Kohei Hatano | Last modified date：2019.06.19 |

Associate Professor /
Division for Theoretical Natural Science /
Faculty of Arts and Science

**Papers**

1. | Shinichi Konomi, Kohei Hatano, Miyuki Inaba, Misato Terai, Tsuyoshi Okamoto, Fumiya Okubo, Atsushi Shimada, Jingyun Wang, Masanori Yamada, Yuki Yamada, Towards supporting multigenerational co-creation and social activities Extending learning analytics platforms and beyond, 6th International Conference on Distributed, Ambient and Pervasive Interactions, DAPI 2018 Held as Part of HCI International 2018
Distributed, Ambient and Pervasive Interactions
Technologies and Contexts - 6th International Conference, DAPI 2018, Held as Part of HCI International 2018, Proceedings, 10.1007/978-3-319-91131-1_6, 82-91, 2018.01, As smart technologies pervade our everyday environments, they change what people should learn to live meaningfully as valuable participants of our society. For instance, ubiquitous availability of smart devices and communication networks may have reduced the burden for people to remember factual information. At the same time, they may have increased the benefits to master the uses of new digital technologies. In the midst of such a social and technological shift, we could design novel integrated platforms that support people at all ages to learn, work, collaborate, and co-create easily. In this paper, we discuss our ideas and first steps towards building an extended learning analytics platform that elderly people and unskilled adults can use. By understanding the characteristics and needs of elderly learners and addressing critical user interface issues, we can build pervasive and inclusive learning analytics platforms that trigger contextual reminders to support people at all ages to live and learn actively regardless of age-related differences of cognitive capabilities. We discuss that resolving critical usability problems for elderly people could open up a plethora of opportunities for them to search and exploit vast amount of information to achieve various goals.. |

2. | Kohei Hatano, Can machine learning techniques provide better learning support for elderly people?, 6th International Conference on Distributed, Ambient and Pervasive Interactions, DAPI 2018 Held as Part of HCI International 2018
Distributed, Ambient and Pervasive Interactions
Technologies and Contexts - 6th International Conference, DAPI 2018, Held as Part of HCI International 2018, Proceedings, 10.1007/978-3-319-91131-1_14, 178-187, 2018.01, Computer-based support for learning of elderly people is now considered as an important issue in the super-aged society. Extra cares are needed for elderly people’s learning compared to younger people, since they might have difficulty in using computers, reduced cognitive ability and other physical problems which make them less motivated. Key components of a better learning support system are sensing the contexts surrounding elderly people and providing appropriate feedbacks to them. In this paper, we review some existing techniques of the contextual bandit framework in the machine learning literature, which could be potentially useful for online decision making scenarios given contexts. We also discuss issues and challenges to apply the framework.. |

3. | Kosuke Matsumoto, Kohei Hatano, Eiji Takimoto, Decision diagrams for solving a job scheduling problem under precedence constraints, 17th Symposium on Experimental Algorithms, SEA 2018
, 10.4230/LIPIcs.SEA.2018.5, 2018.06, We consider a job scheduling problem under precedence constraints, a classical problem for a single processor and multiple jobs to be done. The goal is, given processing time of n fixed jobs and precedence constraints over jobs, to find a permutation of n jobs that minimizes the total flow time, i.e., the sum of total wait time and processing times of all jobs, while satisfying the precedence constraints. The problem is an integer program and is NP-hard in general. We propose a decision diagram π-MDD, for solving the scheduling problem exactly. Our diagram is suitable for solving linear optimization over permutations with precedence constraints. We show the e ectiveness of our approach on the experiments on large scale artificial scheduling problems.. |

4. | Takahiro Fujita, Kohei Hatano, Shuji Kijima, Eiji Takimoto, Online combinatorial optimization with multiple projections and its application to scheduling problem, IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 10.1587/transfun.E101.A.1334, E101A, 9, 1334-1343, 2018.09, We consider combinatorial online prediction problems and propose a new construction method of efficient algorithms for the problems. One of the previous approaches to the problem is to apply online prediction method, in which two external procedures the projection and the metarounding are assumed to be implemented. In this work, we generalize the projection to multiple projections. As an application of our framework, we show an algorithm for an online job scheduling problem with a single machine with precedence constraints.. |

