Makoto Yamada#204 Research Bldg. No.7
Yoshida-honmachi,
Sakyo-ku, Kyoto, 606-8501, Japan

Research
Interests

Email: myamada@i.kyoto-u.ac.jp
http://www.makotoyamada-ml.com/

Machine Learning: Explainable AI, selective inference, optimal transport, and transfer
learning.
Science: Drug discovery for Acute Myeloid Leukemia.

Education  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.
Academic  Kyoto University, Kyoto, Japan
Experience
Associate Professor

April, 2018 - present

 RIKEN AIP center, Tokyo Japan
Team Leader (PI, equivalent to professor)

March, 2020 - present

 RIKEN AIP center, Tokyo Japan
Unit Leader (PI, equivalent to associate professor)

March, 2017 - 2020

 Institute of Statistical Mathematics (ISM), Tokyo Japan
Visiting Associate Professor
 Kyoto University, Kyoto, Japan
Assistant Professor

October, 2015 - February, 2017

 Tokyo Institute of Technology, O-okayama, Tokyo Japan
Postdoctoral Fellow
Industry Yahoo Labs, Sunnyvale USA
Experience
Research Scientist
 NTT Communication Science Laboratory, Kyoto Japan
Research Associate
Publication2018 - present

April, 2010 - June, 2012

July, 2013 - October, 2015
July, 2012 - June, 2013

Journal articles: Please note that TPAMI and IJCV are highly competitive top-tiercomputer vision journals. MLJ and NECO are competitive top-tier machine learningjournals.
1. Hashimoto, M., Saito, Y., Nakagawa, R., Ogahara, I., Takagi, S., Takata, S., Amitani, H., Endo, M., Yuki, H., Ramilowski, J. A., Severin, J., Manabe, R., Watanabe,T., Ozaki, K., Kaneko, A., Kajita, H., Fujiki, S., Sato, K., Honma, T., Uchida, N.,
Fukami, T., Okazaki, Y., Ohara, O., Shultz, L. D., Yamada, M., Taniguchi, S.,
Vyas, P., Hoon, M., Momozawa, Y., Ishikawa, F. Combined inhibition of XIAP and
BCL2 drives maximal therapeutic efficacy in genetically diverse aggressive Acute
Myeloid Leukemia. Nature Cancer 2021.
2. Takahashi, Y., Ueki, M., Yamada, M., Tamiya, G., Motoike, I., Saigusa, D.,
Sakurai, M., Nagami, F., Ogishima, S., Koshiba, S., Kinoshita, K., Yamamoto, M.,
Tomita, H. Improved metabolomic data-based prediction of depressive symptoms
using nonlinear machine learning with feature selection. Translational Psychiatry
volume 10, Article number: 157 (2020)

