K. Tsuda

发表

Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection

Gunnar Rätsch, Manfred K. Warmuth, Koji Tsuda, 2004, J. Mach. Learn. Res..

Heterogeneous Data Comparison and Gene Selection with Kernel Canonical Correlation Analysis

B. Schölkopf, Jean-Philippe Vert, K. Tsuda, 2004 .

Support vector classifier with asymetric kernel function

Koji Tsuda, K. Tsuda, 1999, The European Symposium on Artificial Neural Networks.

Marginalized kernels for biological sequences

Kiyoshi Asai, Koji Tsuda, Taishin Kin, 2002, ISMB.

Kernels for graphs

H. Kashima, K. Tsuda, Akihiro Inokuchi, 2004 .

Mining Significant Substructure Pairs for Interpreting Polypharmacology in Drug-Target Network

K. Tsuda, Hiroshi Mamitsuka, Ichigaku Takigawa, 2011, PloS one.

Discriminative Subsequence Mining for Action Classification

Sebastian Nowozin, Koji Tsuda, Gökhan H. Bakir, 2007, 2007 IEEE 11th International Conference on Computer Vision.

Accurate Splice Site Detection for Caenorhabditis elegans

B. Schölkopf, Jean-Philippe Vert, K. Tsuda, 2004 .

Kernel Methods in Computational Biology

Bernhard Schölkopf, Jean-Philippe Vert, Koji Tsuda, 2005 .

Marginalized Kernels Between Labeled Graphs

Hisashi Kashima, Koji Tsuda, Akihiro Inokuchi, 2003, ICML.

Support Vector Machine Applications in Computational Biology

Bernhard Schölkopf, Jean-Philippe Vert, Koji Tsuda, 2004 .

CellTree: an R/bioconductor package to infer the hierarchical structure of cell populations from single-cell RNA-seq data

Koji Tsuda, Hiroyuki Aburatani, David duVerle, 2016, BMC Bioinformatics.

Statistical significance of combinatorial regulations

K. Tsuda, J. Sese, A. Terada, 2013, Proceedings of the National Academy of Sciences.

A New Discriminative Kernel from Probabilistic Models

Gunnar Rätsch, Motoaki Kawanabe, Klaus-Robert Müller, 2001, Neural Computation.

Mining frequent stem patterns from unaligned RNA sequences

Taku Kudo, Kiyoshi Asai, Koji Tsuda, 2006, Bioinform..

RNAmine : Frequent Stem Pattern Miner from RNAs

Taku Kudo, K. Tsuda, K. Asai, 2006 .

gBoost: a mathematical programming approach to graph classification and regression

Sebastian Nowozin, Taku Kudo, Koji Tsuda, 2009, Machine Learning.

A Linear Programming Approach for Molecular QSAR analysis

Hiroto Saigo, K. Tsuda, G. C. Garriga, 2006 .

Support Vector Classi er with Asymmetric Kernel Functions

K. Tsuda, 1998 .

RNA inverse folding using Monte Carlo tree search

Akito Taneda, Koji Tsuda, Xiufeng Yang, 2017, BMC Bioinformatics.

Machine learning reveals orbital interaction in crystalline materials

K. Tsuda, Ichigaku Takigawa, H. Kino, 2017, 1705.01043.

Density‐Based Set Enumeration in Structured Data

K. Tsuda, Elisabeth Georgii, 2012 .

EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units

B. Schölkopf, K. Borgwardt, A. Ziegler, 2011, European Journal of Human Genetics.

Optimal Hyperplane Classier with Adaptive Norm

K. Tsuda, Mar, 1999 .

Multiple Testing Tool to Detect Combinatorial Effects in Biology.

K. Tsuda, A. Terada, 2018, Methods in molecular biology.

A Compact and Fast Index Structure for Families of Sets

K. Tsuda, J. Kawahara, Shuhei Denzumi, 2014 .

1 Protein Classification via Kernel Matrix Completion

K. Tsuda, Taishin Kin, Tsuyoshi Kato, 2022 .

Image Construction by Linear Programming

Gunnar Rätsch, Bernhard Schölkopf, Lawrence K. Saul, 2004, NIPS 2004.

Asymptotic Properties of the Fisher Kernel

Motoaki Kawanabe, Klaus-Robert Müller, Shotaro Akaho, 2004, Neural Computation.

A New Variational Framework for Rigid -

Kiyoshi Asai, Koji Tsuda, Kentaro Tomii, 2004 .