5. | Kohei Hatano, Combinatorial Online Prediction, 15th International Symposium on Information Theory and Its Applications, ISITA 2018
Proceedings of 2018 International Symposium on Information Theory and Its Applications, ISITA 2018, 10.23919/ISITA.2018.8664224, 40-44, 2019.03, We present a short survey on recent results on combinatorial online prediction in the adversarial setting.. |

6. | Takahiro Fujita, Kohei Hatano, Eiji Takimoto, Boosting over non-deterministic ZDDs, Theoretical Computer Science, https://doi.org/10.1016/j.tcs.2018.11.027, 2018.12, [URL], 本論文では，ZDDと呼ばれる圧縮データ構造を用いてデータを圧縮した上で，圧縮したデータ上でブースティングという機械学習手法が効率よく計算できる事を明らかにした．ビッグデータの解析において，省スペースで計算が出来ることは大きな利点である．本研究の興味深い点は，組合せ論的オンライン予測という（一見）全く異なる機械学習分野の知見が圧縮情報処理に活かされたことである．. |

7. | Takahiro Fujita, Kohei Hatano, and Eiji Takimoto, “, Online Combinatorial Optimization with Multiple Projections and Its Application to Scheduling Problem Volume and Number: Vol.,pp.-,Sep. 2018., IEICE Transactions on Information and Systems, E101-A, 9, 2018.09. |

8. | Ken-ichiro Moridomi, Kohei Hatano, and Eiji Takimoto, “, Tighter generalization bounds for matrix completion via factorization into constrained matrices, IEICE Transactions on Information and Systems, E101-D, 8 , 2018.08. |

9. | Ken-ichiro Moridomi, Kohei Hatano, and Eiji Takimoto, “, Online linear optimization with the log-determinant regularizer, IEICE Transactions on Information and Systems, E101-D, 6, 2018.06. |

10. | Takahiro Fujita, Kohei Hatano, Eiji Takimoto, Boosting over non-deterministic ZDDs, Proceedings of the 12th International Conference and Workshop on Algorithms and Computation( WALCOM 2018), 10.1007/978-3-319-75172-6_17, 195-206, 2018.01, 本論文では，ZDDと呼ばれる圧縮データ構造を用いてデータを圧縮した上で，圧縮したデータ上でブースティングという機械学習手法が効率よく計算できる事を明らかにした．ビッグデータの解析において，省スペースで計算が出来ることは大きな利点である．本研究の興味深い点は，組合せ論的オンライン予測という（一見）全く異なる機械学習分野の知見が圧縮情報処理に活かされたことである．. |

11. | Daiki Suehiro, Kohei hatano, Eiji Takimoto, Efficient reformulation of 1-norm ranking SVM, IEICE Transactions on Information and Systems, 10.1587/transinf.2017EDP7233, E101D, 3, 719-729, 2018.03. |

12. | Emi Ishita, Tetsuya Nakatoh, Kohei Hatano, Michiaki TAKAYAMA, An Attempt to Promote Open Data for Digital Humanities in Japanese University Libraries, Proceedings of the 18th International Conference on Asia-Pacific Digital Libraries (ICADL 2016), 10.1007/978-3-319-49304-6_32, LNCS 10075, 269-274, 2016.12. |

13. | Takumi Nakazono, Ken-ichiro Moridomi, Kohei Hatano, Eiji Takimoto, A Combinatorial Metrical Task System Problem under the Uniform Metric, Proceedings of 27th International Conference on Algorithmic Learning Theory(ALT 2016), 10.1007/978-3-319-46379-7_19, LNCS 9926, 276-287, 2016.10. |

14. | Atsushi Shibagaki, Masayuki Karasuyama, Kohei Hatano, Ichiro Takeuchi, Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling, Proceedings of the 33rd International Conference on Machine Learning (ICML 2016), JMLR W&CP 48, 1577-1586, 2016.07. |

15. | Nir Ailon, Kohei Hatano, Eiji Takimoto, Bandit Online Optimization Over the Permutahedron, Theoretical Computer Science, 10.1016/j.tcs.2016.07.033, 650, 18, 92-108, 2016.10. |