Makoto Yamada

3. Wimalawarne, K., Yamada, M., Mamitsuka, H., Scaled Coupled Norms and Coupled Higher-Order Tensor Completion. Neural Computation, 32(2): 447-484 (2020).
4. Saito, Y., Shin, K., Terayama, K., Desai, S., Onga, M., Nakagawa, Y., Itahashi,
Y.M., Iwasa, Y., Yamada, Y., Tsuda, K. Deep-learning-based quality filtering of
mechanically exfoliated 2D crystals npj Computational Materials, volume 5, Article
number: 124 (2019)
5. Climente-Gonzlez, H., Azencott, C-A., Kaski, S., Yamada, M., Block HSIC Lasso:model-free biomarker detection for ultra-high dimensional data. Bioinformatics.
35(14): i427-i435 (2019).
6. Isozaki, A., Mikami, H., Hiramatsu, K., Sakuma, S., Kasai, Y., Iino, T., Yamano,T., Yasumoto, A., Oguchi, Y., Suzuki, N., Shirasaki, Y., Endo, T., Ito, T., Hiraki,
K., Yamada, M., Matsusaka, S., Hayakawa, T., Fukuzawa, H., Yatomi, Y., Arai,F., Di Carlo, D., Nakagawa, A., Hoshino, Y., Hosokawa, Y., Uemura, S., Sugimura,T., Ozeki, Y., Nitta, N., Goda, K. A practical guide to intelligent image-activatedcell sorting. Nature Protocols, 14, pages23702415(2019).
7. Kobayashi, H., Lei, C., Wu, Y., Huang, C-J., Yasumoto, A., Jona, M., Li, W., Wu,
Y., Yalikun, Y., Jiang, Y., Guo, B., Sun, C-W., Tanaka, Y., Yamada, M., Yatomi,Y., Goda, K. Intelligent whole-blood imaging flow cytometry for simple, rapid, andcost-effective drug-susceptibility testing of leukemia, Lab on a Chip, 2019.
8. Heewon Park, Makoto Yamada, Seiya Imoto, Satoru Miyano: Robust SampleSpecific Stability Selection with Effective Error Control. J. Comput. Biol., 26(3):202-217 (2019)
9. Lei, C., Kobayashi, H., Wu, Yi., Li, Ming., Isozaki, A., Yasumoto, A., Mikami, H.,
Ito, T., Nitta, N., Sugimura, T., Yamada, M., Yatomi, Y., Di Carlo, D., Ozeki,Y., Goda, K High-throughput imaging flow cytometry by optofluidic time-stretch
microscopy, Nature protocols, 13, pages16031631(2018).
10. Nitta, N., Sugimura, T., Isozaki, A., Mikami, H., Hiraki, K., Sakuma, S., Iino, T.,
Arai, F., Endo, T., Fujiwaki, Y., Fukuzawa, H., Hase, M., Hayakawa, T., Hiramatsu,
K., Hoshino, Y., Inaba, M., Ito, T., Karakawa, H., Kasai, Y., Koizumi, K., Lee, S.,
Lei, C., Li, M., Maeno, T., Matsusaka, S., Murakami, D., Nakagawa, A., Oguchi,Y., Oikawa, M., Ota, T., Shiba, K., Shintaku, H., Shirasaki, Y., Suga, K., Suzuki,
Y., Suzuki, N., Tanaka, Y., Tezuka, H., Toyokawa, C., Yalikun, Y., Yamada, M.,
Yamagishi, M., Yamano, T., Yasumoto, A., Yatomi, Y., Yazawa, M., Di Carlo, D.,
Hosokawa, Y., Uemura, S., Ozeki, Y., Goda, K. Intelligent Image-Activated Cell
Sorting. Cell, 175(1):266-276.
11. Wang, Y., Yin, D., Jie, L., Wang, P., Yamada, M., Chang, Y., Mei, Q., Optimizing
Whole-Page Presentation for Web Search. Trans on WEB, 12(3): 19:1-19:25 (2018).
12. Wimalawarne, K., Yamada, M., Mamitsuka, H., Convex Coupled Matrix andTensor Completion. Neural Computation, 30(11) (2018).
13. Yamada, M., Tang, J., Lugo-Martinez, J., Hodzic, E., Shrestha, R., Saha, A.,
Ouyang, H., Yin, D., Mamitsuka, H., Sahinalp, C., Radivojac, P., Menczer, F.,
Chang, Y. Ultra High-Dimensional Nonlinear Feature Selection for Big BiologicalData. IEEE Transactions on Knowledge and Data Engineering, 30(7): 1352-1365(2018).
14. Chang, Y. , Yamada, M., Ortega, A., and Liu, Y., Lifecycle Modeling for Buzz
Temporal Pattern Discovery, TKDD, 11(2): 20:1-20:24 (2016).
15. Yamada, M., Sigal, L., Raptis, M., Toyoda, M., Chang, Yi., and Sugiyama, M.,
Cross-Domain Matching with Squared-Loss Mutual Information,IEEE Transactions
on Pattern Analysis and Machine Intelligence, vol.37, no.9, pp.1764-1776, 2015.
16. Yamada, M., Sigal, L., and Chang, Y., Domain Adaptation for Structured Prediction,International Journal of Computer Vision, vol. 109, 126-145, 2014.

