Mahito Sugiyama

According to our database, Mahito Sugiyama authored at least 58 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

How graph features from message passing affect graph classification and regression?

Masatsugu Yamada, Mahito Sugiyama

Intell. Data Anal., 2024

Pseudo-Non-Linear Data Augmentation via Energy Minimization.

Pingbang Hu, Mahito Sugiyama

CoRR, 2024

Linear Mode Connectivity in Differentiable Tree Ensembles.

Ryuichi Kanoh, Mahito Sugiyama

CoRR, 2024

StiefelGen: A Simple, Model Agnostic Approach for Time Series Data Augmentation over Riemannian Manifolds.

Prasad Cheema, Mahito Sugiyama

CoRR, 2024

Bringing Structure to Naturalness: On the Naturalness of ASTs.

Profir-Petru Pârtachi, Mahito Sugiyama

Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings, 2024

Neural Tangent Kernels for Axis-Aligned Tree Ensembles.

Ryuichi Kanoh, Mahito Sugiyama

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Molecular Graph Generation by Decomposition and Reassembling.

Masatsugu Yamada, Mahito Sugiyama

CoRR, 2023

Unsupervised Tensor Based Feature Extraction From Multivariate Time Series.

Kiyotaka Matsue, Mahito Sugiyama

IEEE Access, 2023

Many-body Approximation for Non-negative Tensors.

Kazu Ghalamkari, Mahito Sugiyama, Yoshinobu Kawahara

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Analyzing Tree Architectures in Ensembles via Neural Tangent Kernel.

Ryuichi Kanoh, Mahito Sugiyama

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Unsupervised feature extraction from multivariate time series for outlier detection.

Kiyotaka Matsue, Mahito Sugiyama

Intell. Data Anal., 2022

Many-Body Approximation for Tensors.

Kazu Ghalamkari, Mahito Sugiyama

CoRR, 2022

A Neural Tangent Kernel Formula for Ensembles of Soft Trees with Arbitrary Architectures.

Ryuichi Kanoh, Mahito Sugiyama

CoRR, 2022

A Neural Tangent Kernel Perspective of Infinite Tree Ensembles.

Ryuichi Kanoh, Mahito Sugiyama

Proceedings of the Tenth International Conference on Learning Representations, 2022

Fast Rank-1 NMF for Missing Data with KL Divergence.

Kazu Ghalamkari, Mahito Sugiyama

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Unintended Effects on Adaptive Learning Rate for Training Neural Network with Output Scale Change.

Ryuichi Kanoh, Mahito Sugiyama

CoRR, 2021

A Closed Form Solution to Best Rank-1 Tensor Approximation via KL divergence Minimization.

Kazu Ghalamkari, Mahito Sugiyama

CoRR, 2021

Hierarchical probabilistic model for blind source separation via Legendre transformation.

Simon Luo, Lamiae Azizi, Mahito Sugiyama

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Investigating Overparameterization for Non-Negative Matrix Factorization in Collaborative Filtering.

Yuhi Kawakami, Mahito Sugiyama

Proceedings of the RecSys '21: Fifteenth ACM Conference on Recommender Systems, Amsterdam, The Netherlands, 27 September 2021, 2021

Fast Tucker Rank Reduction for Non-Negative Tensors Using Mean-Field Approximation.

Kazu Ghalamkari, Mahito Sugiyama

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Unsupervised Tensor based Feature Extraction and Outlier Detection for Multivariate Time Series.

Kiyotaka Matsue, Mahito Sugiyama

Proceedings of the 8th IEEE International Conference on Data Science and Advanced Analytics, 2021

2020

Additive Poisson Process: Learning Intensity of Higher-Order Interaction in Stochastic Processes.

Simon Luo, Feng Zhou, Lamiae Azizi, Mahito Sugiyama

CoRR, 2020

Rank Reduction, Matrix Balancing, and Mean-Field Approximation on Statistical Manifold.

Kazu Ghalamkari, Mahito Sugiyama

CoRR, 2020

A Geometric Look at Double Descent Risk: Volumes, Singularities, and Distinguishabilities.

Prasad Cheema, Mahito Sugiyama

CoRR, 2020

Testing machine learning code using polyhedral region.

Md Sohel Ahmed, Fuyuki Ishikawa, Mahito Sugiyama

Proceedings of the ESEC/FSE '20: 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2020

Coordinate Descent Method for Log-linear Model on Posets.

Shota Hayashi, Mahito Sugiyama, Shin Matsushima

Proceedings of the 7th IEEE International Conference on Data Science and Advanced Analytics, 2020

2019

Summarizing significant subgraphs by probabilistic logic programming.

Elena Bellodi, Ken Satoh, Mahito Sugiyama

Intell. Data Anal., 2019

Finding Statistically Significant Interactions between Continuous Features.

Mahito Sugiyama, Karsten M. Borgwardt

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Bias-Variance Trade-Off in Hierarchical Probabilistic Models Using Higher-Order Feature Interactions.

Simon Luo, Mahito Sugiyama

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Learning Graph Representation via Formal Concept Analysis.

