164, May 2014.
15. Y. Feng, Y. Hori, K. Sakurai, & J. Takeuchi:
    “A Behavior-based Method for Detecting Distributed Scan Attacks in Darknets,” Journal of Information Processing, Vol. 21, No. 3, pp. 527-538, July 2013.
16. J. Takeuchi, T. Kawabata & A. R. Barron:
    “Properties of Jeffreys Mixture for Markov Sources,” IEEE Transactions on Information Theory, Vol. 59, No. 1, pp. 438-457, January 2013.
17. J. Takeuchi:
    “Stochastic complexity, channel capacity, and universal portfolio,” Journal of Math for Industry, JMI2010B-10, pp.213-225, 2010.
18. H. Matsuzoe, J. Takeuchi & S. Amari:
    “Equiaffine structures on statistical manifolds and Bayesian statistics,” Differential Geometry and Its Applications, vol. 24/6, pp. 567-578, December 2006.
19. J. Takeuchi & K. Yamanishi:
    “A Unifying Framework for Detecting Outliers and Change Points from Non-Stationary Time Series Data,” IEEE Transactions on Knowledge and Data Engineering, Vol. 18, No. 4, pp.482-489, 2006.
20. J. Takeuchi & S. Amari:
    “α-Parallel Prior and Its Properties,” IEEE Transactions on Information Theory, Vol. 51, No. 3, pp. 1011-1023, March 2005.
21. K. Yamanishi, J. Takeuchi, G. Williams, & P. Milne:
    “On-line Unsupervised Oultlier Detection Using Finite Mixtures with Discounting Learning Algorithms,” Data Mining and Knowleged Discovery Journal, 8 (3): 275-300, May 2004.
22. N. Abe, J. Takeuchi, & M. Warmuth :
    “Polynomial Learnability of Stochastic Rules with respect to the KL-divergence and Quadratic Distance,” IEICE Transactions (D), Vol.E84-D No.3 pp. 299-316, 2001.
23. J. Takeuchi, N. Abe, & S. Amari :
    “The Lob-Pass problem,” Journal of Computer and System Sciences, Vol. 61, No. 3, pp. 523-557, 2000.
24. A. Nakamura, J. Takeuchi, & N. Abe :
    “Efficient distribution-free population learning of simple concepts,” Annals of Mathematics and Artificial Intelligence, 23, pp. 53-82, 1998.
25. J. Takeuchi :
    “Characterization of the Bayes estimator and the MDL estimator for exponential families,” IEEE Transactions on Information Theory, Vol. 43, No. 4, pp. 1165-1174, 1997.
26. J. Takeuchi :
    “Improved sample complexity bounds for parameter estimation,” IEICE Transactions (D), Vol. E78D, No. 5, pp. 526-531, 1995.

Refereed Conference Papers (International)

