Our Laboratory‎ > ‎Our Staff‎ > ‎

Yoshitaka Yamamoto - Assistant Professor



Place of birth: Okayama, Japan 

Education
■ 1999 - 2003: Faculty of Engineering, Kobe University Awarded the  degree of BSc in Engineering.  

2003 - 2005:   Electrical and Electronic Engineering, Awarded the
degree of MSc in Engineering for a Graduate School of Engineering, Kobe University, by thesis entitled ``On a generalization procedure in CF-induction and its implementation’’. Work supervised by Professor Katsumi Inoue.

2006 - 2010: Ph. D. Program in Informatics, The Graduate University for Advanced Studies (SOKENDAI). Work supervised by Professor Katsumi Inoue.


Dissertation: Research on Logic and Computation in Hypothesis Finding


Research and professional experience:

 2005 - 2006: Department of Quality Management, Keyence Corporation

 2006 - 2009: Research Assistant at National Institute of Informatics

2009 - present: Assistant Professor at Department of Computer Science, 
                            University of Yamanashi (in Pr. Koji Iwanuma's Lab)

 2014 - present: JST PRESTO Researcher


Academic society:

-- The Japanese Society of Artificial Intelligence 

-- Information Processing Society of Japan

-- The Institute of Electronics, Information and Communication Engineers (IEICE)

-- EDBT2015 Program Committee Member 

-- EDBT2016 Program Committee Member 


Current Funding:

2013 - 2015: JSPS Grant-in-Aid for Young Scientists (B), ``Hypothesis-Finding based on Inverse Subsumption and its Applications to Systems Biology'' (Representative)

■ 2014 - 2015: Strategic Collaborative Research Project with National Institute of Informatics (NII, Japan), ``Analyzing GPCRs with Inference in Molecular Networks'' (Representative)

■ 2013 - 2015: JSPS Grant-in-Aid for Scientific Research (C),  ``Efficient Mining Negative Association Rules'' (Koji Iwanuma, Yoshitaka Yamamoto)

■ 2014 - 2017: JST PRESTO Research, ``Resource-oriented Approach for Extracting Deep Knowledge from Big Data Streams'' (In Advanced Core Techniques for Big Data Integration, supervised by Pr. Masaru Kitsuregawa, Director General, National Institute of Informatics)


Awards

Scholarship (Japan Student Services Organization)  to PhD study

Best Student Paper Award in the 20th International Conference on Inductive Logic Programming (ILP 2010)  


Referred Publications

(since 2007)

[23] Koji Iwanuma, Yoshitaka Yamamoto and Shoshi Fukuda. An On-Line Approximation Algorithm for Mining Frequent Closed Itemsets Based on Incremental Intersection. Proceedings of 19th Extended Database Technology (EDBT2015), pages 704-705, 2015.

[22] Yoshitaka Yamamoto and Koji Iwanuma. Online Pattern Mining for High-Dimensional Data Streams. Proceedings of IEEE BigData2015, pages 2615-2617, 2015.  

[21] 
Adrien Rougny, Yoshitaka Yamamoto, Hidetomo Nabeshima, Gauvain Bourgne, Anne Poupon, Katsumi Inoue, and Christine Froidevaux. Completing Signaling Networks by Abductive Reasoning with Perturbation Experiments. Proceedings of the 25st International Conference on Inductive Logic Programming (ILP2015), to appear, 2015.

[20] Yoshitaka Yamamoto, Adrien Rougny, Hidetomo Nabeshima, Katsumi Inoue, Hisao Moriya, Christine Froidevaux and Koji Iwanuma. Completing SBGN-AF Networks by Logic-Based Hypothesis Finding. Proceedings of the 1st International Conference on Formal Methods in Macro-Biology (FMMB2014), Lecture Notes in Bioinformatics, volume 8738, pages 165-179, 2014.

[19] Yoshitaka Yamamoto, Koji Iwanuma and Shoshi Fukuda. Resource-oriented Approximation for Frequent Itemset Mining from Bursty Data Streams. Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data (SIGMOD'14), pages 205-216, 2014.

[18] Adrien Rougny, Christine Froidevaux, Yoshitaka Yamamoto, Katsumi Inoue.
Analyzing SBGN-AF Networks using Normal Logic Programs. In handbook ``Logical Modeling of Biological Systems'', Katsumi Inoue, Luis Farinas (eds.) IStE-Ltd, ISBN: 978-1-84821-680-8, 2014.