16. | Yao Ma, Tingting Zhao, Kohei Hatano, Masashi Sugiyama, An Online Policy Gradient Algorithm for Continuous State and Action Markov Decision Processes, Neural Computation, 10.1162/NECO_a_00808, 28, 3, 563-593, 2016.02. |

17. | Issei Matsumoto, Kohei Hatano, Eiji Takimoto, Online Density Estimation of Bradley-Terry Models, Proceedings of the 28th Conference on Learning Theory (COLT 2015), JMLR W&CP 40, 1343-1359, 2015.06. |

18. | Takahiro Fujita, Kohei Hatano, Shuji Kijima, Eiji Takimoto, Online Linear Optimization for Job Scheduling under Precedence Concstraints, Proceedings of 26th International Conference on Algorithmic Learning Theory(ALT 2015), 10.1007/978-3-319-24486-0_22, LNCS 6331, 345-359, 2015.10. |

19. | Ken-ichiro Moridomi, Kohei Hatano, Eiji Takimoto, Koji Tsuda, Online matrix prediction for sparse loss matrices , Proceedings of the 6th Asian Conference on Machine Learning(ACML 2014)
, JMLR W&CP 39, 250–265, 2015.02. |

20. | Nir Ailon, Kohei Hatano, Eiji Takimoto, Bandit Online Optimization Over the Permutahedron, Proceedings of the 25th International Conference on Algorithmic Learning Theory (ALT 2014),, LNCS 8776, 215–229, 2014.10. |

21. | Yao Ma, Tingting Zhao, Kohei Hatano, Masashi Sugiyama, An Online Policy Gradient Algorithm for Continuous State and Action Markov Decision Processes, Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2014), 10.1007/978-3-662-44851-9_23, LNCS 8725, 354–369, 2014.10. |

22. | Kazuki Teraoka, Kohei Hatano, Eiji Takimoto, Efficient Sampling Method for Monte Carlo Tree Search, IEICE TRANSACTIONS on Information and System, E97-D, 3, 392-298, 2014.03. |

23. | Takahiro Fujita, Kohei Hatano, Eiji Takimoto, Combinatorial Online Prediction via Metarounding, Proceedings of the 24th International Conference on Algorithmic Learning Theory (ALT 2013), 68-82, 2013.10. |

24. | Shota Yasutake, Kohei Hatano, Eiji Takimoto, Masayuki Takeda, Online Rank Aggregation , Proceedings of the 4th Asian Conference on Machine Learning(ACML 2012)
, 539-553, 2012.11. |

25. | Daiki Suehiro, Kohei Hatano, Shuji Kijima, Eiji Takimoto, Kiyohito Nagano, Online Prediction under Submodular Constraints, Proceedings of the 23rd International Conference on Algorithmic Learning Theory (ALT 2012)
, 260-274, 2012.10. |

26. | Yoko Anan, Kohei Hatano, Hideo Bannai, Masayuki Takeda, Polyphonic Music Classification on Symbolic Data Using Dissimilarity Functions, Proceedings of the 13th International Society for Music Information Retrieval Conference (ISMIR 2012), 229-234, 2012.10. |

27. | Shota Yasutake, Kohei Hatano, Shuji Kijima, Eiji Takimoto, Masayuki Takeda, , Online Linear Optimization over Permutations , Proceedings of the 22nd International Symposium on Algorithms and Computation (ISAAC 2011)
, 534-543, 2011.11. |

28. | Shin-ichi Yoshida, Kohei Hatano, Eiji Takimoto, Masayuki Takeda, Adaptive Online Prediction Using Weighted Windows, IEICE Transactions on Information and Systems , E94-D, 10, 1917-1923, 2011.10. |

29. | Yoko Anan, Kohei Hatano, Hideo Bannai, Masayuki Takeda, Music Genre Classification using Similarity Functions, Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR 2011), 693-698, 2011.10. |

30. | Daiki Suehiro, Kohei Hatano, Eiji Takimoto, Approximate Reduction from AUC Maximization to 1-norm Soft Margin Optimization, Proceedings of the 22nd International Conference on Algorithmic Learning Theory (ALT 2011)
, 324-337, 2011.10. |

