Yoshitaka KAMEYA

Assistant Professor at Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology

Research area

Machine Learning, Artificial Intelligence (Probabilistic Logic Programming, Statistical Language Modeling)

Contact information

Address:

Department of Computer Science,
Graduate School of Information Science and Engineering,
Tokyo Institute of Technology,
2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8552, Japan.

Room:

501, West 8E Bldg.

Phone/Fax:

+81-3-5734-2186

E-mail:

kameya [at] mi.cs.titech.ac.jp

Dissertation

Publications (peer-reviewed)

• Kameya, Y. and Sato, T.: RP-growth: Top-k mining of relevant patterns with minimum support raising. Proceedings of the 2012 SIAM International Conference on Data Mining (SDM-2012), pp.816–827, 2012.
• Ishihata, M., Kameya, Y. and Sato, T.: Variational Bayes inference for logic-based probabilistic models on BDDs. Proceedings of the 21st International Conference on Inductive Logic Programming (ILP-2011), to appear, 2011.
• Kameya, Y.: Time series discretization via MDL-based histogram density estimation. Proceedings of the 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2011), pp.732–739, 2011.
• Kameya, Y., Nakamura, S., Iwasaki, T. and Sato, T.: Verbal characterization of probabilistic clusters using minimal discriminative propositions. Proceedings of the 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2011), pp.873–875, 2011.
• Kameya, Y. and Prayoonsri, C.: Pattern-based preservation of building blocks in genetic algorithms. Proceedings of the 2011 IEEE Congress on Evolutionary Computation (CEC-2011), pp.2578–2585, 2011.
• Synnaeve, G., Inoue, K., Doncescu, A., Nabeshima, H., Kameya, Y., Ishihata, M. and Sato, T.: Kinetic models and qualitative abstraction for relational learning in systems biology. Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS-2011), 2011.
• Ishihata M., Kameya, Y., Sato, T. and Minato, S.: An EM algorithm on BDDs with order encoding for logic-based probabilistic models. Proceedings of the 2nd Asian Conference on Machine Learning (ACML-2010), pp.161-176, 2010.
• Kameya, Y., Synnaeve, G., Doncescu, A., Inoue, K. and Sato, T.: A Bayesian hybrid approach to unsupervised time series discretization. Proceedings of the 2010 Conference on Technologies and Applications of Artificial Intelligence (TAAI-2010), pp.342-349, 2010.
• Zhou, N.-F., Kameya, Y. and Sato, T.: Mode-directed tabling for dynamic programming, machine learning, and constraint solving. Proceedings of the 22nd International Conference on Tools with Artificial Intelligence (ICTAI-2010), Vol.2, pp.213–218, 2010.
• Sneyers, J., Meert, W., Vennekens, J., Kameya, Y. and Sato, T.: CHR(PRISM)-based probabilistic logic learning. Theory and Practice of Logic Programming, Vol.10, No.4–6, pp.433–447, 2010.
• Inoue, K., Sato, T., Ishihata, M., Kameya, Y. and Nabeshima, H.: Evaluating abductive hypotheses using an EM algorithm on BDDs. Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI-2009), pp.810–815, 2009.
• Sato, T., Kameya, Y., Kurihara, K.: Variational Bayes via propositionalized probability computation in PRISM. Annals of Mathematics and Artificial Intelligence, Vol.54, No.1–3, pp.135–158, 2009.
• Kameya, Y., Kumagai, J. and Kurata, Y.: Accelerating genetic programming by frequent subtree mining. Proceedings of the 2008 Genetic and Evolutionary Computation Conference (GECCO-2008), pp.1203–1210, 2008.
• Ishihata, M., Kameya, Y., Sato, T. and Minato, S.: Propositionalizing the EM algorithm by BDDs. Late breaking papers at the 18th International Conference on Inductive Logic Programming (ILP-2008), 2008.
• Sato, T. and Kameya, Y.: New advances in logic-based probabilistic modeling by PRISM. In Probabilistic Inductive Logic Programming, LNCS 4911, Springer, pp.118–155, 2008.
• Kurihara, K., Kameya, Y. and Sato, T.: Discovering concepts from word co-occurrences with a relational model. Transactions of the Japanese Society for Artificial Intelligence, Vol.22, No.2, pp.218–226, 2007.
• Kurihara, K., Kameya, Y. and Sato, T.: A frequency-based stochastic blockmodel. Proceedings of IBIS 2006.
• Izumi, Y., Kameya, Y. and Sato, T.: Parallel EM learning for symbolic-statistical models. Proceedings of the International Workshop on Data-Mining and Statistical Science (DMSS-2006), pp.133–140, 2006.
• Sato,T. and Kameya, Y.: Negation elimination for finite PCFGs. Proceedings of the International Symposium on Logic-based Program Synthesis and Transformation 2004 (LOPSTR-04), later selectively published as Logic-based Program Synthesis and Transformation, Springer LNCS 3573, S. Etalle (Ed.), pp.117–132, 2005.
• Sato, T., Kameya, Y. and Zhou, N.-F.: Generative modeling with failure in PRISM. Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI-2005), pp.847–852, 2005.
• Kameya, Y., Sato, T. and Zhou, N.-F.: Yet more efficient EM learning for parameterized logic programs by inter-goal sharing. Proceedings of the 16th European Conference on Artificial Intelligence (ECAI-2004), pp.490-494, 2004.
• Sato, T. and Kameya, Y.: Statistical abduction with tabulation. Computational Logic: Logic Programming and Beyond, Kakas, A. and Sadri, F. (eds), pp.567–587, LNAI Vol.2408, Springer, 2002.
• Sato, T. and Kameya, Y.: Parameter learning of logic programs for symbolic-statistical modeling. Journal of Artificial Intelligence Research (JAIR), Vol.15, pp.391–454, 2001.
• Sato, T., Abe, S., Kameya, Y., and Shirai, K.: A separate-and-learn approach to EM learning of PCFGs. Proceedings of the 6th Natural Language Processing Pacific Rim Symposium (NLPRS-2001), pp.255–262, 2001.
• Kameya, Y. and Sato, T.: Efficient EM learning with tabulation for parameterized logic programs. Proceedings of the 1st International Conference on Computational Logic (CL-2000), LNAI Vol.1861, pp.269 - 294, 2000.
• Kameya, Y., Ueda, N., and Sato, T.: A graphical method for parameter learning of symbolic-statistical models. Proceedings of the 2nd International Conference on Discovery Science (DS-99), LNAI Vol.1721, pp.264 - 276, 1999.
• Ueda, N., Kameya, Y., and Sato, T.: A parameter updating of stochastic context-free grammars in linear time on the number of productions. In Proceedings of the 1st IMC workshop, 1999.
• Sato, T. and Kameya, Y.: PRISM: A symbolic-statistical modeling language. Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI-97), pp.1330–1335, 1997.