Makoto Yamada

17. Niu, G., Dai, B., Yamada, M., and Sugiyama, M., Information-theoretic Semisupervised Metric Learning via Entropy Regularization, Neural Computation, vol.26,
no.8, pp.1717-1762, 2014.
18. Yamada, M., Sugiyama, M., and Sese, J., Least-Squares Independence Regression for Non-linear Causal Inference under Non-Gaussian Noise, Machine Learning.
vol.96, no.3, pp.249-267, 2014.
19. Yamada, M., Sigal, L., and Raptis, M., Covariate Shift Adaptation for Discriminative 3D Pose Estimation,IEEE Transactions on Pattern Analysis and MachineIntelligence, vol. 36, no.2, 235–247, 2014.
20. Yamada, M., Jitkrittum, W., Sigal, L., Xing, E.P., and Sugiyama, M., HighDimensional Feature Selection by Feature-Wise Kernelized Lasso,Neural Computation, vol.26, no.1, pp.185-207, 2014.
21. Sugiyama, M., Niu, G., Yamada, M., Kimura, M., and Hachiya, H., On Information Maximization Clustering: Tuning Parameter Selection and Analytic Solution,
Neural Computation, vol.26, no.1, pp.84-131, 2014.
22. Sugiyama, M., Yamada, M., and du Plessis, M. C., Learning under non-stationarity:
Covariate shift and class-balance change, WIREs Computational Statistics, 13 pages,
2013.
23. Sugiyama, M., Liu, S., M. C. du Plessis, M. Yamanaka, Yamada, M., Suzuki, T.,
and Kanamori, T., Direct Divergence Approximation between Probability Distributions and Its Applications in Machine Learning, JSCE, 7(2), pp.99–111, 2013.
24. Yamada, M., Suzuki, T., Kanamori, T., Hachiya, H., and Sugiyama, M., Relative
Density-Ratio Estimation for Robust Distribution Comparison, Neural Computation, vol.25, no.5, pp.1324-1370, 2013.
25. Liu, S., Yamada, M., Colliear, N., and Sugiyama, M., Change-Point Detection in
Time-Series Data by Relative Density-Ratio Estimation, Neural Networks, vol.43,
pp.72-83, 2013.
26. Yamada, M., Wichern, G., Kondo, K., Sugiyama, M., and Sawada, H., Noise
Adaptive Unmixing Matrix Initialization, Digital Signal Processing, vol.23, 1–8,
2013.
27. Sugiyama, M. and Yamada, M., On kernel parameter selection in Hilbert-Schmidt
independence criterion, IEICE Transactions on Information and Systems, vol.E95D, no.10, pp.2564-2567, 2012.
28. Yamada, M., Sugiyama, M., Wichern, G., and Simm, J., Improving the Accuracyof Least-Squares Probabilistic Classifiers, IEICE Transactions on Information and
Systems, vol.E94-D No.6 pp.1337-1340.
29. Sugiyama, M., Yamada, M., Bünau, P. von., Suzuki, T., Kanamori, T., andKawanabe, K., Direct Density-ratio Estimation with Dimensionality Reduction via
Least-squares Hetero-distributional Subspace Search, Neural Networks, vol.24, no.2,
pp.183–198, 2011.
30. Yamada, M., Sugiyama, M., Wichern, G., and Simm, J., Direct Importance Estimation with Generative Models, IEICE Transactions on Information and Systems,
vol. E93-D, no.10, pp.2846–2849, 2010.
31. Yamada, M., Sugiyama, M., and Matsui, T., Semi-supervised Speaker Identification under Covariate Shift, Signal Processing, vol. 90, no.8, pp.2353–2361, 2010.
32. Yamada, M. and Sugiyama, M., Direct Importance Estimation with Gaussian
Mixture Model, IEICE Transactions on Information and Systems, vol. E92-D,
no.10, pp.2159–2162, 2009.