Yuka Yoneda, Mahito Sugiyama, Takashi Washio

CoRR, 2018

Transductive Boltzmann Machines.

Mahito Sugiyama, Koji Tsuda, Hiroyuki Nakahara

CoRR, 2018

Legendre Tensor Decomposition.

Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda

CoRR, 2018

graphkernels: R and Python packages for graph comparison.

Mahito Sugiyama, M. Elisabetta Ghisu, Felipe Llinares-López, Karsten M. Borgwardt

Bioinform., 2018

Legendre Decomposition for Tensors.

Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Significant Pattern Mining on Continuous Variables.

Mahito Sugiyama, Karsten M. Borgwardt

CoRR, 2017

Tensor Balancing on Statistical Manifold.

Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Information decomposition on structured space.

Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda

Proceedings of the IEEE International Symposium on Information Theory, 2016

2015

Genome-wide detection of intervals of genetic heterogeneity associated with complex traits.

Felipe Llinares-López, Dominik G. Grimm, Dean A. Bodenham, Udo Gieraths, Mahito Sugiyama, Beth Rowan, Karsten M. Borgwardt

Bioinform., 2015

Significant Subgraph Mining with Multiple Testing Correction.

Mahito Sugiyama, Felipe Llinares-López, Niklas Kasenburg, Karsten M. Borgwardt

Proceedings of the 2015 SIAM International Conference on Data Mining, Vancouver, BC, Canada, April 30, 2015

Halting in Random Walk Kernels.

Mahito Sugiyama, Karsten M. Borgwardt

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing.

Felipe Llinares-López, Mahito Sugiyama, Laetitia Papaxanthos, Karsten M. Borgwardt

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

2014

Identifying Higher-order Combinations of Binary Features.

Felipe Llinares, Mahito Sugiyama, Karsten M. Borgwardt

CoRR, 2014

Multi-Task Feature Selection on Multiple Networks via Maximum Flows.

Mahito Sugiyama, Chloé-Agathe Azencott, Dominik G. Grimm, Yoshinobu Kawahara, Karsten M. Borgwardt

Proceedings of the 2014 SIAM International Conference on Data Mining, 2014

Detecting Anomalous Subgraphs on Attributed Graphs via Parametric Flow.

Mahito Sugiyama, Keisuke Otaki

Proceedings of the New Frontiers in Artificial Intelligence, 2014

2013

Learning figures with the Hausdorff metric by fractals - towards computable binary classification.

Mahito Sugiyama, Eiju Hirowatari, Hideki Tsuiki, Akihiro Yamamoto

Mach. Learn., 2013

Semi-supervised learning on closed set lattices.

Mahito Sugiyama, Akihiro Yamamoto

Intell. Data Anal., 2013

Efficient network-guided multi-locus association mapping with graph cuts.

Chloé-Agathe Azencott, Dominik G. Grimm, Mahito Sugiyama, Yoshinobu Kawahara, Karsten M. Borgwardt

Bioinform., 2013

Rapid Distance-Based Outlier Detection via Sampling.

Mahito Sugiyama, Karsten M. Borgwardt

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Outliers on Concept Lattices.

Mahito Sugiyama

Proceedings of the New Frontiers in Artificial Intelligence, 2013

Measuring Statistical Dependence via the Mutual Information Dimension.

Mahito Sugiyama, Karsten M. Borgwardt

Proceedings of the IJCAI 2013, 2013

2012

Semi-Supervised Ligand Finding Using Formal Concept Analysis.

Mahito Sugiyama, Kentaro Imajo, Keisuke Otaki, Akihiro Yamamoto

Inf. Media Technol., 2012

Privacy Preserving Using Dummy Data for Set Operations in Itemset Mining Implemented with ZDDs.

Keisuke Otaki, Mahito Sugiyama, Akihiro Yamamoto

IEICE Trans. Inf. Syst., 2012

2011

The Minimum Code Length for Clustering Using the Gray Code.

Mahito Sugiyama, Akihiro Yamamoto

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2011

Discovering Ligands for TRP Ion Channels Using Formal Concept Analysis.

Mahito Sugiyama, Kentaro Imajo, Keisuke Otaki, Akihiro Yamamoto

Proceedings of the Latest Advances in Inductive Logic Programming, 2011

A Fast and Flexible Clustering Algorithm Using Binary Discretization.

Mahito Sugiyama, Akihiro Yamamoto

Proceedings of the 11th IEEE International Conference on Data Mining, 2011

Semi-supervised Learning for Mixed-Type Data via Formal Concept Analysis.

Mahito Sugiyama, Akihiro Yamamoto

Proceedings of the Conceptual Structures for Discovering Knowledge, 2011

2010

The Coding Divergence for Measuring the Complexity of Separating Two Sets.

Mahito Sugiyama, Akihiro Yamamoto

Proceedings of the 2nd Asian Conference on Machine Learning, 2010

Learning Figures with the Hausdorff Metric by Fractals.

Mahito Sugiyama, Eiju Hirowatari, Hideki Tsuiki, Akihiro Yamamoto

Proceedings of the Algorithmic Learning Theory, 21st International Conference, 2010