Publications & Awards

2022

  • A Drive-by Bridge Inspection Framework Using Non-Parametric Clusters over Projected Data Manifolds
    Cheema, P., Makki Alamdari, M., Chang, K. C., Kim, C. W., Sugiyama, M.   Mechanical Systems and Signal Processing (MSSP), 2022. (accepted)
    [BibTeX]  [Paper]  [DOI: 10.1016/j.ymssp.2022.109401]
  • A Neural Tangent Kernel Perspective of Infinite Tree Ensembles
    Kanoh, R., Sugiyama, M.   The 10th International Conference on Learning Representations (ICLR 2022), 2022.
    [BibTeX]  [arXiv]  [OpenReview]  [Poster]
  • Fast Rank-1 NMF for Missing Data with KL Divergence
    Ghalamkari, K., Sugiyama, M.   The 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022), 2022.
    [BibTeX]  [Paper]  [arXiv]  [Code]  [Poster]

2021

  • Fast Tucker Rank Reduction for Non-Negative Tensors Using Mean-Field Approximation
    Ghalamkari, K., Sugiyama, M.   Advances in Neural Information Processing Systems (NeurIPS 2021), 2021.
    [BibTeX]  [arXiv]  [Paper]  [Code]  [Slide]  [Poster]
  • Unsupervised Feature Extraction from Multivariate Time Series for Outlier Detection
    Matsue, K., Sugiyama, M.   Intelligent Data Analysis, 2021.
    [BibTeX]  [Paper]  [DOI: 10.3233/IDA-216128]
  • Investigating Overparameterization for Non-Negative Matrix Factorization in Collaborative Filtering
    Kawakami, Y., Sugiyama, M.   The 15th ACM Conference on Recommender Systems (RecSys 2021), Late-Breaking Results track, 2021.
    [BibTeX]  [Paper]  [DOI: 10.1145/3460231.3478854]
  • Unsupervised Tensor based Feature Extraction and Outlier Detection for Multivariate Time Series
    Matsue, K., Sugiyama, M.   The 8th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2021), 2021.
    [BibTeX]  [Paper]  [Code]  [DOI: 10.1109/DSAA53316.2021.9564117]
  • Job Recommendation with Career Graphs
    Tanida, H., Sugiyama, M., Shikauchi, M., Oba, S.   The 8th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2021), Industrial Track, 2021.
    [BibTeX
  • Hierarchical Probabilistic Model for Blind Source Separation via Legendre Transformation
    Luo, S., Azizi, L., Sugiyama, M.   The 37th Conference on Uncertainty in Artificial Intelligence (UAI 2021), 2021.
    [BibTeX]  [arXiv]  [Paper]

2020

  • Artificial Neural Networks Applied as Molecular Wave Function Solvers
    Peng-Jian, Y., Sugiyama, M., Tsuda, K., Yanai, T.   Journal of Chemical Theory and Computation, 2020.
    [BibTeX]  [DOI: 10.1021/acs.jctc.9b01132]
  • Testing Machine Learning Code using Polyhedral Region
    Ahmed, M. S., Ishikawa, F., Sugiyama, M.   The 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2020), Visions and Reflections Track, 2020.
    [BibTeX]  [Paper]  [DOI: 10.1145/3368089.3417043]
  • Coordinate Descent Method for Log-Linear Model on Posets
    Hayashi, S., Sugiyama, M., Matsushima, S.   The 7th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2020), 2020.
    [BibTeX]  [DOI: 10.1109/DSAA49011.2020.00022]
  • Unintended Effects on Adaptive Learning Rate for Training Neural Network with Output Scale Change
    Kanoh, R., Sugiyama, M.   NeurIPS 2020 Workshop: Beyond BackPropagation, 2020.
    [BibTeX
  • Convex Optimization for Blind Source Separation on Statistical Manifolds
    Luo, S., Azizi, L., Sugiyama, M.   NeurIPS 2020 Workshop: Differential Geometry meets Deep Learning, 2020.
    [BibTeX
  • Towards Geometric Understanding of Low-Rank Approximation
    Ghalamkari, K., Sugiyama, M.   NeurIPS 2020 Workshop: Differential Geometry meets Deep Learning, 2020.
    [BibTeX
  • Sample Space Truncation on Boltzmann Machines
    Sugiyama, M., Tsuda, K., Nakahara, H.   NeurIPS 2020 Workshop: Deep Learning through Information Geometry, 2020.
    [BibTeX
  • The Volume of Non-Restricted Boltzmann Machines and Their Double Descent Model Complexity
    Cheema, P., Sugiyama, M.   NeurIPS 2020 Workshop: Deep Learning through Information Geometry, 2020.
    (This paper won the best paper award)
    [BibTeX
  • Learning Joint Intensity in a Multivariate Poisson Process on Statistical Manifolds
    Luo, S., Zhou, F., Azizi, L., Sugiyama, M.   NeurIPS 2020 Workshop: Deep Learning through Information Geometry, 2020.
    [BibTeX
  • A Deep Architecture for Log-Linear Models
    Luo, S., Cripps, S., Sugiyama, M.   NeurIPS 2020 Workshop: Deep Learning through Information Geometry, 2020.
    [BibTeX