Makoto YamadaInternational Conference paper (Reviewed)
33. Yamada, H., Yamada, M. Dynamic Sasvi: Strong Safe Screening for NormRegularized Least Squares. NeurIPS 2021
34. Le, T., Nguyen, T., Yamada, M., Blanchet, J., Nguyen, V.A. Adversarial Regression with Doubly Non-negative Weighting Matrices. NeurIPS 2021
35. Liu, Y., Yamada, M., Tsai, Y-H-H, Le, T., Salakuhtdinov, R., Yang, Y. LSMISinkhorn: Semi-supervised Squared-Loss Mutual Information Estimation with Optimal Transport. ECML 2021.
36. Takezawa, Y., Sato, S., Yamada, M. Supervised Tree-Wasserstein Distance. ICML,
2021.
37. Freidling, T., Poignard, B., Climente-Gonzlez, H., Yamada, M. Post-selection
inference with HSIC-Lasso. ICML 2021.
38. Nguyen, V., Le, T., Yamada, M., Osborne, M.A.. Optimal Transport Kernels for
Sequential and Parallel Neural Architecture Search. ICML 2021.
39. Sato, R., Yamada, M., Kashima, H. Random Features Strengthen Graph Neural
Networks, SDM, 2021.
40. Le, T., Ho, N., Yamada, M. Fast Tree Variants of Gromov-Wasserstein. AISTATS,
2021.
41. Sato, R., Yamada, M., Kashima, H., Fast Unbalanced Optimal Transport on aTree. NeurIPS, 2020
42. Tsai, Y-H-H., Zhao, H., Yamada, M., Morency, L-P., Salakhutdinov, R., Neural
Methods for Point-wise Dependency Estimation. NeurIPS, 2020
43. Liu, Y., Zhu, L., Yamada, M., Yang, Y. Semantic Correspondence as an OptimalTransport Problem. CVPR, 2020.
44. Shiraishi, T., Le, T., Kashima, H., Yamada, M.. Topological Bayesian Optimization with Persistence Diagrams. ECAI, 2020.
45. Poignard, B., Yamada, M. Sparse Hilbert-Schmidt Independence Criterion Regression. AISTATS, 2020.
46. Lim, J., Yamada, M., Jitkrittum, W., Terada, Y., Matsui, S., Shimodaira, H.
More Powerful Selective Kernel Tests for Feature Selection. AISTATS, 2020.
47. Huang, Q., Xia, T., Sun, H., Yamada, M., Chang, Y. Unsupervised Nonlinear
Feature Selection from High-dimensional Signed Networks. AAAI 2020.
48. Sato, R., Yamada, M., Kashima, H. Approximation Ratios of Graph Neural Networks for Combinatorial Problems. NeurIPS, 2019.
49. Le, T., Yamada, M., Fukumizu, K., Cuturi, M. Tree-Sliced Variants of Wasserstein
Distances. NeurIPS, 2019.
50. Lim, J., Yamada, M., Schoelkopf, B., Jitkrittum, W. Kernel Stein Tests for Multiple Model Comparison. NeurIPS, 2019.
51. Sato, R., Yamada, M., Kashima, H. Learning to Sample Hard Instances for Graph
Algorithms. ACML, 2019.
52. Tsai Y-H-H., Bai, S., Yamada, M., Morency, L-P., Salakhutdinov, R. Empirical
Study of Transformer’s Attention Mechanism via the Lens of Kernel. EMNLPIJCNLP, 2019.
53. Harada, S., Taniguchi, K., Yamada, M., Kashima, H. Context-Regularized Neural
Collaborative Filtering for Game App Recommendation. ACM RecSys LBR track,2019.