1. M. Iida, Y. Takeishi, & J. Takeuchi :
   “On Fisher Information Matrix for Simple Neural Networks With Softplus Activation,” Proc. of the 2022 IEEE International Symposium in Information Theory, Espoo, Finland, June 2022.
2. K. Miyamoto, H. Goto, R. Ishibashi, H. Chansu, T. Ban, T. Takahashi, & J. Takeuchi:
   “Malicious Packet Classification Based on Neural Network Using Kitsune Features,” Proc. of the Second International Conference on Intelligent Systems and Patterns Recognition, March 2022.
3. T. He, C. Han, T. Takahashi, S. Kijima, & J. Takeuchi :
   “ Scalable and Fast Hierarchical Clustering of IoT Malware Using Active Data Selection,,” Proc. of the Sixth International Conference on Fog and Mobile Edge Computing (FMEC), Gandia, Spain, December 6-9, 2021.
4. 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 20-22, 2021.
5. 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.
6. 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.
7. 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.
8. 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. S322-S323, October 2019.
9. K. Mimura & J. Takeuchi:
   “Dynamics of Damped Approximate Message Passing Algorithms,” Proc. of 2019 IEEE Information Theory Workshop, Visby, Gotland, Sweden, August 25-28, 2019.
10. C. Han, J. Shimamura, T. Takahashi, D. Inoue, M. Kawakita, J. Takeuchi & K. Nakao:
    “Real-Time 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 5-8, 2019.
11. 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 7-12, 2019.
12. 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 28-31, 2018.
13. 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 6-10, 2017.
14. 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. 16-21, 2016.
15. 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. 16-21, 2016.
16. 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 10-15, 2016.
17. 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 19-24, 2016.
18. J. Takeuchi & A. R. Barron:
    “Stochastic complexity for tree models,” Proc. of 2014 IEEE Information Theory Workshop, pp. 223-227, Hobart, Tasmania, Australia, November 2-5, 2014.
19. 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. 44-47, Luzern, Switzerland, September 14-18, 2014.
20. J. Takeuchi & A. R. Barron:
    “Asymptotically minimax regret for models with hidden variables,” Proc. of 2014 IEEE International Symposium on Information Theory, pp. 3037-3041, Honolulu, HI, USA, June 29 - July 4, 2014.
21. 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 Multi-Sensor Adaptive Processing (CAMSAP2013), pp.252-255, December 2013.
22. J. Takeuchi & A. R. Barron:
    “Asymptotically Minimax Regret by Bayes Mixtures for Non-exponential Families,” Proc. of 2013 IEEE Information Theory Workshop, pp. 204-208, Sevilie, Spain, September 9-13, 2013.
23. 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. 1396-1400, Istanbul, Turkey, July 7-12, 2013.
24. S. Yamauchi, M. Kawakita, & J. Takeuchi:
    “Botnet Detection based on Non-negative Matrix Factorization and the MDL Principle,” Proc. of the 19th International Conference on Neural Information Processing, Doha, Qatar, Nov. 12-15, 2012.
25. 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. 9-11, 2012.
26. 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 1-6, 2012.
27. 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. 13-15, 2011
28. M. Tsurusaki & J. Takeuchi :
    “Stochastic Interpretation of Universal Portfolio and Generalized Target Classes,” Proc. of 2011 IEEE International Symposium on Information Theory, pp. 494-498, Saint-Petersburg, Russia, July 31 - Aug. 5, 2011.
29. 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.
30. J. Takeuchi :
    “Fisher Information Determinant and Stochastic complexity for Markov Models,” Proc. of 2009 IEEE International Symposium on Information Theory, pp. 1894-1898, Seoul, Korea, 2009.
31. J. Takeuchi & T. Kawabata :
    “Exponential Curvature of Markov Models,” Proc. of 2007 IEEE International Symposium on Information Theory, pp. 2891-2895, Nice, France, 2007.
32. T. Nakata & J. Takeuchi:
    “Mining Traffic Data from Probe-Car System for Travel Time Prediction,” Proc. of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press (KDD2004), 2004.
33. 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.
34. K. Yamanishi & J. Takeuchi:
    “A Unifying Framework for Detecting Outliers and Change Points from Non-Stationary Time Series Data,” Proc. of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press (KDD2002), 2002.
35. 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.
36. K. Yamanishi, J. Takeuchi, G. Williams, & P. Milne:
    “On-line 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:320-324, 2000.
37. J. Takeuchi & A. R. Barron :
    “Asymptotically minimax regret by Bayes mixtures,” Proc. of 1998 IEEE International Symposium on Information Theory, 1998.
38. J. Takeuchi & T. Kawabata :
    “Approximation of Bayes code for Markov sources,” Proc. of 1995 IEEE International Symposium on Information Theory, p.391, 1995.
39. 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.
40. A. Nakamura, N. Abe & J. Takeuchi :
    “Efficient distribution-free population learning of simple concepts,” Proc. of the 5th International Workshop on Algorithmic Learning Theory, pp. 500-515, 1994.
41. N. Abe & J. Takeuchi :
    “The `Lob-Pass' problem and an on-line learning model of rational choice,” Proc. of the 7th Annual Conference on Computational Learning Theory, pp. 422-428, 1993.
42. J. Takeuchi :
    “Some improved sample complexity bounds in the probabilistic PAC learning model,” Proc. of the 3rd Workshop on Algorithmic Learning Theory, pp. 208-219, 1992.
43. N. Abe, J. Takeuchi, & M. Warmuth :
    “Polynomial learnability of probabilistic concepts with respect to the Kullback-Leibler divergence,” Proc. of the 4th annual Workshop on Computational Learning Theory, pp. 277-289, 1991.

