On the Minimum l1-Norm Signal Recovery in Underdetermined Source Separation. - Ichigaku Takigawa; Mineichi Kudo; Atsuyoshi Nakamura; Jun Toyama (ICA, 2004)

Projection Learning Based Kernel Machine Design Using Series of Monotone Increasing Reproducing Kernel Hilbert Spaces. - Akira Tanaka; Ichigaku Takigawa; Hideyuki Imai; Mineichi Kudo; Masaaki Miyakoshi (KES, 2004)

Performance analysis of minimum ℓ1-norm solutions for underdetermined source separation. - Ichigaku Takigawa; Mineichi Kudo; Jun Toyama (IEEE Trans. Signal Process., 2004)

The Convex Subclass Method: Combinatorial Classifier Based on a Family of Convex Sets. - Ichigaku Takigawa; Mineichi Kudo; Atsuyoshi Nakamura (MLDM, 2005)

Combining Vector-Space and Word-Based Aspect Models for Passage Retrieval. - Raymond Wan 001; Ichigaku Takigawa; Hiroshi Mamitsuka; Vo Ngoc Anh (TREC, 2006)

Applying Gaussian Distribution-Dependent Criteria to Decision Trees for High-Dimensional Microarray Data. - Raymond Wan 001; Ichigaku Takigawa; Hiroshi Mamitsuka (VDMB, 2006)

A Probabilistic Model for Clustering Text Documents with Multiple Fields. - Shanfeng Zhu; Ichigaku Takigawa; Shuqin Zhang; Hiroshi Mamitsuka (ECIR, 2007)

Annotating gene function by combining expression data with a modular gene network. - Motoki Shiga; Ichigaku Takigawa; Hiroshi Mamitsuka (ISMB/ECCB (Supplement of Bioinformatics), 2007)

A spectral clustering approach to optimally combining numericalvectors with a modular network. - Motoki Shiga; Ichigaku Takigawa; Hiroshi Mamitsuka (KDD, 2007)

Mining significant tree patterns in carbohydrate sugar chains. - Kosuke Hashimoto; Ichigaku Takigawa; Motoki Shiga; Minoru Kanehisa; Hiroshi Mamitsuka (ECCB, 2008)

Classification by reflective convex hulls. - Mineichi Kudo; Atsuyoshi Nakamura; Ichigaku Takigawa (ICPR, 2008)

Probabilistic path ranking based on adjacent pairwise coexpression for metabolic transcripts analysis. - Ichigaku Takigawa; Hiroshi Mamitsuka (Bioinform., 2008)

Efficiently finding genome-wide three-way gene interactions from transcript- and genotype-data. - Mitsunori Kayano; Ichigaku Takigawa; Motoki Shiga; Koji Tsuda; Hiroshi Mamitsuka (Bioinform., 2009)

Convex sets as prototypes for classifying patterns. - Ichigaku Takigawa; Mineichi Kudo; Atsuyoshi Nakamura (Eng. Appl. Artif. Intell., 2009)

Field independent probabilistic model for clustering multi-field documents. - Shanfeng Zhu; Ichigaku Takigawa; Jia Zeng; Hiroshi Mamitsuka (Inf. Process. Manag., 2009)

Algorithms for Finding a Minimum Repetition Representation of a String. - Atsuyoshi Nakamura; Tomoya Saito; Ichigaku Takigawa; Hiroshi Mamitsuka; Mineichi Kudo (SPIRE, 2010)

Mining metabolic pathways through gene expression. - Timothy Hancock; Ichigaku Takigawa; Hiroshi Mamitsuka (Bioinform., 2010)

Efficiently mining δ-tolerance closed frequent subgraphs. - Ichigaku Takigawa; Hiroshi Mamitsuka (Mach. Learn., 2011)

A spectral approach to clustering numerical vectors as nodes in a network. - Motoki Shiga; Ichigaku Takigawa; Hiroshi Mamitsuka (Pattern Recognit., 2011)

Extended Analyses for an Optimal Kernel in a Class of Kernels with an Invariant Metric. - Akira Tanaka; Ichigaku Takigawa; Hideyuki Imai; Mineichi Kudo (SSPR/SPR, 2012)

Fast algorithms for finding a minimum repetition representation of strings and trees. - Atsuyoshi Nakamura; Tomoya Saito; Ichigaku Takigawa; Mineichi Kudo; Hiroshi Mamitsuka (Discret. Appl. Math., 2013)

Theoretical Analyses on Ensemble and Multiple Kernel Regressors. - Akira Tanaka; Ichigaku Takigawa; Hideyuki Imai; Mineichi Kudo (ACML, 2014)

Analyses on Generalization Error of Ensemble Kernel Regressors. - Akira Tanaka; Ichigaku Takigawa; Hideyuki Imai; Mineichi Kudo (S+SSPR, 2014)

Similarity-based machine learning methods for predicting drug-target interactions: a brief review. - Hao Ding; Ichigaku Takigawa; Hiroshi Mamitsuka; Shanfeng Zhu (Briefings Bioinform., 2014)

Community Change Detection in Dynamic Networks in Noisy Environment. - Sadamori Koujaku; Mineichi Kudo; Ichigaku Takigawa; Hideyuki Imai (WWW (Companion Volume), 2015)