31. | Michinari Momma, Kohei Hatano, and Hiroki Nakayama, Ellipsoidal Support Vector Machines , Proceedings of the 2nd Asian Conference on Machine Learning (ACML 2010), 31-46, 2010.11. |

32. | Kazuaki Kashihara, Kohei Hatano, Hideo Bannnai, and Masayuki Takeda, Sparse Substring Pattern Set Discovery using Linear Programming Boosting, Proceedings of the 13th International Conference on Discovery Science (DS 2010), 132-143, 2010.10. |

33. | Kohei Hatano and Eiji Takimoto, Linear Programming Boosting by Column and Row Generation, Proceedings of the Twelfth International Conference on Discovery Science (DS'09) , 2009.10. |

34. | Liwei Wang, Masashi Sugiyama, Cheng Yang, Kohei Hatano, and Jufu Feng, Theory and Algorithm for Learning with Dissimilarity Functions, Neural Computation, Vol. 21, No.5, 1459-1484, 2009.05. |

35. | Kazuyuki Narisawa, Hideo Bannnai, Kohei Hatano, Shunsuke Inenaga, and Masayuki Takeda, String Kernels Based on Variable-Length-Don’t-Care Patterns, Proceedings of the 11th International Conference on Discovery Science, 308-318, 2008.10. |

36. | Jun-ichi Moribe, Kohei Hatano, Eiji Takimoto, and Masayuki Takeda, Smooth Boosting for Margin-Based Ranking, Proceedings of 19th International Conference on Algorithmic Learning Theory, 227-239, 2008.10. |

37. | Hayato Kobayashi, Kohei Hatano, Akira Ishino, and Ayumi Shinohara, Autonomous Leaning of Ball Passing by Four-Legged Robots and Trial Reduction by Thinning-Out and Surrogate Functions, Intellegent Autonomous Systems 10, 145—154, 2008.07. |

38. | Kosuke Ishibashi, Kohei Hatano, and Masayuki Takeda, Online Learning of Approximate Maximum p-Norm Margin Classifiers with Biases, Proceedings of the 21st Annual Conference on Learning Theory, 69—80, 2008.07. |

39. | Kazuyuki Narisawa, Hideo Bannnai, Kohei Hatano, and Masayuki Takeda, Unsupervised Spam Detection based on String Alienness Measures, Proceedings of the 10th Inthernational Conference on Discovery Science, 2007.10. |

40. | Hayato Kobayashi, Kohei Hatano, Akira Ishino, and Ayumi Shinohara, Reducing Trials by Thinning-our in Skill Discovery, Proceedings of the 10th Inthernational Conference on Discovery Science, 2007.10. |

41. | Kosuke Ishibashi, Kohei Hatano, and Masayuki Takeda, Online Learning of Approximate Maximum Margin Classifiers with Biases, Proceedings of the 2nd International Workshop on Data Mining and Statistical Science, 2007.10. |

42. | Kohei Hatano, Smooth Boosting Using an Information-based Criterion, The 17 th international conference on algorithmic learning theory, 10.1007/11894841_25, 304-318, LNAI 4264., 2006.10. |

43. | Kohei Hatano and Osamu Watanabe, Learning r-of-k Functions by Boosting, Fifteenth International Conference on Algorithmic Learning Theory(ALT2004), 2004.10. |

44. | Hideo Bannai, Kohei Hatano, Shunsuke Inenaga, and Masayuki Takeda, Practical Algorithms for Pattern Based Linear Regression, the 8th international conference on Discovery Science, 3735, 44-56, 44--56, 2005.01. |

45. | Kohei Hatano, A Simple Boosting Algorithm Using Multi-Way Branching Decision Trees, Theory of Computing Systems, 2004.01. |

46. | Kohei Hatano and Manfred K. Warmuth, Boosting versus Covering, Seventeenth Annual Conference on Neural Information Processing Systems(NIPS2003), 2003.12. |

47. | Kohei Hatano, Simpler Analysis of The Multi-Way Branching Decision Tree Boosting Algorithm, welfth International Conference on Algorithmic Learning Theory(ALT2001, 2001.12. |

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