Machine Learning Predictions of Factors Affecting the Activity of Heterogeneous Metal Catalysts

K. Tsuda, Ichigaku Takigawa, K. Shimizu, 2018 .

1 Kernels for Graphs

H. Kashima, K. Tsuda, 2004 .

Fast Kernels for String and Tree Matching

Bernhard Schölkopf, Jean-Philippe Vert, Koji Tsuda, 2004 .

Advanced Application of Support Vector Machines

Bernhard Schölkopf, Jean-Philippe Vert, Koji Tsuda, 2004 .

Kernel-Based Integration of Genomic Data Using Semidefinite Programming

Bernhard Schölkopf, Jean-Philippe Vert, Koji Tsuda, 2004 .

A Primer on KernelMethods

Bernhard Schölkopf, Jean-Philippe Vert, Koji Tsuda, 2004 .

Kernels for Biological Data

Bernhard Schölkopf, Jean-Philippe Vert, Koji Tsuda, 2004 .

A Primer on Kernel Methods

Bernhard Schölkopf, Jean-Philippe Vert, Koji Tsuda, 2004 .

Subspace Information Criterion for Sparse Regressors

Masashi Sugiyama, K. Tsuda, Klaus-Robert M uller, 2001 .

A Novel Kernel Method for RNA Sequence Data Analysis 1 Marginalized Kernels for RNA Sequence Data Analysis

K. Tsuda, Taishin Kin, K. Asai, 2002 .

Subspace Classier in the Hilbert Space

K. Tsuda, 1999 .

Mining significant substructure-substructure pairs in structural associations

K. Tsuda, Ichigaku Takigawa, 2009 .

BIOINFORMATICS Enumeration of condition-dependent dense modules in protein interaction networks

K. Tsuda, T. Uno, S. Dietmann, 2009 .

Mathematical Programming Methods for Learning from Structured Data: Applications to Molecular QSAR Analysis, HIV Drug Resistance Prediction, and Image Categorisation

Takeaki Uno, Taku Kudo, Koji Tsuda, 2007 .

Machine Learning Algorithm for High-Order Interaction Modeling

Ichiro Takeuchi, Koji Tsuda, Kazuya Nakagawa, 2017 .

Bayesian Inference and Optimal Design in the Sparse Linear Model

Florian Steinke, Matthias W. Seeger, Koji Tsuda, 2007, AISTATS.

Subspace information criterion for nonquadratic regularizers-Model selection for sparse regressors

Klaus-Robert Müller, Masashi Sugiyama, Koji Tsuda, 2002, IEEE Trans. Neural Networks.

Subspace Information Criterion for Sparse Regressors

Masashi Sugiyama, K. Tsuda, Klaus-Robert M uller, 2001 .

An introduction to kernel-based learning algorithms

Gunnar Rätsch, Bernhard Schölkopf, Klaus-Robert Müller, 2001, IEEE Trans. Neural Networks.

Privacy-preserving search for chemical compound databases

Goichiro Hanaoka, Kiyoshi Asai, Kana Shimizu, 2015, BMC Bioinformatics.

Experimental design for efficient identification of gene regulatory networks using sparse Bayesian models

Florian Steinke, Matthias W. Seeger, Koji Tsuda, 2006, BMC Systems Biology.

Learning to Find Graph Pre-images

Alexander Zien, Koji Tsuda, Gökhan H. Bakir, 2004, DAGM-Symposium.

Minimizing the Cross Validation Error to Mix Kernel Matrices of Heterogeneous Biological Data

Kiyoshi Asai, Koji Tsuda, Shinsuke Uda, 2004, Neural Processing Letters.

Mining and Learning with Graphs, MLG 2007, Firence, Italy, August 1-3, 2007, Proceedings

Kristian Kersting, Paolo Frasconi, Koji Tsuda, 2007, Mining and Learning with Graphs.

Comparative Analysis of Patient-Matched PDOs Revealed a Reduction in OLFM4-Associated Clusters in Metastatic Lesions in Colorectal Cancer

K. Tsuda, Yutaka Suzuki, T. Noda, 2021, Stem cell reports.

Fast and More Powerful Selective Inference for Sparse High-order Interaction Model pdf

Ichiro Takeuchi, Koji Tsuda, Hiroyuki Hanada, 2021, AAAI.

Efficiently finding genome-wide three-way gene interactions from transcript- and genotype-data

Koji Tsuda, Ichigaku Takigawa, Hiroshi Mamitsuka, 2009, Bioinform..

