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YAMADA, MAKOTO

Graduate School of Informatics, Department of Intelligence Science and Technology Associate Professor

YAMADA, MAKOTO
list
    Last Updated :2022/05/14

    Basic Information

    Affiliated programs (koza)

    • Graduate School of Pharmaceutical Sciences, 医薬創成情報科学専攻 医薬創成情報科学講座, 助教

    Faculty

    • 工学部

    Professional Memberships

    • 音響学会
    • AAAI
    • IEICE
    • IEEE

    Academic Degree

    • 修士(工学)(Colorado State University)
    • 博士(統計科学)(総合研究大学院大学)

    Research History

    • From Apr. 2010, To Jun. 2012
      Tokyo Institute of Technology
    • From Jul. 2007, To Mar. 2010
      ヤマハ株式会社
    • From Jul. 2005, To Jun. 2007
      ㈱日立製作所
    • Kyoto University
    • Kyoto University Institute for Chemical Research Bioinformatics Center, 助教

    ID,URL

    Website(s) (URL(s))

    researchmap URL

    list
      Last Updated :2022/05/14

      Research

      Research Interests

      • 画像処理
      • 信号処理
      • 機械学習

      Research Areas

      • Natural sciences, Applied mathematics and statistics
      • Natural sciences, Basic mathematics
      • Informatics, Intelligent informatics

