| Parameter learning of logic programs for symbolic-statistical modeling T Sato, Y Kameya Journal of Artificial Intelligence Research 15, 391-454, 2001 | 352 | 2001 |
| PRISM: a language for symbolic-statistical modeling T Sato, Y Kameya IJCAI 97, 1330-1339, 1997 | 291 | 1997 |
| New advances in logic-based probabilistic modeling by PRISM T Sato, Y Kameya Probabilistic Inductive Logic Programming: Theory and Applications, 118-155, 2008 | 69 | 2008 |
| Evaluating Abductive Hypotheses using an EM Algorithm on BDDs. K Inoue, T Sato, M Ishihata, Y Kameya, H Nabeshima IJCAI, 810-815, 2009 | 64 | 2009 |
| Efficient EM learning with tabulation for parameterized logic programs Y Kameya, T Sato Computational Logic—CL 2000: First International Conference London, UK …, 2000 | 56 | 2000 |
| Propositionalizing the EM algorithm by BDDs M Ishihata, Y Kameya, T Sato, S Minato Transactions of the Japanese Society for Artificial Intelligence 25 (3), 475-484, 2010 | 54 | 2010 |
| Generative Modeling with Failure in PRISM. T Sato, Y Kameya, NF Zhou IJCAI, 847-852, 2005 | 51 | 2005 |
| Computation of probabilistic relationship between concepts and their attributes using a statistical analysis of Japanese corpora Y Kameya, T Sato Proc. of Symposium on Large-scale Knowledge Resources: LKR2005, 65-68, 2005 | 43 | 2005 |
| Mode-directed tabling for dynamic programming, machine learning, and constraint solving NF Zhou, Y Kameya, T Sato 2010 22nd IEEE International Conference on Tools with Artificial …, 2010 | 36 | 2010 |
| CHR (PRISM)-based probabilistic logic learning J Sneyers, W Meert, J Vennekens, Y Kameya, T Sato Theory and Practice of Logic Programming 10 (4-6), 433-447, 2010 | 35 | 2010 |
| Variational Bayes via propositionalized probability computation in PRISM T Sato, Y Kameya, K Kurihara Annals of Mathematics and Artificial Intelligence 54 (1), 135-158, 2008 | 30 | 2008 |
| Statistical abduction with tabulation T Sato, Y Kameya Computational Logic: Logic Programming and Beyond: Essays in Honour of …, 2002 | 23 | 2002 |
| Contrastive relevance propagation for interpreting predictions by a single-shot object detector H Tsunakawa, Y Kameya, H Lee, Y Shinya, N Mitsumoto 2019 International Joint Conference on Neural Networks (IJCNN), 1-9, 2019 | 22 | 2019 |
| RP-growth: Top-k Mining of Relevant Patterns with Minimum Support Raising Y Kameya, T Sato Proceedings of the 2012 SIAM international conference on data mining, 816-827, 2012 | 22 | 2012 |
| A Viterbi-like algorithm and EM learning for statistical abductuion T Sato the UAI-2000 workshop on Fusion of Domain Knowledge with Data for Decision …, 2000 | 22 | 2000 |
| Accelerating genetic programming by frequent subtree mining Y Kameya, J Kumagai, Y Kurata Proceedings of the 10th annual conference on Genetic and evolutionary …, 2008 | 18 | 2008 |
| A frequency-based stochastic blockmodel K Kurihara, Y Kameya, T Sato Bernoulli (R (e1, e2) 1 (1), N2, 2006 | 16 | 2006 |
| A Separate-and-Learn Approach to EM Learning of PCFGs. T Sato, S Abe, Y Kameya, K Shirai NLPRS, 255-262, 2001 | 15 | 2001 |
| Pattern-based preservation of building blocks in genetic algorithms Y Kameya, C Prayoonsri 2011 IEEE Congress of Evolutionary Computation (CEC), 2578-2585, 2011 | 14 | 2011 |
| Kinetic models and qualitative abstraction for relational learning in systems biology G Synnaeve, K Inoue, A Doncescu, H Nabeshima, Y Kameya, M Ishihata, ... BIOSTEC Bioinformatics 2011, 2011 | 13 | 2011 |
