Jun Sakuma, University of Tsukuba

Selected Papers

Hiromi Arai, Jun Sakuma: Privacy Preserving Semi-supervised Learning for Labeled Graphs. ECML/PKDD, 2011, pp. 124-139
Toshihiro Kamishima, Shotaro Akaho, Jun Sakuma: Fairness-aware Learning through Regularization Approach. ICDM Workshops 2011, pp. 643-650
Jun Sakuma, Hiromi Arai: Online Prediction with Privacy. ICML 2010, pp. 935-942
Jun Sakuma, Shigenobu Kobayashi: Large-scale k-means clustering with user-centric privacy-preservation. Knowl. Inf. Syst. 25(2), pp. 253-279 (2010)
Jun Sakuma and Rebecca Wright, Privacy-preserving Evaluation of Generalization Error and Its Application to Model/Attribute Selection, Proceedings of the 1st Asian Conference on Machine Learning (ACML), 2009. (to appear, accepted as full paper, acceptance rate 25%=29/113)
Jun Sakuma and Shigenobu Kobayashi, Link Analysis for Privately Weighted Graphs, Proceedings of the 32nd annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR), (2009). (to appear, accepted as full paper, acceptance rate 16%)
Jun Sakuma, Shigenobu Kobayashi and Rebecca N. Wright, Privacy-preserving Reinforcement Learning, Proceedings of The 25th International Conference on Machine Learning (ICML 2008), pp. 864-871, (2008). (accepted as full paper, acceptance rate 27%)
Jun Sakuma and Shigenobu Kobayashi, Large-scale k-means Clustering with User-Centric Privacy Preservation, Proceedings of The Pacific-Asia Conference on Knowledge Discovery and Data Mining 2008 (PAKDD) pp. 320-332, (2008). (accepted as long paper, acceptance rate 12%, invited to the Special Section of An International Journal of Knowledge and Information Systems (KAIS))
Jun Sakuma and Shigenobu Kobayashi, Privacy Preserving Combinatorial Optimization by Means of Genetic algorithm, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) 2007, pp. 1372-1379, London, UK, 2007.
K. Harada, J. Sakuma, S. Kobayashi, and I. Ono. Uniform Sampling of Local Pareto-Optimal Solution Curves by Pareto Path Following and its Applications in Multi-objective GA. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) 2007, pp. 813-820, London, UK, 2007.
K. Harada, J. Sakuma, I. Ono, and S. Kobayashi. Constraint-Handling Method for Multi-Objective Function Optimization: Pareto Descent Repair Operator. Proceedings of Evolutionary Multi-Criterion Optimization (EMO) 2007, pp. 156-170, 2007.
K. Harada, J. Sakuma, S. Kobayashi, K. Ikeda, and I. Ono. Local Search for Multiobjective Function Optimization: Pareto Descent Method. Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2006, pp. 667-674, New York, NY, USA, 2006. ACM Press.
K. Harada, K. Ikeda, S. Kobayashi, J. Sakuma, and I. Ono. Hybridization of Genetic Algorithm and Local Search in Multiobjective Function Optimization: Recommendation of GA then LS. Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2006, pp. 675--682, New York, NY, USA, 2006. ACM Press.
Shin Ando, Jun Sakuma and Shigenobu Kobyashi, Adaptive Isolation Model using Data Clustering for Multimodal Function Optimization, Shin Ando, Jun Sakuma and Kobayashi Shigenobu, Gecco 2005, pp. 1417-1424 (2005)
Jun Sakuma and Shigenobu Kobyashi, Real-coded Crossovers as a role of Kernel Density Estimation, Jun Sakuma and Kobayashi Shigenobu, Gecco 2005, pp. 703-710 (2005)
Jun Sakuma and Shigenobu Kobyashi, Real-coded GA for High-dimensional k-tablet structures, Jun Sakuma and Kobayashi Shigenobu, Gecco 2005, pp. 1347-1354 (2005)
Michael E. Houle and Jun Sakuma, Fast Approximate Similarity Search in Extremely High-Dimensional Data Sets, Michael E. Houle, Jun Sakuma, International Conference on Data Engineering 2005, pp. 619-630 (2005)

Jun Sakuma, University of Tsukuba

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