Research

We have two goals:

  • Machine learning for science: Develop machine learning methods that help scientists finding new scientific discoveries.
  • Machine learning: Develop fundamental machine learning algorithms.

Optimal Transport

  • Yuki Takezawa, Ryoma Sato, Makoto Yamada, Supervised Tree-Wasserstein Distance, 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
  • Yanbin Liu, Makoto Yamada, Yao-Hung Hubert Tsai, Tam Le, Ruslan Salakhutdinov, Yi Yang, LSMI-Sinkhorn: Semi-supervised Squared-Loss Mutual Information Estimation with Optimal Transport, ECML-PKDD 2021
  • Tam Le, Naht Ho, Makoto Yamada (*:equal contribution), Flow-based Alignment Approaches for Probability Measures in Different Spaces, AISTATS 2021
  • Tam Le, Truyen Nguyen (*:equal contribution), Entropy Partial Transport with Tree Metrics: Theory and Practice, AISTATS 2021
  • 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
  • Tam Le, Makoto Yamada, Kenji Fukumizu, Marco Cuturi, Tree-Sliced Variants of Wasserstein Distances, NeurIPS 2019
  • Makoto Yamada, Leonid Sigal, Michalis Raptis, Machiko Toyoda, Yi Chang, Masashi Sugiyama, Cross-Domain Matching with Squared-Loss Mutual Information. IEEE Trans. Pattern Anal. Mach. Intell. 37(9): 1764-1776 (2015)
  • Makoto Yamada, Masashi Sugiyama, Cross-Domain Object Matching with Model Selection. AISTATS 2011

Sparse Learning

  • Benjamin Poignard, Makoto Yamada, Sparse Hilbert-Schmidt Independence Criterion Regression, AISTATS 2020
  • Héctor Climente-González, Chloé-Agathe Azencott, Samuel Kaski, Makoto Yamada, Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional data,Bioinformatics (ISMB) 2019
  • Makoto Yamada, Koh Takeuchi, Tomoharu Iwata, John Shawe-Taylor, Samuel Kaski, Localized Lasso for High-Dimensional Regression, AISTATS 2017

Selective Inference for Kernels

  • Tobias Freidling, Benjamin Poignard, Héctor Climente-González, Makoto Yamada, Post-selection inference with HSIC-Lasso, ICML 2021
  • Jenning Lim, Makoto Yamada, Wittawat Jitkrittum, Yoshikazu Terada, Shigeyuki Matsui, Hidetoshi Shimodaira, More Powerful Selective Kernel Tests for Feature Selection, AISTATS 2020
  • Jenning Lim, Makoto Yamada, Bernhard Schoelkopf, Wittawat Jitkrittum, Kernel Stein Tests for Multiple Model Comparison, NeurIPS 2019
  • Makoto Yamada, Denny Wu, Yao-Hung Hubert Tsai, Hirofumi Ohta, Ruslan Salakhutdinov, Ichiro Takeuchi, Kenji Fukumizu, Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator, ICLR 2019
  • Makoto Yamada, Yuta Umezu, Kenji Fukumizu, Ichiro Takeuchi, Post Selection Inference with Kernels, AISTATS 2018