      Papers

      • Re-evaluating Word Mover's Distance.
        Ryoma Sato; Makoto Yamada; Hisashi Kashima
        CoRR, 2021
      • Poincare: Recommending Publication Venues via Treatment Effect Estimation.
        Ryoma Sato; Makoto Yamada; Hisashi Kashima
        CoRR, 2020
      • Fast Unbalanced Optimal Transport on Tree.
        Ryoma Sato; Makoto Yamada; Hisashi Kashima
        CoRR, 2020
      • Random Features Strengthen Graph Neural Networks.
        Ryoma Sato; Makoto Yamada; Hisashi Kashima
        CoRR, 2020
      • Fast and Robust Comparison of Probability Measures in Heterogeneous Spaces.
        Ryoma Sato; Marco Cuturi; Makoto Yamada; Hisashi Kashima
        CoRR, 2020
      • Neural Methods for Point-wise Dependency Estimation
        Yao-Hung Hubert Tsai; Han Zhao 0002; Makoto Yamada; Louis-Philippe Morency; Russ R. Salakhutdinov
        Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020(NeurIPS), 2020
      • Fast Unbalanced Optimal Transport on a Tree
        Ryoma Sato; Makoto Yamada; Hisashi Kashima
        NeurIPS, 2020
      • Topological Bayesian Optimization with Persistence Diagrams
        Tatsuya Shiraishi; Tam Le; Hisashi Kashima; Makoto Yamada
        ECAI, 2020
      • Simultaneous Link Prediction on Unaligned Networks Using Graph Embedding and Optimal Transport
        Luu Huu Phuc; Koh Takeuchi; Makoto Yamada; Hisashi Kashima
        DSAA, 2020
      • Semantic Correspondence as an Optimal Transport Problem
        Yanbin Liu; Linchao Zhu; Makoto Yamada; Yi Yang 0001
        CVPR, 2020
      • More Powerful Selective Kernel Tests for Feature Selection
        Jen Ning Lim; Makoto Yamada; Wittawat Jitkrittum; Yoshikazu Terada; Shigeyuki Matsui; Hidetoshi Shimodaira
        AISTATS, 2020
      • Sparse Hilbert-Schmidt Independence Criterion Regression
        Benjamin Poignard; Makoto Yamada
        AISTATS, 2020
      • Unsupervised Nonlinear Feature Selection from High-Dimensional Signed Networks
        Qiang Huang; Tingyu Xia; Huiyan Sun; Makoto Yamada; Yi Chang 0001
        AAAI, 2020
      • Scaled Coupled Norms and Coupled Higher-Order Tensor Completion.
        Kishan Wimalawarne; Makoto Yamada; Hiroshi Mamitsuka
        Neural Comput., 2020, Peer-reviewed
      • Author Correction: A practical guide to intelligent image-activated cell sorting.
        Akihiro Isozaki; Hideharu Mikami; Kotaro Hiramatsu; Shinya Sakuma; Yusuke Kasai; Takanori Iino; Takashi Yamano; Atsushi Yasumoto; Yusuke Oguchi; Nobutake Suzuki; Yoshitaka Shirasaki; Taichiro Endo; Takuro Ito; Kei Hiraki; Makoto Yamada; Satoshi Matsusaka; Takeshi Hayakawa; Hideya Fukuzawa; Yutaka Yatomi; Fumihito Arai; Dino Di Carlo; Atsuhiro Nakagawa; Yu Hoshino; Yoichiroh Hosokawa; Sotaro Uemura; Takeaki Sugimura; Yasuyuki Ozeki; Nao Nitta; Keisuke Goda
        Nature protocols, Nov. 2019, Peer-reviewed
      • Intelligent whole-blood imaging flow cytometry for simple, rapid, and cost-effective drug-susceptibility testing of leukemia.
        Hirofumi Kobayashi; Cheng Lei; Yi Wu; Chun-Jung Huang; Atsushi Yasumoto; Masahiro Jona; Wenxuan Li; Yunzhao Wu; Yaxiaer Yalikun; Yiyue Jiang; Baoshan Guo; Chia-Wei Sun; Yo Tanaka; Makoto Yamada; Yutaka Yatomi; Keisuke Goda
        Lab on a chip, 06 Aug. 2019, Peer-reviewed
      • A practical guide to intelligent image-activated cell sorting.
        Akihiro Isozaki; Hideharu Mikami; Kotaro Hiramatsu; Shinya Sakuma; Yusuke Kasai; Takanori Iino; Takashi Yamano; Atsushi Yasumoto; Yusuke Oguchi; Nobutake Suzuki; Yoshitaka Shirasaki; Taichiro Endo; Takuro Ito; Kei Hiraki; Makoto Yamada; Satoshi Matsusaka; Takeshi Hayakawa; Hideya Fukuzawa; Yutaka Yatomi; Fumihito Arai; Dino Di Carlo; Atsuhiro Nakagawa; Yu Hoshino; Yoichiroh Hosokawa; Sotaro Uemura; Takeaki Sugimura; Yasuyuki Ozeki; Nao Nitta; Keisuke Goda
        Nature protocols, Aug. 2019, Peer-reviewed
      • More Powerful Selective Kernel Tests for Feature Selection.
        Jen Ning Lim; Makoto Yamada; Wittawat Jitkrittum; Yoshikazu Terada; Shigeyuki Matsui; Hidetoshi Shimodaira
        CoRR, 2019
      • Approximation Ratios of Graph Neural Networks for Combinatorial Problems.
        Ryoma Sato; Makoto Yamada; Hisashi Kashima
        CoRR, 2019
      • Topological Bayesian Optimization with Persistence Diagrams.
        Tatsuya Shiraishi; Tam Le; Hisashi Kashima; Makoto Yamada
        CoRR, 2019
      • Learning to Find Hard Instances of Graph Problems.
        Ryoma Sato; Makoto Yamada; Hisashi Kashima
        CoRR, 2019
      • Constant Time Graph Neural Networks.
        Ryoma Sato; Makoto Yamada; Hisashi Kashima
        CoRR, 2019
      • Learning to Sample Hard Instances for Graph Algorithms.
        Ryoma Sato; Makoto Yamada; Hisashi Kashima
        2019
      • Tree-Sliced Variants of Wasserstein Distances.
