Makoto Yamada

Makoto Yamada
Associate Professor
Ph.D. in Statistical Science, March 2010, The Graduate University for Advanced Studies, Japan
M.S. in Electrical Engineering, May 2005, Colorado State University, U.S.A.
B.S. in Computer Science, Mar 2003, University of Aizu, Japan

Makoto Yamada received the PhD degree in statistical science from The Graduate University for Advanced Studies (SOKENDAI, The Institute of Statistical Mathematics), Tokyo, in 2010. He has held positions as a postdoctoral fellow with the Tokyo Institute of Technology from 2010 to 2012, as a research associate with NTT Communication Science Laboratories from 2012 to 2013, as a research scientist with Yahoo Labs from 2013 to 2015, as an assistant professor with Kyoto University from 2015 to 2017, as an team leader with RIKEN from 2017 to 2023, and as an associate professor with Kyoto University from 2018 to 2023. Currently, he is an associate professor at the Okinawa Institute of Science and Technology (OIST) His research interests include machine learning and its application to biology, natural language processing, and computer vision. He published more than 50 research papers in premium conferences and journals such as NeurIPS, AISTATS, ICML, ICLR, and TPAMI, and won the WSDM 2016 Best Paper Award.

Professional Experience

  • Kyoto University, Kyoto, Japan Associate Professor
  • RIKEN AIP, Tokyo Japan, Team Leader
  • Kyoto University, Kyoto, Japan Assistant Professor
  • Yahoo Labs, Sunnyvale USA Research Scientist


  • IEICE TC-IBISML Research Award 2019
  • Outstannding SPC award, ACM International Conference on Web Search and Data Mining (WSDM 2020)
  • Outstannding SPC award, ACM International Conference on Web Search and Data Mining (WSDM 2019)
  • Best paper award, ACM International Conference on Web Search and Data Mining (WSDM 2016)
  • Yahoo Labs Excellence Award, 2014

Select Publications

  • Ryoma Sato, Makoto Yamada, Hisashi Kashima Re-evaluating Word Mover’s Distance ICML 2022
  • Yuki Takezawa, Ryoma Sato, Zornitsa Kozareva, Sujith Ravi, Makoto Yamada Fixed Support Tree-Sliced Wasserstein Barycenter AISTATS 2022
  • Benjamin Poignard, Peter Naylor, Héctor Climente, Makoto Yamada Feature Screening with Kernel Knockoff AISTATS 2022
  • Hiroaki Yamada, Makoto Yamada Dynamic Sasvi: Strong Safe Screening for Norm-Regularized Least Squares NeurIPS 2021
  • Tam Le, Truyen Nguyen, Makoto Yamada, Jose Blanchet, Viet Anh Nguyen Adversarial Regression with Doubly Non-negative Weighting Matrices NeurIPS 2021
  • Yuki Takezawa, Ryoma Sato, Makoto Yamada Supervised Tree-Wasserstein Distance ICML 2021
  • Tobias Freidling, Benjamin Poignard, Héctor Climente-González, Makoto Yamada Post-selection inference with HSIC-Lasso ICML 2021
  • Vu Nguyen*, Tam Le*, Makoto Yamada, Michael A Osborne (*:equal contribution) Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search ICML 2021
  • Tam Le*, Naht Ho*, Makoto Yamada (*:equal contribution) Flow-based Alignment Approaches for Probability Measures in Different Spaces AISTATS 2021
  • Ryoma Sato, Makoto Yamada, Hisashi Kashima Random Features Strengthen Graph Neural Networks SDM 2021
  • Yao-Hung Hubert Tsai, Han Zhao, Makoto Yamada, Louis-Philippe Morency, Ruslan Salakhutdinov Neural Methods for Point-wise Dependency Estimation NeurIPS 2020
  • Ryoma Sato, Makoto Yamada, Hisashi Kashima Fast Unbalanced Optimal Transport on Tree NeurIPS 2020
  • Yanbin Liu, Linchao Zhu, Makoto Yamada, Yi Yang Semantic Correspondence as an Optimal Transport Problem CVPR 2020
  • Benjamin Poignard, Makoto Yamada Sparse Hilbert-Schmidt Independence Criterion Regression AISTATS 2020
  • Jenning Lim, Makoto Yamada, Wittawat Jitkrittum, Yoshikazu Terada, Shigeyuki Matsui, Hidetoshi Shimodaira More Powerful Selective Kernel Tests for Feature Selection AISTATS 2020
  • Ryoma Sato, Makoto Yamada, Hisashi Kashima Approximation Ratios of Graph Neural Networks for Combinatorial Problems NeurIPS 2019
  • Tam Le, Makoto Yamada, Kenji Fukumizu, Marco Cuturi Tree-Sliced Variants of Wasserstein Distances NeurIPS 2019
  • Jenning Lim, Makoto Yamada, Bernhard Schoelkopf, Wittawat Jitkrittum Kernel Stein Tests for Multiple Model Comparison NeurIPS 2019