Refereed Conference Papers (Japan domestic)

1. T. Nakata & J. Takeuchi:
   “Hierarchical State Space Model for Long-term Prediction (in Japanese),” Proc. of the Eighth Workshop on Information-Based Induction Sciences (IBIS2005), 2005.
2. J. Takeuchi & T. Kawabata:
   “ Exponential Curvature and Jeffreys Mixture Prediction Strategy for Markov Model (in Japanese),” Proc. of the Seventh Workshop on Information-Based Induction Sciences (IBIS2004), 2004.
3. J. Takeuchi, T. Kawabata, & A. R. Barron:
   “ Properties of Jeffreys mixture for Markov sources,” Proc. of the fourth Workshop on Information-Based Induction Sciences (IBIS2001), pp. 327-332, 2001.
4. 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

1. J. Takeuchi:
   “MDL principle and stochastic complexity,” SUUGAKU Seminar, vol. 8，pp.38-43, 2016.
2. J. Takeuchi:
   “An introduction to the minimum description length principle,” in A Mathematical Approach to Research Problems of Science and Technology, pp. 279-296, Springer, 2014. (book chapter)
3. S. Konishi & J. Takeuchi (authors)，M. Wakayama (editor):
   Statistical Modeling/Information Theory and Learning Theory, Kodansha, 2008. (in Japanese)
4. J. Takeuchi, A. R. Barron & T. Kawabata:
   “ Statistical curvature and stochastic complexity,” Proc. of the 2nd Symposium on Information Geometry and Its Applications, pp. 29--36, 2006.
5. K. Yamanishi, J. Takeuchi, Y. Maruyama:
   “Three Methods for Statistical Anomaly Detection (in Japanese),” IPSJ Magazine (Joho Shori), Vol. 46, No. 1, pp. 34-40, 2005.
6. N. Abe, K. Yamanishi, A. Nakamura, H. Mamitsuka, J.Takeuchi, & H. Li:
   “Distributed and Active Learning,” The Foundations of Real-World Intelligendce, Oct. 2001.
7. 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.
8. J. Takeuchi:
   “Asymptotically Minimax Codes by Bayes Procedures (in Japanese),” Proc. of IEICE Society Conference, October 1998.
9. J. Takeuchi:
   “ Stochastic complexity and Jeffreys mixture prediction strategies (in Japanese),” Proc. of the first Workshop on Information Based Induction Sciences, pp. 9-16, 1998.
10. A. R. Barron & J. Takeuchi:
    “Mixture models achieving optimal coding regret,” Proc. of 1998 IEEE Inform. Theory Workshop, 1998.

Other Conference Papers (selected)

1. J. Takeuchi:
   “ Geometry of Markov Chains, Finite State Machines, and Tree Models,” IEICE Technical Reports (IT2014-40), pp. 159-164, 2014.
2. J. Takeuchi & T. Kawabata:
   “ Tree Source and Stochastic Complexity, (in Japanese)” Proc. of Shannon Theory Workshop 2006, September 2006.
3. 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.
4. 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. 665-668, 1997. One of best papers at SITA'97.
5. 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.513-516, 1994.

Technical Memos

1. J. Takeuchi & H. Nagaoka:
   “ On Asymptotic Exponential Family of Markov Sources and Exponential Family of Markov Kernels, ” May 2017.
2. J. Takeuchi & A. R. Barron:
   “ Some Inequality for Models with Hidden Variables, ” January 2014.
3. J. Takeuchi & A. R. Barron:
   “ Some Inequality for Mixture Families, ” October 2013.