Data Mining Technology Group
is trying to realize a brighter world using data analytics.
Our purpose is to predict the future and optimize an entire society based on data accumulated in the real world.
We are developing data analytics technologies by using mathematical techniques such as machine learning, statistics and optimization, and are conducting commercialization of those technologies.
- 2018/05/15Our papers has been accepted to The International Conference on Machine Learning (ICML).
- 1)Akihiro Yabe, Daisuke Hatano, Hanna Sumita, Shinji Ito, Naonori Kakimura, Takuro Fukunaga, Ken-ichi Kawarabayashi. Causal Bandit with Propagating Inference.
- 2)Shinji Ito, Akihiro Yabe, Ryohei Fujimaki, Unbiased Objective Estimation in Predictive Optimization.
- 3)Takafumi Kajihara, Motonobu Kanagawa, Keisuke Yamazaki , and Kenji Fukumizu, Kernel Recursive ABC: Point Estimation with Intractable Likelihood.
- 2017/08/29Our paper on Customer Profile Estimation has been accepted to The IEEE International Conference on Data Mining (ICDM) as a regular paper.
Masafumi Oyamada and Shinji Nakadai, Relational Mixture of Experts: Explainable Demographics Prediction with Behavioral Data, IEEE International Conference on Data Mining (ICDM) 2017
- 2017/03/10Ryohei Fujimaki (Ph. D) led a presentation entiled "Predictive Analysis Automation" on the Gartner Data & Analytics Summit.
Click here to view the flash report about the presentation.
- 2017/01/12Ryohei Fujimaki (Ph. D), Research Fellow appeard at the panel discussion of “Artificial Intelligence and U.S.-Japan Alliance Engagement” symposium.
- 2016/06/24We become a sponsor of the Conference on Knowledge Discovery and Data Mining (KDD'2016).
- 2016/04/13We released the Presentation Summary and Full Transcript of Gartner Business Intelligence & Analytics Summit.
- 2016/03/30We released the Flash Report of Gartner Business Intelligence & Analytics Summit.
Further details and complete transcript of this presentation will be updated in April.
- 2016/03/03Ryohei Fujimaki (Ph. D), Research Fellow will lead a presentation entitled “Prescriptive Analysis: the Marriage of Your Business and Data Science” on March 16th from 2:00 p.m. to 2:30 p.m. (CST) at Texas C.
Gartner Business Intelligence & Analytics Summit
14 - 16 March 2016 | Grapevine, TX
Click here to view the latest information of our session.
- 2016/02/25NEC exhibited in Mobile World Congress 2016.
Click here to watch the video about the customer retention solution by our big data analysis technologies called HML (Heterogeneous Mixture Learning).
- Zhao Song, Yusuke Muraoka, Ryohei Fujimaki, Lawrence Carin, Scalable Model Selection for Belief Networks, Thirty-first Annual Conference on Neural Information Processing Systems (NIPS), 2017
- Shinji Ito, Daisuke Hatano, Hanna Sumita, Akihiro Yabe, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi, Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression with Limited Observation, Thirty-first Annual Conference on Neural Information Processing Systems (NIPS), 2017
- Masafumi Oyamada, Shinji Nakadai, Relational Mixture of Experts: Explainable Demographics Prediction with Behavioral Data, IEEE International Conference on Data Mining (ICDM), 2017
- Chunchen Liu, Feng Lu, Ryohei Fujimaki, Streaming Model Selection via Online Factorized Asymptotic Bayesian Inference, IEEE International Conference on Data Mining (ICDM), 2016
- Ito and Fujimaki, Large-scale Price Optimization via Network Flow, Annual Conference on Neural Information Processing Systems (NIPS), 2016.
- Haichuan Yang, Ryohei Fujimaki, Yukitaka Kusumura, Ji Liu, "Online Feature Selection: A Limited-Memory Substitution Algorithm and its Asynchronous Parallel Variation", Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016
- Masato Asahara, Ryohei Fujimaki, "Distributed Heterogeneous Mixture Learning On Spark", Spark Summit 2016.
- Masato Asahara, Ryohei Fujimaki, "Big Data Heterogeneous Mixture Learning on Spark", Hadoop Summit San Jose, 2016.
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