Significant Pattern Mining with Confounding Variables

Koji Tsuda, Aika Terada, David duVerle, 2016, PAKDD.

Privacy-Preserving Statistical Analysis by Exact Logistic Regression

Jun Sakuma, Koji Tsuda, David duVerle, 2015, 2015 IEEE Security and Privacy Workshops.

Multi-way set enumeration in weight tensors

Bernhard Schölkopf, Koji Tsuda, Elisabeth Georgii, 2011, Machine Learning.

Machine-learning prediction of the d-band center for metals and bimetals

Koji Tsuda, Ichigaku Takigawa, Ken-ichi Shimizu, 2016 .

SCARNA: fast and accurate structural alignment of RNA sequences by matching fixed-length stem fragments

Yasuo Tabei, Kiyoshi Asai, Koji Tsuda, 2006, Bioinform..

The DICS repository: module-assisted analysis of disease-related gene lists

Koji Tsuda, Elisabeth Georgii, Alexey V. Antonov, 2009, Bioinform..

Clustering with the Fisher Score

Motoaki Kawanabe, Klaus-Robert Müller, Koji Tsuda, 2002, NIPS.

Dense module enumeration in biological networks

Koji Tsuda, Elisabeth Georgii, K. Tsuda, 2009, Methods in molecular biology.

Enumeration of condition-dependent dense modules in protein interaction networks

K. Tsuda, T. Uno, S. Dietmann, 2009, Bioinform..

Enumeration of condition-dependent dense modules in protein interaction networks

Takeaki Uno, Koji Tsuda, Elisabeth Georgii, 2009, 21st International Conference on Data Engineering Workshops (ICDEW'05).

Learning kernels from biological networks by maximizing entropy

William Stafford Noble, Koji Tsuda, K. Tsuda, 2004, ISMB/ECCB.

Protein classification via kernel matrix completion

Tsuyoshi Kato, Koji Tsuda, Taishin Kin, 2003 .

The em Algorithm for Kernel Matrix Completion with Auxiliary Data

Kiyoshi Asai, Shotaro Akaho, Koji Tsuda, 2003, J. Mach. Learn. Res..

Simultaneous inference of biological networks of multiple species from genome-wide data and evolutionary information: a semi-supervised approach

Yoshihiro Yamanishi, Masashi Sugiyama, Hisashi Kashima, 2009, Bioinform..

A Fast Method of Statistical Assessment for Combinatorial Hypotheses Based on Frequent Itemset Enumeration

Takeaki Uno, Koji Tsuda, Shin-ichi Minato, 2014, ECML/PKDD.

Fast Statistical Assessment for Combinatorial Hypotheses Based on Frequent Itemset Mining

Takeaki Uno, Koji Tsuda, Shin-ichi Minato, 2014 .

Data Mining for Biologists

Koji Tsuda, K. Tsuda, 2012, Int. J. Knowl. Discov. Bioinform..

Graph Classification Methods in Chemoinformatics

Koji Tsuda, K. Tsuda, 2011, Handbook of Statistical Bioinformatics.

Graph Classification

Koji Tsuda, Hiroto Saigo, Hiroto Saigo, 2010, Managing and Mining Graph Data.

Artificial Neural Networks Applied as Molecular Wave Function Solvers.

K. Tsuda, T. Yanai, M. Sugiyama, 2020, Journal of chemical theory and computation.

Legendre Tensor Decomposition pdf

Koji Tsuda, Hiroyuki Nakahara, Mahito Sugiyama, 2018, ArXiv.

Tensor Balancing on Statistical Manifold

Koji Tsuda, Hiroyuki Nakahara, Mahito Sugiyama, 2017, ICML.

A Quantum-Statistical-Mechanical Extension of Gaussian Mixture Model

Koji Tsuda, Kazuyuki Tanaka, Kazuyuki Tanaka, 2008 .

Site Density Functional Theory and Structural Bioinformatics Analysis of the SARS-CoV Spike Protein and hACE2 Complex

K. Tsuda, M. Valiev, Adnan Sljoka, 2022, Molecules.

PDB‐scale analysis of known and putative ligand‐binding sites with structural sketches

Yasuo Tabei, Kana Shimizu, Koji Tsuda, 2012, Proteins.

Mining expression-dependent modules in the human interaction network

Takeaki Uno, Koji Tsuda, Elisabeth Georgii, 2007, BMC Bioinformatics.