[17] Adrien Rougny, Chrisine Froidevaux, Yoshitaka Yamamoto and Katsumi Inoue. Translating the SBGN-AF Language into Logics to Analyze Signalling NetworksPost-Proceedings of the 1st International Workshop on Learning and Nonmonotonic Reasoning (LNMR2013), CoRR, volume 975, pages 43-54, 2013.

[16] Yoshitaka Yamamoto, Koji Iwanuma and Hidetomo NabeshimaPractically Fast Non-monotone Dualization based on Monotone Dualization. Post-Proceedings of the 1st International Workshop on Learning and Nonmonotonic Reasoning (LNMR2013), CoRR, volume 975, pages 55-66, 2013. 

[15] Yoshitaka Yamamoto, Koji Iwanuma and Katsumi Inoue. Non-monotine dualization via Monotone DualizationProceedings of the 22nd International Conference on Inductive Logic Programming (ILP2012), CEUR, volume 975, pages 74-79, 2012. 

[14] Yoshitaka Yamamoto, Katsumi Inoue and Koji Iwanuma. Heuristic Inverse Subsumption in Full-clausal Theories. Proceedings of the 22nd International Conference on Inductive Logic Programming (ILP2012), 2012. An extended version is published in: Inductive Logic Programming: Revised Selected Papers from the 22nd International Conference (ILP '12), Lecture Notes in Artificial Intelligence, volume 7842, pages 241-256, Springer, 2013. Note that the final publication is available at www.springerlink.com.

[13] Yoshitaka Yamamoto, Katsumi Inoue and Koji Iwanuma. Comparison of Upward and Downward Generalizations in CF-induction. Proceedings of the 21st International Conference on Inductive Logic Programming (ILP 2011), 2011. An extended version is published in: Inductive Logic Programming: Revised Selected Papers from the 21st International Conference (ILP '11), Lecture Notes in Artificial Intelligence, volume 7207, pages 373-388 Springer, 2012. Note that the final publication is available at www.springerlink.com.

[12] Yoshitaka Yamamoto, Katsumi Inoue and Koji Iwanuma. Inverse Subsumption for Complete Explanatory Induction. Journal of Machine Learning, DOI: 10.1007/s10994-011-5250-y, 2011. Note that the final publication is available at www.springerlink.com.

[11] Yoshitaka Yamamoto, Katsumi Inoue and Koji Iwanuma. From Inverse Entailment to Inverse Subsumption. Proceedings of the 20th International Conference on Inductive Logic Programming (ILP 2010), 2010.

[10] Yoshitaka Yamamoto, Katsumi Inoue and Andrei Doncescu. Integrating Abduction and Induction in Biological Inference using CF-Induction. Huma Lodhi and Stephen Muggleton (eds.), Elements of Computational Systems Biology, Wiley Book Series on Bioinformatics, pages 213-234, John Wiley and Sons, Inc., 2009.

[9] Yoshitaka Yamamoto, Katsumi Inoue and Koji Iwanuma. Hypothesis enumeration by CF-induction. Proceedings of the Sixth Workshop on Learning with Logics and Logics for Learning (LLLL2009), pages 80-87. The Japanese Society for Artificial Intelligence, 2009.

[8] Yoshitaka Yamamoto, Katsumi Inoue and Andrei Doncescu. Abductive reasoning in cancer therapy. Proceedings of the 23rd International Conference on Advanced Information Networking and Applications (AINA 2009), pages 948-953. IEEE Computer Society, 2009.

[7] Yoshitaka Yamamoto, Oliver Ray and Katsumi Inoue. Towards a logical reconstruction of CF-induction. In New Frontiers in Artificial Intelligence: JSAI 2007 Conference and Workshop Revised Selected PapersLecture Notes in Artificial Intelligence, volume 4914, pages 330-343, Springer, 2008.

[6] Yoshitaka Yamamoto and Katsumi Inoue. An efficient hypothesis-finding system implemented with deduction and dualization. Proceedings of the 22nd Workshop on Logic Programming (WLP 2008), pages 92-103. University Halle –Wittenberg Institute of Computer Science Technical Report, 2008.

[5] Yoshitaka Yamamoto, Katsumi Inoue and Andrei Doncescu. Inferring inhibition and pathway rules using CF-induction. Poster presentation at: the 6th Asia Pacific Bioinformatics Conference (APBC 2008), Japan, 2008.