Makoto Yamada

54. Tanaka D., Yamada, M., Kashima, H., Kishikawa, T., Haga, T., Sasaki, T. InVehicle Network Intrusion Detection andExplanation Using Density Ratio Estimation. ITSC, 2019.
55. Harada, S., Taniguchi, K., Yamada, M., Kashima, H. In-app Purchase Prediction
Using Bayesian Personalized Dwell Day Ranking. AdKDD 2019.
56. Yamada, M., Wu, D., Tsai Y-H-H., Hirofumi Ota, Salakhutdinov, R., Takeuchi,
I Fukumizu, K. Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator. ICLR, 2019.
57. Le, T., Yamada, M., Persistence Fisher Kernel: A Riemannian Manifold Kernel
for Persistence Diagrams. NeurIPS, 2018.
58. Mukherjee, T., Yamada, M., Hospedales, T. Learning Unsupervised Word Translations Without Adversaries. EMNLP, 2018.
59. Kikui, K., Itoh, Y., Yamada, M., Sugiura, Y. Sugimoto, M. Intra-/Inter-user
Adaptation Framework for Wearable Gesture Sensing Device. ISWC, 2018.
60. Yamada, M., Umezu, Y., Fukumizu, K., Takeuchi, I. Post Selection Inference withKernels. AISTATS, 2018.
61. Yamada, M., Lian, W, Goyal, Amit, Chen, J. Wimalawarne, K., Khan, S. A.,
Kaski, S., Mamitsuka, H., Chang, Y, Convex Facotrization Machine for Toxicogenomics Prediction. KDD, 2017.
62. Yamada, M., Takeuchi, K., Iwata, T., Shawe-Taylor, J., and Kaski, S., Localized
Lasso for High-dimensional Regression, AISTATS, 2017.
63. Iwata, T., Yamada, M. Multi-view Anomaly Detection via Robust Probabilistic
Latent Variable Models. NIPS, 2016.
64. Kozareva, Z. Yamada, M. Which Tumblr Post Should I Read Next?, ACL, 2016.
65. Chang, Y., Tang, J., Yin, D., Yamada, M., Liu, Y. Timeline Summarization with
Publications Life Cycle Models, IJCAI, 2016.
66. Gao, J., Yamada, M., Kaski, S., Mamitsuka, H., Zhu, S. A Robust Convex Formulation for Ensemble Clustering, IJCAI, 2016.
67. Wang, Y., Yin, D., Luo, J., Wang, P., Yamada, M., Chang, Y., Mei, Q. Beyond Ranking: Optimizing Whole-Page Presentation. WSDM, 2016. (Best Paper
Award).
68. Gunasekar, S., Yamada, M., Yin, D., and Chang, Y., Consistent Collective Matrix
Completion under Joint Low Rank Structure, AISTATS, 2015.
69. Chang, Y. , Yamada, M., Ortega, A., and Liu, Y., Ups and Downs in Buzzes:
Life Cycle Modeling for Temporal Pattern Discovery, ICDM, 2014.
70. Marcos, A. M., Yamada, M., Kimura, A., and Iwata, T., Clustering-Based Anomaly
Detection in Multi-View Data , CIKM, 2013.
71. Kimura, A., Ishiguro, K., Marcos, A. M., Kataoka, K., Murasaki, K., and Yamada,M., Image context discovery from socially curated contents, ACMMM, 2013.
72. Yamada, M., Kimura, A., Naya, F., and Sawada, H., Change-Point Detection
with Feature Selection in High-Dimensional Time-Series Data, IJCAI, 2013.
73. Liu, S., Yamada, M., Colliear, N., and Sugiyama, M., Change-Point Detection in
Time-Series Data by Relative Density-Ratio Estimation, SPR, 2012.
74. Yamada, M., Sigal, L., and Raptis, M., No Bias Left Behind: Covariate Shift
Adaptation for Discriminative 3D Pose Estimation, ECCV, 2012.
75. Niu, G., Dai, B., Yamada, M., and Sugiyama, M., Information-theoretic Semisupervised Metric Learning via Entropy Regularization, ICML, 2012.