Publications (unreviewed)

• Kameya, Y., Nakamura, S., Iwasaki, T. and Sato, T.: Verbal Characterization of Probabilistic Clusters using Minimal Discriminative Propositions, Technical Report TR11-0001, Dept. of Computer Science, Tokyo Institute of Technology, August, 2011.
• Kameya, Y., Nakamura, S., Iwasaki, T. and Sato, T.: Characterizing probabilistic clusters by minimal discriminative propositions. Extended abstract at the 7th Workshop on Learning with Logics and Logics for Learning (LLLL-2011), 2011.
• Ishihata, M., Kameya, Y., Sato, T. and Minato, S.: Propositionalizing the EM algorithm by BDDs. Late breaking papers at the 18th International Conference on Inductive Logic Programming (ILP-2008), 2008.
• Sato, T. and Kameya, Y.: Learning through failure. Dagstuhl Seminar Proceedings on Probabilistic, Logical and Relational Learning - Towards a Synthesis, 2006.
• Sato, T. and Kameya, Y.: A Viterbi-like algorithm and EM learning for statistical abduction. Proceedings of UAI-2000 Workshop on Fusion of Domain Knowledge with Data for Decision Support, 2000.
• Kameya, Y. and Sato, T.: Abstracting human's decision process by PRISM. Proceedings of the 1st International Conference on Discovery Science (DS-98), pp.389-390, 1998.

Electric versions of the papers above, please visit here.

Software

• PRISM — Prolog-based programming language for probabilistic modeling
• NBCTK — General-purpose probabilistic clustering tool

Links

• Sato (Taisuke) Laboratory

Last update: Dec. 25, 2011