Sato Lab, Dept. of Computer Science, Tokyo Inst. of Tech.
Researche topics Publications Members Contact information Related Links Japanese
Go back to the top page

Journal / Conference Papers

2014
2013
  • Ishihata, M. and Sato, T.: Markov Chain Monte Carlo for Bayesian Inference via Propositionalized Probability Computation. Transactions of the Japanese Society for Artificial Intelligence, Vol.28, No.2, pp.230–242, 2013.
    PDF (JSAI J-STAGE, http://dx.doi.org/10.1527/tjsai.28.230)
  • Sato, T. and Meyer, P.: Infinite probability computation by cyclic explanation graphs. Theory and Practice of Logic Programming
    FirstView Article / November 2013, pp 1 - 29 DOI: 10.1017/S1471068413000562, Published online: 04 November 2013
    http://journals.cambridge.org/abstract_S1471068413000562
    http://arxiv.org/abs/1309.0339
  • Sato, T., Kubota, K. and Kameya, Y.: Logic-based Approach to Generatively Defined Discriminative Modeling, to appear in the post proceedings of the 23rd Inernational Conference on Inductive Logic Programming (ILP 2013)
    PDF (Draft)
2012
  • Sato, T. and Kubota, K.: Viterbi training in PRISM. Theory and Practice of Logic Programming, (TPLP), submitted, 2012.
    PDF (http://arxiv.org/abs/1303.5659)
  • Sato, T. and Meyer, P.: Tabling for infinite probability computation. The 28th International Conference on Logic Programming, (ICLP-2012), accepted as Technical Communications, 2012.
    PDF (Draft),
  • Sato, T. and Kubota, K.: Viterbi training in PRISM. ICML Workshop on Statistical Relational Learning, (SRL-2012),2012.
    PDF (workshop page),
  • 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.
    PDF (Online proceedings)
2011
  • Ishihata, M., Sato, T. and Minato, S.: Compiling Bayesian Networks for Parameter Learning based on Shared BDDs. Proceedings of the 24th Australasian Joint Conference on Artificial Intelligence (AI-2011), LNAI 7106, Springer, pp.203-212, December, 2011.
    PDF (Springer)
  • Ishihata, M. and Sato, T.: Bayesian inference for statistical abduction using Markov chain Monte Carlo. Proceedings of the 3rd Asian Conference on Machine Learning (ACML-2011), JMLR Workshop and Conference Proceedings, Vol.20, pp.81-96, November, 2011.
    PDF (Online proceedings)
  • 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.
    PDF (IEEE Xplore)
  • 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 (The full version is available as arXiv:1108.5002 and as Technical Report TR11-0001, Dept. of Computer Science, Tokyo Institute of Technology, August, 2011).
    PDF (IEEE Xplore)
  • Ishihata, M., Kameya, Y. and Sato, T.: Variational Bayes inference for logic-based probabilistic models on BDDs. The 21st International Conference on Inductive Logic Programming (ILP-2011), to appear, 2011.
  • Sato, T., Ishihata, M. and Inoue, K.: Constraint-based probabilistic modeling for statistical abduction. Machine Learning, Vol.83, No.2, pp.241–264, 2011.
    PDF (Springer)
  • Sato, T.: A general MCMC method for Bayesian inference in logic-based probabilistic modeling. Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI-2011), pp.1472–1477, 2011.
    PDF (Online proceedings)
  • 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.
    PDF (IEEE Xplore)
  • 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.
  • 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. Received the Best Student Paper award.
2010
  • Ishihata M., Sato, T. and Minato, S.: Parameter learning for Bayesian networks on Shared Binary Decision Diagrams. Proceedings of the 1st International Workshop on Advanced Methodologies for Bayesian Networks (AMBN-2010), 2010.
  • 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.
    PDF (Online proceedings)
  • 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.
    PDF (IEEE Xplore), Slides, Dataset
  • 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.
    PDF (IEEE Xplore)
  • 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.
    PDF (Cambridge Journals Online)
2009
  • 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.
    PDF (Online proceedings)
  • Sato, T.: Generative modeling by PRISM. Proceedings of the 25th International Conference on Logic Programming (ICLP-2009), LNCS 5649, Springer, pp.24–35, 2009.
    PDF (Springer), PDF (Draft), Slides