Makoto Yamada

76. Sugiyama, M., Hachiya, H., Yamada, M., Simm, J., and Nam, H., Least-squares
probabilistic classifier: A computationally efficient alternative to kernel logistic regression, IWSML, 2012.
77. Yamada, M., Suzuki, T., Kanamori, T., Hachiya, H., and Sugiyama, M., Relative
Density-Ratio Estimation for Robust Distribution Comparison, NIPS, 2011.
78. Yamada, M., Niu, G., Takagi, J., and Sugiyama, M., Computationally Efficient Sufficient Dimension Reduction via Squared-Loss Mutual Information, ACML,
2011.
79. Yamada, M. and Sugiyama, M., Direct Density-Ratio Estimation with Dimensionality Reduction via Hetero-Distributional Subspace Analysis, AAAI, 2011.
80. Sugiyama, M., Yamada, M., Kimura, M., and Hachiya, H., On InformationMaximization Clustering: Tuning Parameter Selection and Analytic Solution, ICML,
2011.
81. Yamada, M. and Sugiyama, M., Cross-Domain Object Matching with Model Selection, AISTATS, 2011.
82. Takagi, J., Ohishi, Y., Kimura, A., Sugiyama, M., Yamada, M., and Kameoka,
H.,Automatic Audio Tag Classification via Semi-Supervised Canonical Density Estimation, ICASSP, 2011.
83. Yamada, M. and Sugiyama, M., Dependence Minimizing Regression with Model
Selection for Non-Linear Causal Inference under Non-Gaussian Noise, AAAI, 2010.
84. Wichern, G., Yamada, M., Thornburg, H., Sugiyama, M. and Spanias, A., Automatic Audio Tagging using Covariate Shift Adaptation, ICASSP, 2010.
85. Yamada, M., Sugiyama, M., and Wichern, G., Direct Importance Estimation with
Probabilistic Principal Component Analyzers, ICASSP, 2010.
86. Yamada, M., Sugiyama, M., Wichern, G., and Matsui, T., Acceleration of Sequence Kernel Computation for Real-time Speaker Identification, ICASSP, 2010.
87. Kondo, K., Yamada, M., and Kenmochi, H., A Semi-blind Source Separation
Method with A Less Amount of Computation Suitable for Tiny DSP Modules,
Interspeech, 2009.
88. Yamada, M., Sugiyama, M., and Matsui, T., Covariate shift adaptation for semisupervised speaker identification, ICASSP, 2009.
89. Yamada, M. and Azimi-Sadjadi, M. R., Kernel Wiener Filter with Distance Constraint, ICASSP, 2006.
90. Yamada, M. and Azimi-Sadjadi, M. R., Nonlinear signal estimation using kernel
Wiener filter in Canonical Correlation Analysis Framework, CIMCA, 2005.
91. Yamada, M., Azimi-Sadjadi, M. R., and Cartmill, J., Buried Underwater Target Classification Using the New BOSS and Canonical Coordinate Decomposition
Feature Extraction, MTS/IEEE Oceans Conference, 2005.
92. Yamada, M., Pezeshki, A., and Azimi-Sadjadi, M. R., Relation between KCCA
and KFDA, IJCNN, 2005.
93. Yamada, M. and Azimi-Sadjadi, M. R., Kernel Wiener Filter using CanonicalCorrelation Analysis Framework, SSP, 2005.
 Pre-print
94. Sato, R., Yamada, M, Kashima, H. Poincare: Recommending Publication Venues
via Treatment Effect Estimation.
95. Sato, R., Yamada, M, Kashima, H. Re-evaluating Word Movers Distance.
96. Takezawa, Y., Sato, R., Kozareva, Z., Ravi, S., Yamada, M. Fixed Support TreeSliced Wasserstein Barycenter