        Tam Le; Makoto Yamada; Kenji Fukumizu; Marco Cuturi
        Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019(NeurIPS), 2019, Peer-reviewed
      • Approximation Ratios of Graph Neural Networks for Combinatorial Problems.
        Ryoma Sato; Makoto Yamada; Hisashi Kashima
        Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019(NeurIPS), 2019, Peer-reviewed
      • Kernel Stein Tests for Multiple Model Comparison.
        Jen Ning Lim; Makoto Yamada; Bernhard Schölkopf; Wittawat Jitkrittum
        Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019(NeurIPS), 2019, Peer-reviewed
      • OSTNet: Calibration Method for Optical See-Through Head-Mounted Displays via Non-Parametric Distortion Map Generation.
        Kiyosato Someya; Yuichi Hiroi; Makoto Yamada; Yuta Itoh 0001
        2019, Peer-reviewed
      • Transformer Dissection: An Unified Understanding for Transformer's Attention via the Lens of Kernel.
        Yao-Hung Hubert Tsai; Shaojie Bai; Makoto Yamada; Louis-Philippe Morency; Ruslan Salakhutdinov
        Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019, Peer-reviewed
      • Context-Regularized Neural Collaborative Filtering for Game App Recommendation.
        Shonosuke Harada; Kazuki Taniguchi; Makoto Yamada; Hisashi Kashima
        Proceedings of ACM RecSys 2019 Late-Breaking Results co-located with the 13th ACM Conference on Recommender Systems, RecSys 2019 Late-Breaking Results, Copenhagen, Denmark, September 16-20, 2019., 2019, Peer-reviewed
      • Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator.
        Makoto Yamada; Denny Wu; Yao-Hung Hubert Tsai; Hirofumi Ohta; Ruslan Salakhutdinov; Ichiro Takeuchi; Kenji Fukumizu
        7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019, 2019, Peer-reviewed
      • Robust Sample-Specific Stability Selection with Effective Error Control.
        Heewon Park; Makoto Yamada; Seiya Imoto; Satoru Miyano
        J. Comput. Biol., 2019, Peer-reviewed
      • Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional data.
        Héctor Climente-González; Chloé-Agathe Azencott; Samuel Kaski; Makoto Yamada
        Bioinform., 2019, Peer-reviewed
      • Intelligent Image-Activated Cell Sorting.
        Nao Nitta; Takeaki Sugimura; Akihiro Isozaki; Hideharu Mikami; Kei Hiraki; Shinya Sakuma; Takanori Iino; Fumihito Arai; Taichiro Endo; Yasuhiro Fujiwaki; Hideya Fukuzawa; Misa Hase; Takeshi Hayakawa; Kotaro Hiramatsu; Yu Hoshino; Mary Inaba; Takuro Ito; Hiroshi Karakawa; Yusuke Kasai; Kenichi Koizumi; SangWook Lee; Cheng Lei; Ming Li; Takanori Maeno; Satoshi Matsusaka; Daichi Murakami; Atsuhiro Nakagawa; Yusuke Oguchi; Minoru Oikawa; Tadataka Ota; Kiyotaka Shiba; Hirofumi Shintaku; Yoshitaka Shirasaki; Kanako Suga; Yuta Suzuki; Nobutake Suzuki; Yo Tanaka; Hiroshi Tezuka; Chihana Toyokawa; Yaxiaer Yalikun; Makoto Yamada; Mai Yamagishi; Takashi Yamano; Atsushi Yasumoto; Yutaka Yatomi; Masayuki Yazawa; Dino Di Carlo; Yoichiroh Hosokawa; Sotaro Uemura; Yasuyuki Ozeki; Keisuke Goda
        Cell, 20 Sep. 2018, Peer-reviewed
      • High-throughput imaging flow cytometry by optofluidic time-stretch microscopy.
        Lei C; Kobayashi H; Wu Y; Li M; Isozaki A; Yasumoto A; Mikami H; Ito T; Nitta N; Sugimura T; Yamada M; Yatomi Y; Di Carlo D; Ozeki Y; Goda K
        Nature protocols, Jul. 2018, Peer-reviewed
      • Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data
        Makoto Yamada; Jiliang Tang; Jose Lugo-Martinez; Ermin Hodzic; Raunak Shrestha; Avishek Saha; Hua Ouyang; Dawei Yin; Hiroshi Mamitsuka; Cenk Sahinalp; Predrag Radivojac; Filippo Menczer; Yi Chang
        IEEE Transactions on Knowledge and Data Engineering, 01 Jul. 2018, Peer-reviewed
      • Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams.
        Tam Le; Makoto Yamada
        Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3-8 December 2018, Montréal, Canada., 2018, Peer-reviewed
      • Convex Coupled Matrix and Tensor Completion.
        Kishan Wimalawarne; Makoto Yamada,Array
        Neural Computation, 2018, Peer-reviewed
      • Post Selection Inference with Kernels.
        Makoto Yamada; Yuta Umezu; Kenji Fukumizu; Ichiro Takeuchi
        International Conference on Artificial Intelligence and Statistics, AISTATS 2018, 9-11 April 2018, Playa Blanca, Lanzarote, Canary Islands, Spain, 2018, Peer-reviewed
      • Intra-/inter-user adaptation framework for wearable gesture sensing device.
        Kosuke Kikui; Yuta Itoh; Makoto Yamada; Yuta Sugiura; Maki Sugimoto
        Proceedings of the 2018 ACM International Symposium on Wearable Computers, UbiComp 2018, Singapore, Singapore, October 8-12, 2018, 2018, Peer-reviewed
      • Learning Unsupervised Word Translations Without Adversaries.
        Tanmoy Mukherjee; Makoto Yamada; Timothy; M. Hospedales
        Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018, 2018, Peer-reviewed
      • Optimizing Whole-Page Presentation for Web Search.
        Yue Wang; Dawei Yin; Luo Jie; Pengyuan Wang; Makoto Yamada; Yi Chang; Qiaozhu Mei
        TWEB, 2018, Peer-reviewed
      • Convex factorization machine for toxicogenomics prediction
        Makoto Yamada; Wenzhao Lian; Amit Goyal; Jianhui Chen; Kishan Wimalawarne; Suleiman A. Khan; Samuel Kaski; Hiroshi Mamitsuka; Yi Chang
        Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13 Aug. 2017, Peer-reviewed
      • Localized Lasso for High-Dimensional Regression.
        Makoto Yamada; Koh Takeuchi; Tomoharu Iwata; John Shawe-Taylor; Samuel Kaski
        Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, 20-22 April 2017, Fort Lauderdale, FL, USA, 2017, Peer-reviewed
      • Lifecycle Modeling for Buzz Temporal Pattern Discovery
        Yi Chang; Makoto Yamada; Antonio Ortega; Yan Liu
        ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA, Dec. 2016, Peer-reviewed
      • Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models.
        Tomoharu Iwata; Makoto Yamada
        Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, 2016, Peer-reviewed
      • Timeline Summarization from Social Media with Life Cycle Models.
        Yi Chang; Jiliang Tang; Dawei Yin; Makoto Yamada; Yan Liu
        Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9-15 July 2016, 2016, Peer-reviewed
      • A Robust Convex Formulation for Ensemble Clustering.
        Junning Gao; Makoto Yamada; Samuel Kaski; Hiroshi Mamitsuka; Shanfeng Zhu
        Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9-15 July 2016, 2016, Peer-reviewed
      • Which Tumblr Post Should I Read Next?
        Zornitsa Kozareva; Makoto Yamada
        Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, August 7-12, 2016, Berlin, Germany, Volume 2: Short Papers, 2016, Peer-reviewed
      • Sparse Network Lasso for Local High-dimensional Regression.
        Makoto Yamada; Koh Takeuchi; Tomoharu Iwata; John Shawe-Taylor; Samuel Kaski
        CoRR, 2016, Peer-reviewed
      • Beyond Ranking: Optimizing Whole-Page Presentation
        Yue Wang; Dawei Yin; Luo Jie; Pengyuan Wang; Makoto Yamada; Yi Chang; Qiaozhu Mei
        PROCEEDINGS OF THE NINTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'16), 2016, Peer-reviewed
      • Cross-Domain Matching with Squared-Loss Mutual Information
        Makoto Yamada; Leonid Sigal; Michalis Raptis; Machiko Toyoda; Yi Chang; Masashi Sugiyama
        IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Sep. 2015, Peer-reviewed
      • Convex Factorization Machine for Regression.
        Makoto Yamada; Amit Goyal; Yi Chang
        CoRR, 2015, Peer-reviewed
      • Consistent Collective Matrix Completion under Joint Low Rank Structure.
        Suriya Gunasekar; Makoto Yamada; Dawei Yin; Yi Chang
        Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2015, San Diego, California, USA, May 9-12, 2015, 2015, Peer-reviewed
      • Least-squares independence regression for non-linear causal inference under non-Gaussian noise
        Makoto Yamada; Masashi Sugiyama; Jun Sese
        MACHINE LEARNING, Sep. 2014, Peer-reviewed
      • Information-Theoretic Semi-Supervised Metric Learning via Entropy Regularization
        Gang Niu; Bo Dai; Makoto Yamada; Masashi Sugiyama
        NEURAL COMPUTATION, Aug. 2014, Peer-reviewed
      • Domain Adaptation for Structured Regression
        Makoto Yamada; Leonid Sigal; Yi Chang
        INTERNATIONAL JOURNAL OF COMPUTER VISION, Aug. 2014, Peer-reviewed
      • Covariate Shift Adaptation for Discriminative 3D Pose Estimation
        Makoto Yamada; Leonid Sigal; Michalis Raptis
        IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Feb. 2014, Peer-reviewed
      • Consistent Collective Matrix Completion under Joint Low Rank Structure.
        Suriya Gunasekar; Makoto Yamada; Dawei Yin; Yi Chang
        CoRR, 2014, Peer-reviewed
      • Multi-view Anomaly Detection via Probabilistic Latent Variable Models.
        Tomoharu Iwata; Makoto Yamada
        CoRR, 2014, Peer-reviewed
      • N$^3$LARS: Minimum Redundancy Maximum Relevance Feature Selection for Large and High-dimensional Data.
        Makoto Yamada; Avishek Saha; Hua Ouyang; Dawei Yin; Yi Chang
        CoRR, 2014, Peer-reviewed
      • Ups and Downs in Buzzes: Life Cycle Modeling for Temporal Pattern Discovery
        Yi Chang; Makoto Yamada; Antonio Ortega; Yan Liu
        2014 IEEE International Conference on Data Mining (ICDM), 2014, Peer-reviewed
      • High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso
        Makoto Yamada; Wittawat Jitkrittum; Leonid Sigal; Eric P. Xing; Masashi Sugiyama
        NEURAL COMPUTATION, Jan. 2014, Peer-reviewed
