 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
(AI2011),
LNAI 7106, Springer, pp.203212, 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
(ACML2011),
JMLR Workshop and Conference Proceedings, Vol.20, pp.8196, November, 2011.
PDF
(Online proceedings)

Kameya, Y.:
Time series discretization via MDLbased histogram density estimation.
Proceedings of the 23rd IEEE International Conference on Tools with Artificial Intelligence
(ICTAI2011), 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
(ICTAI2011), pp.873–875, 2011
(The full version is available as
arXiv:1108.5002 and as
Technical Report TR110001,
Dept. of Computer Science, Tokyo Institute of Technology, August, 2011).
PDF
(IEEE Xplore)

Ishihata, M., Kameya, Y. and Sato, T.:
Variational Bayes inference for logicbased probabilistic models on BDDs.
The 21st International Conference on Inductive Logic Programming
(ILP2011), to appear, 2011.

Sato, T., Ishihata, M. and Inoue, K.:
Constraintbased 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 logicbased probabilistic modeling.
Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI2011), pp.1472–1477, 2011.
PDF
(Online proceedings)

Kameya, Y. and Prayoonsri, C.:
Patternbased preservation of building blocks in genetic algorithms.
Proceedings of the 2011 IEEE Congress on Evolutionary Computation (CEC2011), 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 (LLLL2011),
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
(BIOINFORMATICS2011), 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 (AMBN2010), 2010.

Ishihata M., Kameya, Y., Sato, T. and Minato, S.:
An EM algorithm on BDDs with order encoding for logicbased
probabilistic models.
Proceedings of the 2nd Asian Conference on Machine Learning
(ACML2010),
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
(TAAI2010), pp.342–349, 2010.
PDF (IEEE Xplore),
Slides,
Dataset

Zhou, N.F., Kameya, Y. and Sato, T.:
Modedirected tabling for dynamic programming, machine learning, and
constraint solving.
Proceedings of the 22nd International Conference on Tools with
Artificial Intelligence
(ICTAI2010), 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
(IJCAI2009), pp.810–815, 2009.
PDF (Online proceedings)

Sato, T.:
Generative modeling by PRISM.
Proceedings of the 25th International Conference on Logic Programming
(ICLP2009), LNCS 5649, Springer, pp.24–35, 2009.
PDF (Springer),
PDF (Draft),
Slides

Sato, T.:
Logicbased probabilistic modeling.
Proceedings of the 16th Workshop on Logic, Language, Information and Computation
(WoLLIC2009), 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 Kmeans as a "maximizationexpectation" 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 (ILP2008), pp.44–49, 2008.
PDF (Draft)

Sato, T.:
A glimpse of symbolicstatistical 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 (GECCO2008),
pp.1203–1210, 2008.
PDF (Draft),
PDF (ACM DL)

Sato, T. and Kameya, Y.:
New advances in logicbased 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 LargeScale 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 cooccurrences with a relational model.
Transactions of the Japanese Society for Artificial Intelligence, Vol.22, No.2,
pp.218–226, 2007.
PDF
(JSAI JSTAGE), errata (PDF)

Minato, S., Satoh, K., and T. Sato:
Compiling Bayesian networks by symbolic probability calculation
based on Zerosuppressed BDDs.
Proceedings of the 20th International Joint Conference on
Artificial Intelligence
(IJCAI2007),
pp.2550–2555, 2007.
PDF (Draft),
PDF (Online proceedings)

Sato, T.:
Insideoutside probability computation for belief propagation.
Proceedings of the 20th International Joint Conference on
Artificial Intelligence
(IJCAI2007),
pp.2605–2610, 2007.
PDF (Draft),
PDF (Online proceedings)
 2006


Kurihara, K., Kameya, Y. and Sato, T.:
A frequencybased stochastic blockmodel.
Proceedings of IBIS 2006.
PDF

Kurihara, K., Kameya, Y. and Sato, T.:
Discovering concepts from word cooccurrences with a relational model.
Proceedings of the International Workshop on DataMining and Statistical Science
(DMSS2006),
pp.26–33, 2006.

Izumi, Y., Kameya, Y. and Sato, T.:
Parallel EM learning for symbolicstatistical models.
Proceedings of the International Workshop on DataMining and Statistical Science
(DMSS2006),
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
(ICGI2006),
pp.84–95, 2006.
PDF
(copyright SpringerVerlag)

Yamamoto, M., Mitomi, H., Fujiwara, F. and Sato, T.:
Bayesian classification of taskoriented actions based on stochastic
contextfree 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
Logicbased Program Synthesis and Transformation 2004
(LOPSTR04),
later selectively published as Logicbased Program Synthesis
and Transformation,
Springer LNCS 3573,
S. Etalle (Ed.), pp.117–132, 2005.
PDF

Sato, T.:
A generic approach to EM learning for symbolicstatistical 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.:
Seminaive evaluation in linear tabling.
Proceedings of the 6th ACMSIGPLAN 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 intergoal 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
contextfree grammars.
IJCNLP04 Workshop Beyond shallow analyses, 2004.
PDF
 2003


Zhou, N.F. and Sato, T.:
Efficient fixpoint computation in linear tabling.
Proc. of the 5th ACMSIGPLAN 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 IJCAI03 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 highperformance system for symbolic and statistical modeling.
Proceedings of IJCAI03 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 symbolicstatistical 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
(FLOPS2001),
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 separateandlearn approach to EM learning of PCFGs.
Proceedings of the 6th Natural Language Processing Pacific Rim
Symposium
(NLPRS2001),
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 Viterbilike 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 symbolicstatistical 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 contextfree 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 symbolicstatistical 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 TR110001,
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 TR080004,
Dept. of Computer Science, Tokyo Institute of Technology,
June, 2008.

Zhou, NengFa and Sato, T.:
Toward a HighPerformance System for Symbolic and Statistical
Modeling,
Technical Report (Computer Science)
TR200212, 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
TR010006,
Dept. of Computer Science,
Tokyo Institute of Technology, May, 2001.
PS,
PS + gz,
PDF
Last Update: Sept. 22, 2014