[4] Yoshitaka Yamamoto, Katsumi Inoue and Andrei Doncescu. Estimation of possible reaction states in metabolic pathways using inductive logic programming. Proceedings of the 22nd International Conference on Advanced Information Networking and Applications (AINA 2008), pages 808-813. IEEE Computer Society, 2008.

[3] Andrei Doncescu, Katsumi Inoue and Yoshitaka Yamamoto. Knowledge- based discovery in systems biology using CF-induction. New Trends in Applied Artificial Intelligence: Proceedings of the 20th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2007), Lecture Notes in Artificial Intelligence, volume 4570, pages 395-404, Springer, 2007.

[2] Yoshitaka Yamamoto, Oliver Ray and Katsumi Inoue. Towards a logical reconstruction of CF-Induction. Proceedings of the 5th International Workshop on Learning with Logics and Logics for Learning (LLLL 2007), pages 18-24, The Japanese Society for Artificial Intelligence, 2007.

[1] Andrei Doncescu, Yoshitaka Yamamoto and Katsumi Inoue. Biological systems analysis using Inductive Logic Programming. Proceedings of the 21st International Conference on Advanced Information Networking and Applications (AINA 2007), pages 690-695, IEEE Computer Society, 2007.


Oral presentations (since 2007)

[9] Yoshitaka Yamamoto, Resource-oriented Online Approach for Itemset-Mining and Hypothesis-Finding. Presentation at The ILP NII-Satellite Meeting, 2015

[8] Yoshitaka Yamamoto, A Case Study on Network Completion in Yeast Glucose Repression. Presentation at The LRI-NII-Yamanshi Workshop on Analyzing GPCRs with Inference in Molecular Networks, 2014 

[7] Yoshitaka Yamamoto, Completing SBGN based networks using meta-level abduction and SOLAR.  Presentation at The 5th JFLI-NII-LRI Workshop on Formal Approaches for Modeling and Analyzing Biological Networks, 2013

[6] Yoshitaka Yamamoto, Koji Iwanuma and Katsumi Inoue. On Subclasses of Non-monotone Dualization Equivalent to Monotone Dualization. Presentation at the CRIL-NII Collaborative Meeting on Reasoning about Dynamic Constraint Networks, 2012

[5] Yoshitaka Yamamoto, Haruka Sakamoto and Koji Iwanuma. Logical Verification of Biological Networks with Gene Expression Data. Presentation at the Fourth JFLI-LRI-NII Workshop on Consequence Finding and Satisfiability Testing in Distributed Environments and Systems Biology, 2012

[4] Yoshitaka Yamamoto, Katusmi Inoue and Koji Iwanuma. An incremental way for finding characteristic hypotheses in CF-induction. Presentation at the Third Franco-Japanese Symposium on Knowledge Discovery in Systems Biology (FJ’09), 2009.

[3] Yoshitaka Yamamoto and Katsumi Inoue. CF-induction for hypothesis enumeration. Presentation at the Second Franco-Japanese Symposium on Knowledge Discovery in Systems Biology (FJ'08), 2008.

[2] Yoshitaka Yamamoto and Katsumi Inoue. An Efficient Hypothesis-finding System Implemented with Deduction and Dualization. Presentation at One day Workshop for Answer Set Programming and Abduction, 2008.

[1] Yoshitaka Yamamoto, Katsumi Inoue and Andrei Doncescu. Integration of abduction and induction in biological networks using CF-induction. Presentation at the First Franco-Japanese Symposium on Knowledge Discovery in Systems Biology (FJ'07), 2007.


Other presentations (since 2004) in Japanese

[26] 吉田 一生,山本 泰生,岩沼 宏治.行動データマイニングのためのオンライン離散化手法の提案.人工知能学会全国大会 (第29回),2016.

[25] 山本 泰生,岩沼 宏治.オンライン頻出パターンマイニングの並列分散化.人工知能学会全国大会 (第29回),2016.

[24] 山本 泰生,山内 夏美,岩沼 宏治.漸近交差法に基づくオンライン頻出系列パターンマイニング.人工知能学会第100回人工知能基本問題研究会,人工知能学会研究会資料 SIG-FPAI-B503, 2016.

[23] 黒岩 健歩,岩沼 宏治,山本 泰生.負相関ルールを抽出する準オンラインアルゴリズム.人工知能学会第100回人工知能基本問題研究会,人工知能学会研究会資料 SIG-FPAI-B503, 2016.

[22] 平沼 悠人,山本 泰生,守屋 央朗,宋 剛秀,岩沼 宏治.分子ネットワーク上の状態推定とその可視化による知識発見支援.情報処理学会第45回バイオ情報学研究会 (SIG BIO), 2016.