      • Information-Maximization Clustering Based on Squared-Loss Mutual Information
        Masashi Sugiyama; Gang Niu; Makoto Yamada; Manabu Kimura; Hirotaka Hachiya
        NEURAL COMPUTATION, Jan. 2014, Peer-reviewed
      • Change-point detection in time-series data by relative density-ratio estimation
        Song Liu; Makoto Yamada; Nigel Collier; Masashi Sugiyama
        NEURAL NETWORKS, Jul. 2013, Peer-reviewed
      • Image context discovery from socially curated contents
        Akisato Kimura; Katsuhiko Ishiguro; Makoto Yamada; Alejandro Marcos Alvarez; Kaori Kataoka; Kazuhiko Murasaki
        MM 2013 - Proceedings of the 2013 ACM Multimedia Conference, 2013, Peer-reviewed
      • Exploiting socially-generated side information in dimensionality reduction
        Alejandro Marcos Alvarez; Makoto Yamada; Akisato Kimura
        SAM 2013 - Proceedings of the 2nd International Workshop on Socially-Aware Multimedia, Co-located with ACM Multimedia 2013, 2013, Peer-reviewed
      • Change-Point Detection with Feature Selection in High-Dimensional Time-Series Data.
        Makoto Yamada; Akisato Kimura; Futoshi Naya; Hiroshi Sawada
        IJCAI 2013, Proceedings of the 23rd International Joint Conference on Artificial Intelligence, Beijing, China, August 3-9, 2013, 2013, Peer-reviewed
      • Clustering-based anomaly detection in multi-view data.
        Alejandro Marcos Alvarez; Makoto Yamada; Akisato Kimura; Tomoharu Iwata
        22nd ACM International Conference on Information and Knowledge Management, CIKM'13, San Francisco, CA, USA, October 27 - November 1, 2013, 2013, Peer-reviewed
      • Relative Density-Ratio Estimation for Robust Distribution Comparison.
        Makoto Yamada; Taiji Suzuki; Takafumi Kanamori; Hirotaka Hachiya; Masashi Sugiyama
        Neural Computation, 2013, Peer-reviewed
      • Direct divergence approximation between probability distributions and its applications in machine learning
        Masashi Sugiyama; Song Liu; Marthinus Christoffel du Plessis; Masao Yamanaka; Makoto Yamada; Taiji Suzuki; Takafumi Kanamori
        Journal of Computing Science and Engineering, 2013, Peer-reviewed
      • Noise adaptive optimization of matrix initialization for frequency-domain independent component analysis
        Makoto Yamada; Gordon Wichern; Kazunobu Kondo; Masashi Sugiyama; Hiroshi Sawada
        DIGITAL SIGNAL PROCESSING, Jan. 2013, Peer-reviewed
      • On Kernel Parameter Selection in Hilbert-Schmidt Independence Criterion
        Masashi Sugiyama; Makoto Yamada
        IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, Oct. 2012, Peer-reviewed
      • Information-theoretic Semi-supervised Metric Learning via Entropy Regularization
        Gang Niu; Bo Dai; Makoto Yamada; Masashi Sugiyama
        CoRR, 2012, Peer-reviewed
      • Dependence Maximizing Temporal Alignment via Squared-Loss Mutual Information
        Makoto Yamada; Leonid Sigal,Michalis Raptis; Masashi Sugiyama
        CoRR, 2012, Peer-reviewed
      • Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation
        Song Liu; Makoto Yamada; Nigel Collier; Masashi Sugiyama
        STRUCTURAL, SYNTACTIC, AND STATISTICAL PATTERN RECOGNITION, 2012, Peer-reviewed
      • High-Dimensional Feature Selection by Feature-Wise Non-Linear Lasso
        Makoto Yamada; Wittawat Ji; krittum; Leonid Sigal; Masashi Sugiyama
        CoRR, 2012, Peer-reviewed
      • Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation
        Song Liu; Makoto Yamada; Nigel Collier; Masashi Sugiyama
        STRUCTURAL, SYNTACTIC, AND STATISTICAL PATTERN RECOGNITION, 2012, Peer-reviewed
      • Information-theoretic Semi-supervised Metric Learning via Entropy Regularization.
        Gang Niu; Bo Dai; Makoto Yamada; Masashi Sugiyama
        Proceedings of the 29th International Conference on Machine Learning, ICML 2012, Edinburgh, Scotland, UK, June 26 - July 1, 2012, 2012, Peer-reviewed
      • No Bias Left behind: Covariate Shift Adaptation for Discriminative 3D Pose Estimation
        Makoto Yamada; Leonid Sigal; Michalis Raptis
        COMPUTER VISION - ECCV 2012, PT IV, 2012, Peer-reviewed
      • Improving the Accuracy of Least-Squares Probabilistic Classifiers
        Makoto Yamada; Masashi Sugiyama; Gordon Wichern; Jaak Simm
        IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, Jun. 2011, Peer-reviewed
      • Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search
        Masashi Sugiyama; Makoto Yamada; Paul von Buenau; Taiji Suzuki; Takafumi Kanamori; Motoaki Kawanabe
        NEURAL NETWORKS, Mar. 2011, Peer-reviewed
      • Direct Density-Ratio Estimation with Dimensionality Reduction via Hetero-Distributional Subspace Analysis
        Makoto Yamada; Masashi Sugiyama
        In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI-11), 2011, Peer-reviewed