Makoto Yamada

97. Petrovich, M., Liang, C., Liu, Y., Tsai, Y-H-H, Zhu, L., Yang, Y., Salakhutdinov,R., Yamada, M. Feature Robust Optimal Transport for High-dimensional Data.
98. Ziyin, L., Wang, Z., Yamada, M., Ueda, M. Volumization as a Natural Generalization of Weight Decay.
99. Petrovich, M., Yamada, M. Fast local linear regression with anchor regularization.
100. Sato, R., Cuturi, M., Yamada, M., Kashima, H. Fast and Robust Comparison of
Probability Measures in Heterogeneous Spaces.
101. Sato, R., Yamada, M., Kashima, H. Constant time graph neural networks.
102. Singh, D., Yamada, M. FsNet: Feature Selection Network on High-dimensional
Biological Data.
103. Huang, Q., Yamada, M., Tian, Y., Singh, D., Yin, D., Chang, Y. GraphLIME:
Local Interpretable Model Explanations for Graph Neural Networks.
104. Le, T., Huynh, V., Ho, N., Phung, D., Yamada, M. Tree-Wasserstein Barycenter
for Large-Scale Multilevel Clustering and Scalable Bayes.
Professional
Activities

1. Workflow chair AISTATS 2019
2. Publicity chair WSDM 2018
3. Senior Meta-reviewer IJCAI 2020, 2021, AAAI 2021
4. Meta-reviewer (Senior PC) NeurIPS 2020,2021, ICML 2019-2021, ICLR 2021, AAAI
2020, WWW 2020, WSDM 2018-2020,2022, IJCAI 2018, ACML 2019,2020, AIRS2016
5. Program Committee, WWW 2015-2018, WSDM 2017, AISTATS 2014-2017, ICML
2017, ICANN 2011, IJCAI 2011-15, 2017, AAAI 2017, ACML 2010-2015, 2017,
ECML 2016-2017, SIGIR 2016-2017, SDM 2017

Honors andAwards

1. IEICE TC-IBISML Research Award 2019
2. Outstannding SPC award, ACM International Conference on Web Search and Data
Mining (WSDM 2020)
3. Outstannding SPC award, ACM International Conference on Web Search and Data
Mining (WSDM 2019)
4. Best paper award, ACM International Conference on Web Search and Data Mining(WSDM 2016)
5. Yahoo Labs Excellence Award, 2014
6. Interactive Presentation Award, Meeting on Image Recognition and Undestanding(MIRU2013)
7. IBISML Award Finalist in 2012, IEICE, Information-Based Induction Sciences andMachine Learning (IBISML) Technical Group
8. Honorable mention, Information-Based Induction Sciences (IBIS 2010)
9. Student Travel Grant, International Conference on Acoustics, Speech, and Signal
Processing (ICASSP 2009)

Honors andAwards(Students)

1. Tatsuya Shiraishi (JSAI Annual Conference Student Incentive Award 2019)
2. Ayato Toyokuni (JSAI Annual Conference Student Incentive Award 2020)
3. Ryoma Sato (IEICE TC-IBISML Research Award 2019)

Makoto Yamada

Research
Grant

1. 2021 - 2025, Grant-in-Aid for Young Scientists (A), Japan Ministry of Education,
Culture, Sports, Science and Technology. =Y 4,000,000 for four years. (Co-PI)
2. 2021 - 2023, Marie Curie Individual Fellowship 2021 (Research host of Dr. Marco
Fiorucci), e92,380.80
3. 2020 - 2024, Grant-in-Aid for Young Scientists (B), Japan Ministry of Education,
Culture, Sports, Science and Technology. =Y 13,600,000 for four years. (PI)
4. 2021, CyberAgent, =Y 3,500,000 (Co-PI)
5. 2020, CyberAgent, =Y 3,500,000 (Co-PI)
6. 2019, CyberAgent, =Y 1,500,000 (Co-PI)
7. 2018 - 2021, RIKEN Engineering network =Y 6,000,000 for three years. (Co-PI)
8. 2018 - 2021 Grant-in-Aid for Young Scientists (S), Japan Ministry of Education,
Culture, Sports, Science and Technology. =Y 6,000,000 for three years. (Co-PI)
9. 2016 - 2020, PRESTO (Synthesis of Knowledge for Information Oriented Society),
Japan Science and Technology Agency. =Y 35,600,000 for 3.5 years. (PI)
10. 2016 - 2018, Grant-in-Aid for Young Scientists (B), Japan Ministry of Education,
Culture, Sports, Science and Technology. =Y 1,950,000 for two years. (PI)Advising
and Collaborating
Students