[21] 山本 泰生,岩沼 宏治. リソース指向近似計算に基づくオンライン頻出圧縮アイテム集合マイニング. 第77回情報処理学会全国大会, 2015

[20] 福田 翔士,岩沼 宏治,山本 泰生. トランザクションストリーム上のオンライン型頻出飽和集合マイニング.  人工知能学会第97回人工知能基本問題研究会,人工知能学会研究会資料 SIG-FPAI-B404, 2015 (人工知能学会研究会優秀賞受賞). 

[19] 山本 泰生.SBGNに基づく分子ネットワーク推論の利用.GPCR研究会 (ポスター発表),2014.

[18] 宮城 智輝,山本 泰生,岩沼 宏治.フーリエ変換を用いた命題論理式の充足可能性に関する考察ー第2報ー.人工知能学会全国大会 (第27回),2014.

[17] 黒岩 健歩,岩沼 宏治,山本 泰生.関連尺度に基づいた負の相関ルール抽出手法の高機能化.人工知能学会全国大会 (第27回),2014.

[16] 平沼 祐人,山本 泰生,岩沼 宏治.遺伝子発現データを用いた転写因子束縛ネットワークの状態推定.人工知能学会全国大会 (第27回),2014.

[15] 宮城 智輝,山本 泰生,岩沼 宏治.大規模SAT問題における効率的な非ゼロフーリエ係数の数え上げ.人工知能学会第92回人工知能基本問題研究会,人工知能学会研究会資料 SIG-FPAI-B303,2014.

[14] 福田 翔士,岩沼 宏治,山本 泰生.可変長トランザクションからなるストリーム上のオンライン型アイテムマイニング.人工知能学会第92回人工知能基本問題研究会,人工知能学会研究会資料 SIG-FPAI-B303,2014.

[13] 井出 典子,岩沼 宏治,山本 泰生.負の相関ルールを抽出する高速トップダウン型アルゴリズム.人工知能学会第92回人工知能基本問題研究会,人工知能学会研究会資料 SIG-FPAI-B303,2014.

[12] 山本 泰生,岩沼 宏治,坂本 悠.仮説推論に基づく分子ネットワークの補完.電子情報通信学会技術研究報告,Vol. 113(332), pp.1-6, 2013.

[11] 山本 泰生.仮説推論による分子ネットワーク上のミッシングリンク補完.定量生物学会第六回年会 (ポスター発表),2013.

[10] 宮城 智輝,山本 泰生,岩沼 宏治.フーリエ変換を用いた命題論理式の充足可能性に関する一考察.人工知能学会全国大会 (第27回),2013.

[9] 坂本 悠, 山本 泰生, 岩沼 宏治. 多値論理を用いた生体ネットワークシステムのモデル検査. 情報処理学会 バイオ情報学第29回研究会 (SIG BIO), 2012.

[8] 宮城 智輝, 山本 泰生, 岩沼 宏治. 時系列信号処理に基づくSAT解法: Wave-SATソルバの実現に向けて. 人工知能学会全国大会 (第26回), 2012.

[7] 坂本 悠, 山本 泰生, 岩沼 宏治. 論理モデルによるグルコース抑制機構のパスウェイ補完. 人工知能学会全国大会 (第25),  2011.

[6] 山本 泰生鍋島 英知, 岩沼 宏治. 一般双対化問題における冗長節生成の抑止法とその評価. 人工知能学会全国大会 (第25),  2011.

[5] 山本 泰生鍋島 英知, 岩沼 宏治. 単調双対化計算に基づく一般双対化問題の解法人工知能基本問題研究会 (第81),  2011.

[4] 山本 泰生, 井上 克巳. CF帰納法の効率的実装とパスウェイ推論への応用. 人工知能基本問題研究会 (71), 人工知能学会研究会資料 SIG-FPAI-A802, 2008.

[3] 山本 泰生, 井上 克巳, Ray Oliver. CF帰納法の理論的再構築について. 電子情報通信学会技術研究報告, volume 107, number 78, 2007.

[2] 山本 泰生, 井上 克巳. CF帰納法における一般化に関する考察―第2報―. 電子情報通信学会技術研究報告, volume 104, number 726, 2005.

[1] 山本 泰生, 井上 克巳. CF帰納法における一般化に関する考察. 電子情報通信学会技術研究報告, volume 103, number 623, 2004.