      • Cross-Domain Object Matching with Model Selection
        Makoto Yamada; Masashi Sugiyama
        In Proceedings of Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS2011), 2011, Peer-reviewed
      • Computationally efficient sufficient dimension reduction via squared-loss mutual information
        Makoto Yamada; Gang Niu; Jun Takagi; Masashi Sugiyama
        Journal of Machine Learning Research, 2011, Peer-reviewed
      • SERAPH: Semi-supervised Metric Learning Paradigm with Hyper Sparsity
        Gang Niu; Bo Dai; Makoto Yamada; Masashi Sugiyama
        CoRR, 2011, Peer-reviewed
      • Suffcient Component Analysis.
        Makoto Yamada; Gang Niu; Jun Takagi; Masashi Sugiyama
        Proceedings of the 3rd Asian Conference on Machine Learning, ACML 2011, Taoyuan, Taiwan, November 13-15, 2011, 2011, Peer-reviewed
      • Relative Density-Ratio Estimation for Robust Distribution Comparison.
        Makoto Yamada; Taiji Suzuki; Takafumi Kanamori; Hirotaka Hachiya; Masashi Sugiyama
        Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, Granada, Spain., 2011, Peer-reviewed
      • On information-maximization clustering: Tuning parameter selection and analytic solution
        Masashi Sugiyama; Makoto Yamada; Manabu Kimura; Hirotaka Hachiya
        Proceedings of the 28th International Conference on Machine Learning, ICML 2011, 2011, Peer-reviewed
      • AUTOMATIC AUDIO TAG CLASSIFICATION VIA SEMI-SUPERVISED CANONICAL DENSITY ESTIMATION
        Jun Takagi; Yasunori Ohishi; Akisato Kimura; Masashi Sugiyama; Makoto Yamada; Hirokazu Kameoka
        2011 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2011, Peer-reviewed
      • Direct Importance Estimation with a Mixture of Probabilistic Principal Component Analyzers
        Makoto Yamada; Masashi Sugiyama; Gordon Wichern; Jaak Simm
        IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, Oct. 2010, Peer-reviewed
      • Semi-supervised speaker identification under covariate shift
        Makoto Yamada; Masashi Sugiyama; Tomoko Matsui
        SIGNAL PROCESSING, Aug. 2010, Peer-reviewed
      • DIRECT IMPORTANCE ESTIMATION WITH PROBABILISTIC PRINCIPAL COMPONENT ANALYZERS
        Makoto Yamada; Masashi Sugiyama; Gordon Wichern
        2010 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2010, Peer-reviewed
      • ACCELERATION OF SEQUENCE KERNEL COMPUTATION FOR REAL-TIME SPEAKER IDENTIFICATION
        Makoto Yamada; Masashi Sugiyama; Gordon Wichern; Tomoko Matsui
        2010 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2010, Peer-reviewed
      • AUTOMATIC AUDIO TAGGING USING COVARIATE SHIFT ADAPTATION
        Gordon Wichern; Makoto Yamada; Harvey Thornburg; Masashi Sugiyama; Andreas Spanias
        2010 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2010, Peer-reviewed
      • Dependence Minimizing Regression with Model Selection for Non-Linear Causal Inference under Non-Gaussian Noise.
        Makoto Yamada; Masashi Sugiyama
        Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010, Atlanta, Georgia, USA, July 11-15, 2010, 2010, Peer-reviewed
      • Direct Importance Estimation with Gaussian Mixture Models
        Makoto Yamada; Masashi Sugiyama
        IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, Oct. 2009, Peer-reviewed
      • A semi-blind source separation method with a less amount of computation suitable for tiny DSP modules.
        Kazunobu Kondo; Makoto Yamada; Hideki Kenmochi
        INTERSPEECH 2009, 10th Annual Conference of the International Speech Communication Association, Brighton, United Kingdom, September 6-10, 2009, 2009, Peer-reviewed
      • Covariate shift adaptation for semi-supervised speaker identification.
        Makoto Yamada; Masashi Sugiyama; Tomoko Matsui
        Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009, 19-24 April 2009, Taipei, Taiwan, 2009, Peer-reviewed
      • Nonlinear signal estimation using kernel Wiener filter in Canonical Correlation Analysis framework
        Makoto Yamada; Mahmood R. Azimi-Sadjadi
        INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR MODELLING, CONTROL & AUTOMATION JOINTLY WITH INTERNATIONAL CONFERENCE ON INTELLIGENT AGENTS, WEB TECHNOLOGIES & INTERNET COMMERCE, VOL 1, PROCEEDINGS, 2006, Peer-reviewed
      • Kernel Wiener Filter with Distance Constraint.
        Makoto Yamada; Mahmood; R. Azimi-Sadjadi
        2006 IEEE International Conference on Acoustics Speech and Signal Processing, ICASSP 2006, Toulouse, France, May 14-19, 2006, 2006, Peer-reviewed
      • Kernel Wiener filter using Canonical Correlation Analysis framework
        Makoto Yamada; Mahmood R. Azimi-Sadjadi
        2005 IEEE/SP 13TH WORKSHOP ON STATISTICAL SIGNAL PROCESSING (SSP), VOLS 1 AND 2, 2005, Peer-reviewed

