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ORCIDMahito Sugiyama
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2020 – today
- 2024
[j11]Masatsugu Yamada, Mahito Sugiyama:
How graph features from message passing affect graph classification and regression? Intell. Data Anal. 28(1): 57-75 (2024)- [c31]Ryuichi Kanoh, Mahito Sugiyama:
Neural Tangent Kernels for Axis-Aligned Tree Ensembles. ICML 2024 - [c30]Profir-Petru Pârtachi
, Mahito Sugiyama:
Bringing Structure to Naturalness: On the Naturalness of ASTs. ICSE Companion 2024: 378-379 - [i24]Prasad Cheema, Mahito Sugiyama:
StiefelGen: A Simple, Model Agnostic Approach for Time Series Data Augmentation over Riemannian Manifolds. CoRR abs/2402.19287 (2024) - [i23]Ryuichi Kanoh, Mahito Sugiyama:
Linear Mode Connectivity in Differentiable Tree Ensembles. CoRR abs/2405.14596 (2024) - [i22]Pingbang Hu, Mahito Sugiyama:
Pseudo-Non-Linear Data Augmentation via Energy Minimization. CoRR abs/2410.00718 (2024) - [i21]James Enouen, Mahito Sugiyama:
A Complete Decomposition of KL Error using Refined Information and Mode Interaction Selection. CoRR abs/2410.11964 (2024) - 2023
- [j10]Kiyotaka Matsue, Mahito Sugiyama:
Unsupervised Tensor Based Feature Extraction From Multivariate Time Series. IEEE Access 11: 116277-116295 (2023) - [c29]Ryuichi Kanoh, Mahito Sugiyama:
Analyzing Tree Architectures in Ensembles via Neural Tangent Kernel. ICLR 2023 - [c28]Kazu Ghalamkari, Mahito Sugiyama, Yoshinobu Kawahara:
Many-body Approximation for Non-negative Tensors. NeurIPS 2023 - [i20]Masatsugu Yamada, Mahito Sugiyama:
Molecular Graph Generation by Decomposition and Reassembling. CoRR abs/2302.00587 (2023) - 2022
- [j9]Kiyotaka Matsue, Mahito Sugiyama:
Unsupervised feature extraction from multivariate time series for outlier detection. Intell. Data Anal. 26(6): 1451-1467 (2022) - [c27]Kazu Ghalamkari, Mahito Sugiyama:
Fast Rank-1 NMF for Missing Data with KL Divergence. AISTATS 2022: 2927-2940 - [c26]Ryuichi Kanoh, Mahito Sugiyama:
A Neural Tangent Kernel Perspective of Infinite Tree Ensembles. ICLR 2022 - [i19]Ryuichi Kanoh, Mahito Sugiyama:
A Neural Tangent Kernel Formula for Ensembles of Soft Trees with Arbitrary Architectures. CoRR abs/2205.12904 (2022) - [i18]Kazu Ghalamkari, Mahito Sugiyama:
Many-Body Approximation for Tensors. CoRR abs/2209.15338 (2022) - 2021
- [c25]Kiyotaka Matsue, Mahito Sugiyama:
Unsupervised Tensor based Feature Extraction and Outlier Detection for Multivariate Time Series. DSAA 2021: 1-12 - [c24]Kazu Ghalamkari, Mahito Sugiyama:
Fast Tucker Rank Reduction for Non-Negative Tensors Using Mean-Field Approximation. NeurIPS 2021: 443-454 - [c23]Yuhi Kawakami, Mahito Sugiyama:
Investigating Overparameterization for Non-Negative Matrix Factorization in Collaborative Filtering. RecSys 2021: 685-690 - [c22]Simon Luo, Lamiae Azizi, Mahito Sugiyama:
Hierarchical probabilistic model for blind source separation via Legendre transformation. UAI 2021: 312-321 - [i17]Kazu Ghalamkari, Mahito Sugiyama:
A Closed Form Solution to Best Rank-1 Tensor Approximation via KL divergence Minimization. CoRR abs/2103.02898 (2021) - [i16]Ryuichi Kanoh, Mahito Sugiyama:
Unintended Effects on Adaptive Learning Rate for Training Neural Network with Output Scale Change. CoRR abs/2103.03466 (2021) - [i15]Ryuichi Kanoh, Mahito Sugiyama:
A Neural Tangent Kernel Perspective of Infinite Tree Ensembles. CoRR abs/2109.04983 (2021) - [i14]Kazu Ghalamkari, Mahito Sugiyama:
Fast Rank-1 NMF for Missing Data with KL Divergence. CoRR abs/2110.12595 (2021) - 2020
- [c21]Shota Hayashi, Mahito Sugiyama, Shin Matsushima:
Coordinate Descent Method for Log-linear Model on Posets. DSAA 2020: 99-108 - [c20]Md Sohel Ahmed, Fuyuki Ishikawa, Mahito Sugiyama:
Testing machine learning code using polyhedral region. ESEC/SIGSOFT FSE 2020: 1533-1536 - [i13]Prasad Cheema, Mahito Sugiyama:
A Geometric Look at Double Descent Risk: Volumes, Singularities, and Distinguishabilities. CoRR abs/2006.04366 (2020) - [i12]Kazu Ghalamkari, Mahito Sugiyama:
Rank Reduction, Matrix Balancing, and Mean-Field Approximation on Statistical Manifold. CoRR abs/2006.05321 (2020) - [i11]Simon Luo, Feng Zhou, Lamiae Azizi, Mahito Sugiyama:
Additive Poisson Process: Learning Intensity of Higher-Order Interaction in Stochastic Processes. CoRR abs/2006.08982 (2020)
2010 – 2019
- 2019
- [j8]Elena Bellodi, Ken Satoh, Mahito Sugiyama:
Summarizing significant subgraphs by probabilistic logic programming. Intell. Data Anal. 23(6): 1299-1312 (2019) - [c19]Simon Luo, Mahito Sugiyama:
Bias-Variance Trade-Off in Hierarchical Probabilistic Models Using Higher-Order Feature Interactions. AAAI 2019: 4488-4495 - [c18]Mahito Sugiyama, Karsten M. Borgwardt:
Finding Statistically Significant Interactions between Continuous Features. IJCAI 2019: 3490-3498 - [i10]Simon Luo, Mahito Sugiyama:
Bias-Variance Trade-Off in Hierarchical Probabilistic Models Using Higher-Order Feature Interactions. CoRR abs/1906.12063 (2019) - [i9]Simon Luo, Lamiae Azizi, Mahito Sugiyama:
Hierarchical Probabilistic Model for Blind Source Separation via Legendre Transformation. CoRR abs/1909.11294 (2019) - 2018
- [j7]Mahito Sugiyama, M. Elisabetta Ghisu, Felipe Llinares-López, Karsten M. Borgwardt:
graphkernels: R and Python packages for graph comparison. Bioinform. 34(3): 530-532 (2018) - [c17]Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda:
Legendre Decomposition for Tensors. NeurIPS 2018: 8825-8835 - [i8]Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda:
Legendre Tensor Decomposition. CoRR abs/1802.04502 (2018) - [i7]Mahito Sugiyama, Koji Tsuda, Hiroyuki Nakahara:
Transductive Boltzmann Machines. CoRR abs/1805.07938 (2018) - [i6]Yuka Yoneda, Mahito Sugiyama, Takashi Washio:
Learning Graph Representation via Formal Concept Analysis. CoRR abs/1812.03395 (2018) - 2017
- [c16]Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda:
Tensor Balancing on Statistical Manifold. ICML 2017: 3270-3279 - [i5]Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda:
Tensor Balancing on Statistical Manifold. CoRR abs/1702.08142 (2017) - [i4]Mahito Sugiyama, Karsten M. Borgwardt:
Significant Pattern Mining on Continuous Variables. CoRR abs/1702.08694 (2017) - 2016
- [c15]Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda:
Information decomposition on structured space. ISIT 2016: 575-579 - [i3]Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda:
Information Decomposition on Structured Space. CoRR abs/1601.05533 (2016) - 2015
- [j6]Felipe Llinares-López, Dominik G. Grimm, Dean A. Bodenham, Udo Gieraths, Mahito Sugiyama, Beth Rowan, Karsten M. Borgwardt:
Genome-wide detection of intervals of genetic heterogeneity associated with complex traits. Bioinform. 31(12): 240-249 (2015) - [c14]Felipe Llinares-López, Mahito Sugiyama, Laetitia Papaxanthos, Karsten M. Borgwardt:
Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing. KDD 2015: 725-734 - [c13]Mahito Sugiyama, Karsten M. Borgwardt:
Halting in Random Walk Kernels. NIPS 2015: 1639-1647 - [c12]Mahito Sugiyama, Felipe Llinares-López, Niklas Kasenburg, Karsten M. Borgwardt:
Significant Subgraph Mining with Multiple Testing Correction. SDM 2015: 37-45 - 2014
- [c11]Mahito Sugiyama, Keisuke Otaki:
Detecting Anomalous Subgraphs on Attributed Graphs via Parametric Flow. JSAI-isAI Workshops 2014: 340-355 - [c10]Mahito Sugiyama, Chloé-Agathe Azencott, Dominik G. Grimm, Yoshinobu Kawahara, Karsten M. Borgwardt:
Multi-Task Feature Selection on Multiple Networks via Maximum Flows. SDM 2014: 199-207 - [i2]Mahito Sugiyama, Felipe Llinares-López, Niklas Kasenburg, Karsten M. Borgwardt:
Significant Subgraph Mining with Multiple Testing Correction. CoRR abs/1407.0316 (2014) - [i1]Felipe Llinares, Mahito Sugiyama, Karsten M. Borgwardt:
Identifying Higher-order Combinations of Binary Features. CoRR abs/1407.1176 (2014) - 2013
- [j5]Chloé-Agathe Azencott, Dominik G. Grimm, Mahito Sugiyama, Yoshinobu Kawahara, Karsten M. Borgwardt:
Efficient network-guided multi-locus association mapping with graph cuts. Bioinform. 29(13): 171-179 (2013) - [j4]Mahito Sugiyama, Akihiro Yamamoto:
Semi-supervised learning on closed set lattices. Intell. Data Anal. 17(3): 399-421 (2013) - [j3]Mahito Sugiyama, Eiju Hirowatari, Hideki Tsuiki, Akihiro Yamamoto:
Learning figures with the Hausdorff metric by fractals - towards computable binary classification. Mach. Learn. 90(1): 91-126 (2013) - [c9]Mahito Sugiyama, Karsten M. Borgwardt:
Measuring Statistical Dependence via the Mutual Information Dimension. IJCAI 2013: 1692-1698 - [c8]Mahito Sugiyama:
Outliers on Concept Lattices. JSAI-isAI Workshops 2013: 352-368 - [c7]Mahito Sugiyama, Karsten M. Borgwardt:
Rapid Distance-Based Outlier Detection via Sampling. NIPS 2013: 467-475 - 2012
- [j2]Keisuke Otaki, Mahito Sugiyama, Akihiro Yamamoto:
Privacy Preserving Using Dummy Data for Set Operations in Itemset Mining Implemented with ZDDs. IEICE Trans. Inf. Syst. 95-D(12): 3017-3025 (2012) - [j1]Mahito Sugiyama, Kentaro Imajo, Keisuke Otaki, Akihiro Yamamoto:
Semi-Supervised Ligand Finding Using Formal Concept Analysis. Inf. Media Technol. 7(3): 928-937 (2012) - 2011
- [c6]Mahito Sugiyama, Akihiro Yamamoto:
Semi-supervised Learning for Mixed-Type Data via Formal Concept Analysis. ICCS 2011: 284-297 - [c5]Mahito Sugiyama, Akihiro Yamamoto:
A Fast and Flexible Clustering Algorithm Using Binary Discretization. ICDM 2011: 1212-1217 - [c4]Mahito Sugiyama, Kentaro Imajo, Keisuke Otaki, Akihiro Yamamoto:
Discovering Ligands for TRP Ion Channels Using Formal Concept Analysis. ILP (Late Breaking Papers) 2011: 53-60 - [c3]Mahito Sugiyama, Akihiro Yamamoto:
The Minimum Code Length for Clustering Using the Gray Code. ECML/PKDD (3) 2011: 365-380 - 2010
- [c2]Mahito Sugiyama, Eiju Hirowatari, Hideki Tsuiki, Akihiro Yamamoto:
Learning Figures with the Hausdorff Metric by Fractals. ALT 2010: 315-329 - [c1]Mahito Sugiyama, Akihiro Yamamoto:
The Coding Divergence for Measuring the Complexity of Separating Two Sets. ACML 2010: 127-143
Coauthor Index
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last updated on 2024-11-25 22:44 CET by the dblp team
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