- 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: Sept. 22, 2014