      Misc.

      • Learning under nonstationarity: Covariate shift and class-balance change
        Masashi Sugiyama; Makoto Yamada; Marthinus Christoffel du Plessis
        Wiley Interdisciplinary Reviews: Computational Statistics, Nov. 2013
      • Relative Density-Ratio Estimation for Robust Distribution Comparison
        Makoto Yamada; Taiji Suzuki; Takafumi Kanamori; Hirotaka Hachiya; Masashi Sugiyama
        23 Jun. 2011
      • SERAPH: Semi-supervised Metric Learning Paradigm with Hyper Sparsity
        Gang Niu; Bo Dai; Makoto Yamada; Masashi Sugiyama
        01 May 2011
      • Least-Squares Independence Regression for Non-Linear Causal Inference under Non-Gaussian Noise
        Makoto Yamada; Masashi Sugiyama; Jun Sese
        29 Mar. 2011
      • Sufficient Component Analysis for Supervised Dimension Reduction
        Makoto Yamada; Gang Niu; Jun Takagi; Masashi Sugiyama
        26 Mar. 2011
      • Cross-Domain Object Matching with Model Selection
        Makoto Yamada; Masashi Sugiyama
        07 Dec. 2010

      Awards

      • 11 Feb. 2019
        Association for Computing Machinery (ACM), WSDM 2019 Outstanding Reviewer award
      • 03 Feb. 2020
        Association for Computing Machinery (ACM), WSDM 2019 Outstanding Reviewer award

      External funds: Kakenhi

      • 個別化医療の適応的臨床研究を支える統計・機械学習法に関する研究
        Grant-in-Aid for Scientific Research (A)
        Medium-sized Section 60:Information science, computer engineering, and related fields
        Nagoya University
        松井 茂之
        From 05 Apr. 2021, To 31 Mar. 2025, Granted
        個別化医療;因果推論;機械学習
      • Research and development of nonlinear Selective Inference for high-dimensional and small number of samples data
        Grant-in-Aid for Scientific Research (B)
        Basic Section 61030:Intelligent informatics-related
        Kyoto University
        山田 誠
        From 01 Apr. 2020, To 31 Mar. 2024, Granted
        選択的推論;カーネル法;統計的推論;機械学習;特徴選択
      • Developing Nonlinear Feature Selection Algorithm for Ultra High-Dimensional Data
        Grant-in-Aid for Young Scientists (B)
        Institute of Physical and Chemical Research;Kyoto University
        Makoto Yamada
        From 01 Apr. 2016, To 31 Mar. 2018, Project Closed
        特徴選択;非線形;機械学習
      • 個別化医療の開発のための統計的方法論の構築とその実践に関する総合的研究
        Grant-in-Aid for Scientific Research (S)
        Nagoya University
        松井 茂之
        From 31 May 2016, To 31 Mar. 2021, Granted
        統計科学;社会医学;薬剤反応性;個別化医療;臨床試験;研究デザイン;統計解析;診断法開発;統計数学