  • Sato, T.: Logic-based probabilistic modeling. Proceedings of the 16th Workshop on Logic, Language, Information and Computation (WoLLIC-2009), LNAI 5514, pp.61–71, 2009.
    PDF (Draft), PDF (Springer)
  • 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.
    PDF (Springer)
  • Kurihara, K and Welling, M: Bayesian K-means as a "maximization-expectation" algorithm, Neural Computation, Vol.21, No.4, pp.1145–1172, 2009.
    PDF (MIT Press)
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), pp.44–49, 2008.
    PDF (Draft)
  • Sato, T.: A glimpse of symbolic-statistical modeling by PRISM. Journal of Intelligent Information Systems, Vol.31, No.2, pp.161–176, 2008.
    PDF (Springer)
  • 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.
    PDF (Draft), PDF (ACM DL)
  • 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.
    PDF (Draft), PDF (Springer)
  • Zhou, N.-F., Sato, T. and Shen, Y.-D.: Linear tabling strategies and optimization. Theory and Practice of Logic Programming, Vol.8, No.1, pp.81–109, 2008.
  • Tsuda, K and Kurihara, K: Graph mining with variational Dirichlet process mixture models, Proceedings of the 2008 SIAM International Conference on Data Mining (SDM 2008), pp.432–442, 2008.
    PDF
  • Kurihara, K, Murata, T and Sato, T: Identification of MCMC samples for clustering, Proceedings of the 3rd International Conference on Large-Scale Knowledge Resources (LKR 2008), LNAI 4938, Springer, pp.27–37, 2008.
    PDF (Springer)
2007
  • 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.
    PDF (JSAI J-STAGE), errata (PDF)
  • Minato, S., Satoh, K., and T. Sato: Compiling Bayesian networks by symbolic probability calculation based on Zero-suppressed BDDs. Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI-2007), pp.2550–2555, 2007.
    PDF (Draft), PDF (Online proceedings)
  • Sato, T.: Inside-outside probability computation for belief propagation. Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI-2007), pp.2605–2610, 2007.
    PDF (Draft), PDF (Online proceedings)
2006
  • Kurihara, K., Kameya, Y. and Sato, T.: A frequency-based stochastic blockmodel. Proceedings of IBIS 2006.
    PDF
  • Kurihara, K., Kameya, Y. and Sato, T.: Discovering concepts from word co-occurrences with a relational model. Proceedings of the International Workshop on Data-Mining and Statistical Science (DMSS-2006), pp.26–33, 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.
    PDF
  • Kurihara, K. and Sato, T.: Variational Bayesian grammar induction for natural language. Proceedings of the 8th International Colloquium on Grammatical Inference (ICGI-2006), pp.84–95, 2006.
    PDF (copyright Springer-Verlag)
  • Yamamoto, M., Mitomi, H., Fujiwara, F. and Sato, T.: Bayesian classification of task-oriented actions based on stochastic context-free grammar. Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition (FG2006), pp.317–322, 2006.
  • Sato, T. and Kameya, Y.: Learning through failure. Dagstuhl Seminar Proceedings on Probabilistic, Logical and Relational Learning - Towards a Synthesis, 2006.
2005
  • Sato,T. and Kameya,Y.: Negation elimination for finite PCFGs. Proceedings of the International Symposium on Logic-based Program Synthesis and Transformation 2004 (LOPSTR04), later selectively published as Logic-based Program Synthesis and Transformation, Springer LNCS 3573, S. Etalle (Ed.), pp.117–132, 2005.
    PDF
  • Sato, T.: A generic approach to EM learning for symbolic-statistical models. Proeedings of the 4th Learning Language in Logic Workshop (LLL05), 2005.
    PDF
  • Sato, T., Kameya, Y. and Zhou, N.-F.: Generative modeling with failure in PRISM. Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI2005), pp.847–852, 2005.
    PDF (Draft), PDF (Online proceedings)
2004
  • Sato, T. and Kameya, Y.: A dynamic programming approach to parameter learning of generative models with failure. Proceedings of ICML Workshop on Statistical Relational Learning and its Connection to the Other Fields (SRL2004), 2004.
    PS, PDF
  • Zhou, N.-F., Shen, Y.-D. and Sato, T.: Semi-naive evaluation in linear tabling. Proceedings of the 6th ACM-SIGPLAN International Conference on Principles and Practice of Declarative Programming (PPDP04), pp.90–97, 2004.