Propagating distributions on a hypergraph by dual information regularization

Koji Tsuda, K. Tsuda, 2005, ICML.

Approximating Incomplete Kernel Matrices by the em Algorithm

Kiyoshi Asai, Shotaro Akaho, Koji Tsuda, 2002, ArXiv.

PoSSuM: a database of similar protein–ligand binding and putative pockets

Yasuo Tabei, Kana Shimizu, Koji Tsuda, 2011, Nucleic Acids Res..

Legendre decomposition for tensors

Koji Tsuda, Hiroyuki Nakahara, Mahito Sugiyama, 2018, NeurIPS.

Fast Westfall-Young permutation procedure for combinatorial regulation discovery

Koji Tsuda, Jun Sese, Aika Terada, 2013, 2013 IEEE International Conference on Bioinformatics and Biomedicine.

Weighted Substructure Mining for Image Analysis

Sebastian Nowozin, Takeaki Uno, Taku Kudo, 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

Partial least squares regression for graph mining

Nicole Krämer, Koji Tsuda, Hiroto Saigo, 2008, KDD.

Entire regularization paths for graph data

Koji Tsuda, K. Tsuda, 2007, ICML '07.

SlideSort: all pairs similarity search for short reads

Kana Shimizu, Koji Tsuda, K. Tsuda, 2010, Bioinform..

Distribution Loss Minimization With Guaranteed Error Bound

Ryo Yoshinaka, Yasuhiro Hayashi, Akihiro Kishimoto, 2014, IEEE Transactions on Smart Grid.

Cartesian Kernel: An Efficient Alternative to the Pairwise Kernel

Yoshihiro Yamanishi, Hisashi Kashima, Koji Tsuda, 2010, IEICE Trans. Inf. Syst..

propagation: A fast semisupervised learning algorithm for link prediction

Yoshihiro Yamanishi, Masashi Sugiyama, Hisashi Kashima, 2009 .

Selective integration of multiple biological data for supervised network inference

Tsuyoshi Kato, Kiyoshi Asai, Koji Tsuda, 2005, Bioinform..

Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining

Ichiro Takeuchi, Masayuki Karasuyama, Shinya Suzumura, 2016, KDD.

RNA inverse folding using Monte Carlo tree search

K. Tsuda, Kazuki Yoshizoe, Xiufeng Yang, 2017, BMC Bioinformatics.

ROS-DET: robust detector of switching mechanisms in gene expression

Koji Tsuda, Ichigaku Takigawa, Hiroshi Mamitsuka, 2011, Nucleic acids research.

Machine learning reveals orbital interaction in materials

Kiyoyuki Terakura, Koji Tsuda, Ichigaku Takigawa, 2017, Science and technology of advanced materials.

Marginalized kernels for RNA sequence data analysis.

Kiyoshi Asai, Koji Tsuda, Taishin Kin, 2002, Genome informatics. International Conference on Genome Informatics.

Network-based de-noising improves prediction from microarray data

Paul Horton, Tsuyoshi Kato, Kiyoshi Asai, 2006, BMC Bioinformatics.

Discovering combinatorial interactions in survival data

Ichiro Takeuchi, Koji Tsuda, David duVerle, 2013, Bioinform..

Clustering graphs by weighted substructure mining

Taku Kudo, Koji Tsuda, Taku Kudo, 2006, ICML.

Efficient model selection for predictive pattern mining model by safe pattern pruning

K. Tsuda, H. Hanada, Takumi Yoshida, 2023, Patterns.

Selective Inference for Sparse High-Order Interaction Models

Ichiro Takeuchi, Shinya Suzumura, Koji Tsuda, 2017, ICML.

Mining complex genotypic features for predicting HIV-1 drug resistance

Takeaki Uno, Koji Tsuda, Hiroto Saigo, 2007, Bioinform..

Graph kernels for chemoinformatics

Koji Tsuda, Hisashi Kashima, Hiroto Saigo, 2010 .

Transductive Boltzmann Machines pdf

Koji Tsuda, Hiroyuki Nakahara, Mahito Sugiyama, 2018, ArXiv.

Fast Iterative Mining Using Sparsity-Inducing Loss Functions

Hisashi Kashima, Koji Tsuda, Hiroto Saigo, 2013, IEICE Trans. Inf. Syst..

Change-Point Detection using Krylov Subspace Learning

Tsuyoshi Idé, Koji Tsuda, K. Tsuda, 2007, SDM.