      External funds: others

      • 科学的発見のための非線形機械学習技術の創生
        さきがけ
        From 01 Sep. 2016, To 31 Mar. 2020
        代表
      list
        Last Updated :2022/05/14

        Education

        Teaching subject(s)

        • From Apr. 2016, To Mar. 2017
          Skill Development for Chemoinformatics
          Year-long, 薬学研究科
        • From Apr. 2016, To Mar. 2017
          Introduction to Bioinformatics
          Spring, 薬学研究科
        • From Apr. 2018, To Mar. 2019
          Computer Science Laboratory and Exercise 2
          Fall, 工学部
        • From Apr. 2018, To Mar. 2019
          Computer Science Laboratory and Exercise 3
          Spring, 工学部
        • From Apr. 2019, To Mar. 2020
          Information and Business
          Spring, 工学部
        • From Apr. 2019, To Mar. 2020
          Introductory Statistics
          Spring, 全学共通科目
        • From Apr. 2019, To Mar. 2020
          Statistical Learning Theory
          Spring, 工学研究科
        • From Apr. 2019, To Mar. 2020
          Statistical Learning Theory
          Spring, 情報学研究科
        • From Apr. 2019, To Mar. 2020
          Computer Science Laboratory and Exercise 2
          Fall, 工学部
        • From Apr. 2019, To Mar. 2020
          Computer Science Laboratory and Exercise 3
          Spring, 工学部
        • From Apr. 2020, To Mar. 2021
          Statistics and Artificial Intelligence
          Spring, 全学共通科目
        • From Apr. 2020, To Mar. 2021
          Statistics and Artificial Intelligence
          Fall, 全学共通科目
        • From Apr. 2020, To Mar. 2021
          Statistical Learning Theory
          Spring, 工学研究科
        • From Apr. 2020, To Mar. 2021
          Statistical Learning Theory
          Spring, 情報学研究科
        • From Apr. 2020, To Mar. 2021
          Computer Science Laboratory and Exercise 2
          Fall, 工学部
        • From Apr. 2021, To Mar. 2022
          ILAS Seminar :Machine Learning
          Spring, 全学共通科目
        • From Apr. 2021, To Mar. 2022
          Statistical Learning Theory
          Spring, 全学共通科目
        • From Apr. 2021, To Mar. 2022
          Reading and Writing Scientific English
          Spring, 工学部
        • From Apr. 2021, To Mar. 2022
          Statistics and Artificial Intelligence
          Spring, 全学共通科目
        • From Apr. 2021, To Mar. 2022
          Statistics and Artificial Intelligence
          Fall, 全学共通科目
        • From Apr. 2021, To Mar. 2022
          Statistical Learning Theory
          Spring, 工学研究科
        • From Apr. 2021, To Mar. 2022
          Statistical Learning Theory
          Spring, 情報学研究科
        • From Apr. 2021, To Mar. 2022
          Computer Science Laboratory and Exercise 2
          Fall, 工学部

        Student achievements: Awards

        • 人工知能学会2019年度全国大会学生奨励賞
          白石 竜也, 人工知能学会, 04 Jun. 2019
        • 全国大会学生奨励賞
          豊國 郁⼈, 人工知能学会, 2020
        list
          Last Updated :2022/05/14

          Administration

          Faculty management (title, position)

          • From 01 Apr. 2016, To 31 Mar. 2017
            化学研究所広報委員会委員
          • From 01 Apr. 2018, To 31 Mar. 2019
            図書WG委員
          • From 01 Apr. 2021, To 31 Mar. 2022
            評価WG委員
          • From 01 Apr. 2021, To 31 Mar. 2022
            図書WG委員(兼任 工学部計算機コース)
          list
            Last Updated :2022/05/14

            Academic, Social Contribution

            Committee Memberships

            • From Nov. 2016, To Nov. 2016
              プログラム委員, IBIS 2016

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