Postdoc, technical staff
1. Peter Naylor (2021-)
2. Héctor Climente (2020-)
3. Jun Lu (2018)
4. Dinesh Singh (2018-)
5. Tam Le (2017-)
Students at Kyoto University
1. Yuki Takezawa (B.S.) ICML 2021
2. Yuki Tomimura (B.S.)3. Kohei Morita (B.S.)
4. Hiroaki Yamada (B.S.) NeurIPS 2021
5. Ayato Toyokuni (B.S.) EACL SRW 2021
6. Noeru Suzuki (B.S.)7. Akito Seki (B.S., M.S.)
8. Kanata Satake (B.S., M.S.)9. Tatsuya Shiraishi (B.S., M.S.) ECAI 2020
10. Ryoma Sato (B.S., M.S.) (Co-supervision with Prof. Kashima) NeurIPS 2019,2020, ICML 2021
Intern students
1. Tobias Freidling (2020, M.S. student Technical University of Munich. now Ph.D.
student University of Cambridge) ICML 2021
2. Chao Liang (2020, Ph.D. student Zhejiang University)
3. Mathis Petrovich (2019-2020, M.S. student ENS, now Ph.D. student at Ecole desPonts Paristech)
4. Qiang Huang (2019, M.S. student Jilin University), AAAI 2020
5. Shaoshen Wang (2019, Ph.D. student University of Technology Sydney)

Makoto Yamada

6. Jenning Lim (2019, M.S. student UCL, now Ph.D. student at University of Warwick), NeurIPS 2019, AISTATS 2020
7. Hirofumi Ohta (Ph.D. student at Rutgers University), ICLR 2019
8. Yanbin Liu (2019, Ph.D. student University of Technology Sydney), CVPR 2020,
ECML 2021
9. Ankesh Gupta (2019, undergraduate student at IIT Delhi)
10. Héctor Climente (2018, Ph.D. student Mines ParisTech), ISMB 2019
11. Anuj Dhawan (2018, undergraduate student at IIT Delhi)
12. Ziyin Liu (2018, undergraduate student at CMU, now Ph.D. student at Universityof Tokyo)
13. Jun Lu (2018, Technical staff)
14. Rui Zhang (2018, Ph.D. student Georgia Institute of Technology)
15. Liyan Xie (2018, Ph.D. student Georgia Institute of Technology)
16. Yu Saito (University of Tokyo, now postdoctoral researcher at UCSB)
17. Yao-Hung Hubert Tsai (2017, Ph.D. student at CMU), ICLR 2019, EMNLP 201918. Nataliya Polyakovska (Undergraduate student at Kharkiv National University) (Cosupervised by Max)
19. Tanmoy Mukherjee (2017, Ph.D. student at University of Edinburgh), EMNLP 201820. Denny Wu (2017, student at CMU, now Ph.D. student at Univeristy of Toronto)
ICLR 201921. Junning Gao (2015, Ph.D. student at Fudan University), IJCAI 2016
22. Wenzhao Lian (2015, Ph.D. student at Duke University, now Google X), KDD 201723. Nurjahan Begum (2015, Ph.D. student at University of California Riverside)
24. Suriya Gunasekar (2014, Ph.D. student at University of Texas Austin, now Seniorresearcher MSR redmond), AISTATS 2015
25. Yue Wang (2014, Ph.D. student at University of Michigan, Ann Arbor, now assistnat professor at University of North Carolina), WSDM 2016
26. Alejandro Marcos Alvarez (2013, Ph.D. student at Universite de Liege), CIKM 2013