2019

  • Legendre Decomposition for Tensors
    Sugiyama, M., Nakahara, H., Tsuda, K.   Journal of Statistical Mechanics: Theory and Experiment, 2019.
    [BibTeX]  [Paper]  [DOI: 10.1088/1742-5468/ab3196]
  • Summarizing Significant Subgraphs by Probabilistic Logic Programming
    Bellodi, E., Sato, K., Sugiyama, M.   Intelligent Data Analysis, 2019.
    [BibTeX]  [DOI: 10.3233/IDA-184339]
  • Finding Statistically Significant Interactions between Continuous Features
    Sugiyama, M., Borgwardt, K.M.   The 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), 2019.
    [BibTeX]  [arXiv]  [Paper]  [Code]  [Slide]  [Poster]  [DOI: 10.24963/ijcai.2019/484]
  • Bias-Variance Trade-Off in Hierarchical Probabilistic Models Using Higher-Order Feature Interactions
    Luo, S., Sugiyama, M.   The 33rd AAAI Conference on Artificial Intelligence (AAAI-19), 2019.
    [BibTeX]  [arXiv]  [Paper]  [Code]  [DOI: 10.1609/aaai.v33i01.33014488]

2018

  • graphkernels: R and Python Packages for Graph Comparison
    Sugiyama, M., Ghisu, E., Llinares-López, F., Borgwardt, K.M.   Bioinformatics, 34(3), 530—532, 2018
    [BibTeX]  [Paper]  [Library (R)]  [Library (Python)]  [DOI: 10.1093/bioinformatics/btx602
  • Legendre Decomposition for Tensors
    Sugiyama, M., Nakahara, H., Tsuda, K.   Advances in Neural Information Processing Systems (NeurIPS2018), 2018.
    [BibTeX]  [arXiv]  [Paper]  [Code]  [Slide]  [Poster]  [Video]
  • Learning Graph Representation via Formal Concept Analysis
    Yoneda, Y., Sugiyama, M., Washio, T.   NeurIPS 2018 Workshop on Relational Representation Learning, 2018.
    [BibTeX]  [arXiv]
  • 2018 JSAI Incentive Award
    Yoneda, Y., Sugiyama, M., Washio, T.

2017

  • Tensor Balancing on Statistical Manifold
    Sugiyama, M., Nakahara, H., Tsuda, K.   The 34th International Conference on Machine Learning (ICML 2017), 2017.
    [BibTeX]  [arXiv]  [Paper]  [Code]  [Slide]  [Poster]  [Video]
  • Searching for Bacterial Pathogens in the Digital Ocean—Executive Summary
    Giuliano, L., Dorman, C., Bowler, C., Sugiyama, M., Vezzulli, L., Czerucka, D., Le Roux, F., D'Auria, G., Troussellier, M., Briand, F.   CIESM Workshop Monograph, 49, 5–25, 2017.
    [BibTeX]  [Paper]
  • Finding Statistically Significant Patterns from Data
    Sugiyama, M.   CIESM Workshop Monograph, 49, 53–58, 2017.
    [BibTeX
  • Pattern Mining with Statistical Significance (in Japanese)
    Sugiyama, M.   Communications of the Operations Research Society of Japan, 62(4), 226–232, 2017.
    [BibTeX]  [Paper]
  • 2017 JSAI Incentive Award
    Sugiyama, M., Nakahara, H., Tsuda, K.

© Mahito Sugiyama 2022