  • 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 (ECAI2004), pp.490–494, 2004.
    PDF
  • Kurihara, K. and Sato, T.: An application of the variational Bayesian approach to probabilistic context-free grammars. IJCNLP-04 Workshop Beyond shallow analyses, 2004.
    PDF
2003
  • Zhou, N.-F. and Sato, T.: Efficient fixpoint computation in linear tabling. Proc. of the 5th ACM-SIGPLAN International Conference on Principles and Practice of Declarative Programming (PPDP 03), pp.275–283, 2003.
    PDF
  • Sato, T. and Zhou, N.-F.: A new perspective of PRISM relational modeling. Proceedings of IJCAI-03 workshop on Learning Statistical Models from Relational Data (SRL2003), pp.133–139, 2003.
    PS, PS + gz, PDF
  • Zhou, N.-F., Sato, T., and Hashida, K.: Toward a high-performance system for symbolic and statistical modeling. Proceedings of IJCAI-03 workshop on Learning Statistical Models from Relational Data (SRL2003), pp.153–159, 2003.
    PDF
2002
  • 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.
    PS, PS + gz, PDF
  • Sato, T.: Computing Kikuchi approximations by Cluster BP. Proceedings of Machine Intelligence 19, 2002.
    PS, PS + gz, PDF
2001
  • 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.
    PDF (JAIR site), PDF (Draft)
  • Sato, T.: Parameterized logic programs where computing meets learning. Proceedings of the 5th International Symposium on Functional and Logi Programming (FLOPS-2001), LNCS Vol.2024, pp.40–60, 2001.
    PS, PS + gz, PDF
  • Ueda, N., Sato, T.: Simplified training algorithms for hierarchical hidden Markov models. Proceedings of the 4th International Conference on Discovery Science (DS2001), LNCS Vol.2226, pp.401–415, Springer, 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.
    Revised version: PS, PS + gz, PDF
2000
  • Ueda, N., Sato, T.: Finding original regulatory networks with weight matrices, Proceedings of the 1st International Conference on Systems Biology, 2000.
  • Sato, T.: Program extraction from quantified decision trees, Proc. of Machine Intelligence 17, Bury St Edmunds, pp.78–80, 2000.
    PS, PS + gz, PDF
  • Sato, T. and Kameya, Y.: A Viterbi-like algorithm and EM learning for statistical abduction. Proceedings of UAI2000 Workshop on Fusion of Domain Knowledge with Data for Decision Support, 2000.
    PS, PS + gz, PDF
  • Kameya, Y. and Sato, T.: Efficient EM learning with tabulation for parameterized logic programs. Proceedings of the 1st International Conference on Computational Logic (CL2000), LNAI Vol.1861, pp.269–294, 2000.
    PS, PS + gz, PDF
Before 2000
  • Kameya, Y., Ueda, N., and Sato, T.: A graphical method for parameter learning of symbolic-statistical models. In Proceedings of the 2nd International Conference on Discovery Science (DS99), LNAI Vol.1721, pp.264–276, 1999.
    PS, PS + gz, PDF
  • 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.
  • Kameya, Y. and Sato, T.: Abstracting human's decision process by PRISM. Proceedings of the 1st International Conference on Discovery Science (DS98), pp.389–390, 1998.
  • Sato, T.: Modeling scientific theories as PRISM programs. ECAI98 Workshop on Machine Discovery, pp.37–45, 1998.
    PS, PS + gz, PDF
  • Sato, T. and Kameya, Y.: PRISM: A symbolic-statistical modeling language. Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI97), pp.1330–1335, 1997.
    PS, PS + gz, PDF
  • Sato, T.: A statistical learning method for logic programs with distribution semantics. Proceedings of the 12th International Conference on Logic Programming (ICLP95), Tokyo, pp.715–729, 1995.
    extended version: PS, PS + gz, PDF

Technical Reports

  • 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.
  • Ishihata, M., Kameya, Y., Sato, T. and Minato, S.: Propositionalizing the EM algorithm by BDDs, Technical Report TR08-0004, Dept. of Computer Science, Tokyo Institute of Technology, June, 2008.
  • Zhou, Neng-Fa and Sato, T.: Toward a High-Performance System for Symbolic and Statistical Modeling, Technical Report (Computer Science) TR-200212, City University of New York, 2002.
    PDF
  • Sato, T., Kameya, Y., Abe, S., and Shirai, K.: Fast EM learning of a family of PCFGs, Technical Report TR01-0006, Dept. of Computer Science, Tokyo Institute of Technology, May, 2001.
    PS, PS + gz, PDF

Last Update: Apr. 21, 2014