Ensemble and Multiple Kernel Regressors: Which Is Better? - Akira Tanaka; Hirofumi Takebayashi; Ichigaku Takigawa; Hideyuki Imai; Mineichi Kudo (IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2015)

Reducing Redundancy with Unit Merging for Self-constructive Normalized Gaussian Networks. - Jana Backhus; Ichigaku Takigawa; Hideyuki Imai; Mineichi Kudo; Masanori Sugimoto (ICANN (1), 2016)

Online EM for the Normalized Gaussian Network with Weight-Time-Dependent Updates. - Jana Backhus; Ichigaku Takigawa; Hideyuki Imai; Mineichi Kudo; Masanori Sugimoto (ICONIP (4), 2016)

Mining approximate patterns with frequent locally optimal occurrences. - Atsuyoshi Nakamura; Ichigaku Takigawa; Hisashi Tosaka; Mineichi Kudo; Hiroshi Mamitsuka (Discret. Appl. Math., 2016)

Dense core model for cohesive subgraph discovery. - Sadamori Koujaku; Ichigaku Takigawa; Mineichi Kudo; Hideyuki Imai (Soc. Networks, 2016)

Exploring phenotype patterns of breast cancer within somatic mutations: a modicum in the intrinsic code. - Sohiya Yotsukura; Masayuki Karasuyama; Ichigaku Takigawa; Hiroshi Mamitsuka (Briefings Bioinform., 2017)

An Online Self-Constructive Normalized Gaussian Network with Localized Forgetting. - Jana Backhus; Ichigaku Takigawa; Hideyuki Imai; Mineichi Kudo; Masanori Sugimoto (IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2017)

Generalized Sparse Learning of Linear Models Over the Complete Subgraph Feature Set. - Ichigaku Takigawa; Hiroshi Mamitsuka (IEEE Trans. Pattern Anal. Mach. Intell., 2017)

FPGA-Based QBoost with Large-Scale Annealing Processor and Accelerated Hyperparameter Search. - Takashi Takemoto; Normann Mertig; Masato Hayashi; Saki Susa-Tanaka; Hiroshi Teramoto; Atsuyoshi Nakamura; Ichigaku Takigawa; Shin-ichi Minato; Tamiki Komatsuzaki; Masanao Yamaoka (ReConFig, 2018)

Graph Minors from Simulated Annealing for Annealing Machines with Sparse Connectivity. - Yuya Sugie; Yuki Yoshida 002; Normann Mertig; Takashi Takemoto; Hiroshi Teramoto; Atsuyoshi Nakamura; Ichigaku Takigawa; Shin-ichi Minato; Masanao Yamaoka; Tamiki Komatsuzaki (TPNC, 2018)

Jointly learning relevant subgraph patterns and nonlinear models of their indicators. - Ryo Shirakawa; Yusei Yokoyama; Fumiya Okazaki; Ichigaku Takigawa (CoRR, 2018)

Dual Convolutional Neural Network for Graph of Graphs Link Prediction. - Shonosuke Harada; Hirotaka Akita; Masashi Tsubaki; Yukino Baba; Ichigaku Takigawa; Yoshihiro Yamanishi; Hisashi Kashima (CoRR, 2018)

Learning Relevant Molecular Representations via Self-Attentive Graph Neural Networks. - Shoma Kikuchi; Ichigaku Takigawa; Satoshi Oyama; Masahito Kurihara (IEEE BigData, 2019)

Efficiently Enumerating Substrings with Statistically Significant Frequencies of Locally Optimal Occurrences in Gigantic String. - Atsuyoshi Nakamura; Ichigaku Takigawa; Hiroshi Mamitsuka (AAAI, 2020)

Dual graph convolutional neural network for predicting chemical networks. - Shonosuke Harada; Hirotaka Akita; Masashi Tsubaki; Yukino Baba; Ichigaku Takigawa; Yoshihiro Yamanishi; Hisashi Kashima (BMC Bioinform., 2020)

Fast improvement of TEM image with low-dose electrons by deep learning. - Hiroyasu Katsuno; Yuki Kimura; Tomoya Yamazaki; Ichigaku Takigawa (CoRR, 2021)

Minor-embedding heuristics for large-scale annealing processors with sparse hardware graphs of up to 102, 400 nodes. - Yuya Sugie; Yuki Yoshida 002; Normann Mertig; Takashi Takemoto; Hiroshi Teramoto; Atsuyoshi Nakamura; Ichigaku Takigawa; Shin-ichi Minato; Masanao Yamaoka; Tamiki Komatsuzaki (Soft Comput., 2021)

Interval-Memoized Backtracking on ZDDs for Fast Enumeration of All Lower Cost Solutions. - Shin-ichi Minato; Mutsunori Banbara; Takashi Horiyama; Jun Kawahara; Ichigaku Takigawa; Yutaro Yamaguchi 001 (CoRR, 2022)

Machine learning refinement of in situ images acquired by low electron dose LC-TEM. - Hiroyasu Katsuno; Yuki Kimura; Tomoya Yamazaki; Ichigaku Takigawa (CoRR, 2023)

C2Explainer: Customizable Mask-based Counterfactual Explanation for Graph Neural Networks. - Jiali Ma; Ichigaku Takigawa; Akihiro Yamamoto (FAccT, 2025)