A Kernel for Protein Secondary Structure Prediction

Bernhard Schölkopf, Jean-Philippe Vert, Koji Tsuda, 2004 .

Fast protein classification with multiple networks

Bernhard Schölkopf, Hyunjung Shin, Koji Tsuda, 2005, ECCB/JBI.

Bayesian Inference and Optimal Design in the Sparse Linear Model

M. Seeger, K. Tsuda, Florian Steinke, 2007, AISTATS.

Yasuo Tabei, Koji Tsuda, K. Tsuda, 2011, SDM.

Feature Importance Measurement based on Decision Tree Sampling

K. Tsuda, Diptesh Das, Chao Huang, 2023, arXiv.org.

Efficient query autocompletion with edit distance-based error tolerance

K. Tsuda, Y. Ishikawa, Wei Wang, 2019, The VLDB Journal.

Modeling splicing sites with pairwise correlations

Kiyoshi Asai, Masanori Arita, Koji Tsuda, 2002, ECCB.

Reaction graph kernels predict EC numbers of unknown enzymatic reactions in plant secondary metabolism

Hisashi Kashima, Koji Tsuda, Hiroto Saigo, 2010, BMC Bioinformatics.

Inferring and visualising the hierarchical tree structure of Single-Cell RNA-seq Data data with the cellTree package

K. Tsuda, David duVerle, 2016 .

Graph Mining in Chemoinformatics

Koji Tsuda, Hiroto Saigo, Hiroto Saigo, 2010 .

Iterative Subgraph Mining for Principal Component Analysis

Koji Tsuda, Hiroto Saigo, Hiroto Saigo, 2008, 2008 Eighth IEEE International Conference on Data Mining.

Matrix Decomposition-based Dimensionality Reduction on Graph Data

Koji Tsuda, Hiroto Saigo, Hiroto Saigo, 2011, Graph Data Management.

Frequent Subgraph Retrieval in Geometric Graph Databases

Sebastian Nowozin, Koji Tsuda, S. Nowozin, 2008, 2008 Eighth IEEE International Conference on Data Mining.

Efficient Error-tolerant Query Autocompletion

Kunihiko Sadakane, Yoshiharu Ishikawa, Wei Wang, 2013, Proc. VLDB Endow..

Image reconstruction by linear programming

Gunnar Rätsch, Koji Tsuda, G. Rätsch, 2003, IEEE Transactions on Image Processing.

On a linear fused Gromov-Wasserstein distance for graph structured data

Dai Hai Nguyen, K. Tsuda, 2022, Pattern Recognit..

Learning to Predict the Leave-One-Out Error of Kernel Based Classifiers

Gunnar Rätsch, Klaus-Robert Müller, Sebastian Mika, 2001, ICANN.

Statistically significant subgraphs for genome-wide association study

Koji Tsuda, Jun Sese, Yuki Saito, 2014, SSDM@ECML/PKDD.

Machine Learning in Computational Biology

William Stafford Noble, C. Leslie, G. Rätsch, 2007, NIPS 2007.

Submodularity Cuts and Applications

Jeff A. Bilmes, Yoshinobu Kawahara, Koji Tsuda, 2009, NIPS.

Safe Feature Pruning for Sparse High-Order Interaction Models

Ichiro Takeuchi, Masayuki Karasuyama, Shinya Suzumura, 2015, 1506.08002.

Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining

S. Suzumura, I. Takeuchi, K. Tsuda, 2016, KDD.

Protein functional class prediction with a combined graph

Bernhard Schölkopf, Hyunjung Shin, Koji Tsuda, 2003, Expert Syst. Appl..

SketchSort: Fast All Pairs Similarity Search for Large Databases of Molecular Fingerprints

Yasuo Tabei, Koji Tsuda, K. Tsuda, 2011, Molecular informatics.

Controlling the False Discovery Rate of the Association/Causality Structure Learned with the PC Algorithm

Z. Jane Wang, Junning Li, Z. J. Wang, 2009, J. Mach. Learn. Res..

Oblivious Evaluation of Non-deterministic Finite Automata with Application to Privacy-Preserving Virus Genome Detection

Hiroki Arimura, Jun Sakuma, Koji Tsuda, 2014, WPES.

A Bayesian Approach to Graphy Regression with Relevant Subgraph Selection

Koji Tsuda, Hiroto Saigo, Silvia Chiappa, 2009, SDM.