研究成果

2020

ジャーナル

  • Kishan, Wimalawarne, Makoto Yamada,  Hiroshi Mamitsuka.
    Scaled Coupled Norms and Coupled Higher Order Tensor Completion.
    Neural Computation (NECO), 2020.

国際会議

  • Qiang Huang, TingYu Xia, HuiYan Sun, Makoto Yamada, Yi Chang.
    Unsupervised Nonlinear Feature Selection from High-dimensional Signed Networks
    In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI), 2020.

2019

ジャーナル

  • Shonosuke Harada, Hirotaka Akita, Masashi Tsubaki, Yukino Baba, Ichigaku Takigawa, Yoshihiro Yamanishi, Hisashi Kashima. Dual Graph Convolutional Neural Network for Predicting Chemical Networks. BMC Bioinformatics (presented at GIW/ABACBS 2019), 2019.
    # 化合物ネットワーク予測のためのグラフ深層学習法を提案
  • Jiyi Li, 馬場 雪乃, 鹿島 久嗣.
    超問題:専門知識を要するクラウドソーシングタスクの回答統合法.
    日本データベース学会和文論文誌, Vol. 17-J, 2019.
    # 多数決の結果が正解とならない難しい問題に対する回答統合法を提案
  • Héctor Climente, Chloé-Agathe Azencott, Samuel Kaski and Makoto Yamada. Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional data.  Bioinformatics (presented at ISMB 2019)
    # 超高次元非線形特徴選択手法を提案, 2019.
  • Akihiro Isozaki, Hideharu Mikami, Kotaro Hiramatsu, Shinya Sakuma, Yusuke Kasai, Takanori Iino, Takashi Yamano, Atsushi Yasumoto, Yusuke Oguchi, Nobutake Suzuki, Yoshitaka Shirasaki, Taichiro Endo, Takuro Ito, Kei Hiraki, Makoto Yamada, Satoshi Matsusaka, Takeshi Hayakawa, Hideya Fukuzawa, Yutaka Yatomi, Fumihito Arai, Dino Di Carlo, Atsuhiro Nakagawa, Yu Hoshino, Yoichiroh Hosokawa, Sotaro Uemura, Takeaki Sugimura, Yasuyuki Ozeki, Nao Nitta, Keisuke Goda.
    A practical guide to intelligent image-activated cell sorting
    Nature Protocols, 2019.
    # Image-activated cell sorting (Cell, 2018)のプロトコルの詳細
  • Hirofumi Kobayashi, Cheng Lei, Yi Wu, Chun-Jung Huang, Atsushi Yasumoto,  Masahiro Jona, Wenxuan Li, Yunzhao Wu, Yaxiaer Yalikun, Yiyue Jiang, Baoshan Guo, Chia-Wei Sun, Yo Tanaka, Makoto Yamada, Yutaka Yatomif, Keisuke Goda
    Intelligent whole-blood imaging flow cytometry for simple, rapid, and cost-effective drug-susceptibility testing of leukemia
    Lab on a Chip, 2019.
    # DNNに基づいた細胞分類手法の提案

国際会議

  • Ryoma Sato, Makoto Yamada, Hisashi Kashima.
    Approximation Ratios of Graph Neural Networks for Combinatorial Problems.
    In Advances in Neural Information Processing Systems (NeurIPS), 2019.
    # Graph Neural Network と Distributed Local Algorithm の関係性を理論的に示し、より強力なGNNを提案
  • Yasutoshi Ida, Yasuhiro Fujiwara, Hisashi Kashima.
    Fast Sparse Group Lasso.
    In Advances in Neural Information Processing Systems (NeurIPS), 2019.
    # 枝刈りによる Group Lasso の高速化
  • Tam Le, Makoto Yamada, Kenji Fukumizu, Marco Cuturi.
    Tree-Sliced Variants of Wasserstein Distances.
    In Advances in Neural Information Processing Systems (NeurIPS), 2019.
    # 木構造データ間の Wasserstein距離を高速に求める方法を提案
  • Jenning Lim, Makoto Yamada, Bernhard Schoelkopf, Wittawat Jitkrittum
    Kernel Stein Tests for Multiple Model Comparison.
    In Advances in Neural Information Processing Systems (NeurIPS), 2019.
    # Selective Inference を用いた Goodness-of-fit テスト
  • Rafael Pinot, Laurent Meunier, Alexandre Araujo, Hisashi Kashima, Florian Yger, Cédric Gouy-Pailler, Jamal Atif.
    Theoretical Evidence for Adversarial Robustness Through Randomization.
    In Advances in Neural Information Processing Systems (NeurIPS), 2019.
    # モデルへの攻撃に対するランダム化による対策についての理論的考察
  • Shogo Hayashi, Yoshinobu Kawahara, Hisashi Kashima.
    Active Change-Point Detection.
    In Proceedings of the 11th Asian Conference on Machine Learning (ACML), 2019.
    # 新たな機械学習問題「能動変化検知」とその一般的解法の提案
  • Ryoma Sato, Makoto Yamada, Hisashi Kashima.
    Learning to Sample Hard Instances for Graph Algorithms.
    In Proceedings of the 11th Asian Conference on Machine Learning (ACML), 2019.
    # グラフアルゴリズムに対して難しい例を生成する方法の提案
  • Yao-Hung Hubert Tsai, Shaojie Bai, Makoto Yamada, Louis-Philippe Morency, Ruslan Salakhutdinov.
    Empirical Study of Transformer’s Attention Mechanism via the Lens of Kernel
    In Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2019.
    # カーネルを用いた新しいAttention解釈の枠組みの提案
  • Shonosuke Harada, Kazuki Taniguchi, Makoto Yamada, Hisashi Kashima.
    Context-Regularized Neural Collaborative Filtering for Game App Recommendation
    In ACM RecSys LBR track, 2019.
    # コンテキスト情報を用いたゲームアプリ推薦手法の提案
  • Daiki Tanaka, Makoto Yamada, Hisashi Kashima, Takeshi Kishikawa, Tomoyuki Haga, Takamitsu Sasaki.
    In-Vehicle Network Intrusion Detection and Explanation Using Density Ratio Estimation.
    In Proceedings of 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019.
    # 車載NWへの攻撃検知とその原因箇所特定を統計的変化検知手法によって実現
  • Daiki Tanaka, Yukino Baba, Kashima Hisashi, Yuta Okubo.
    Large-scale Driver Identification Using Automobile Driving Data.
    In Proceedings of 2019 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2019.
    # モバイルセンサーデータに基づくドライバー識別を一万人規模で検証
  • Shonosuke Harada, Kazuki Taniguchi, Makoto Yamada, Hisashi Kashima.
    In-app Purchase Prediction Using Bayesian Personalized Dwell Day Ranking.
    In Proceedings of AdKDD 2019 Workshop (AdKDD), 2019.
    # モバイルゲームアプリ内での購買行動予測に使用期間情報を利用する手法を提案
  • Kosuke Yoshimura, Tomoaki Iwase, Yukino Baba, Hisashi Kashima.
    Interdependence Model for Multi-label Classification.
    In Proceedings of the 28th International Conference on Artificial Neural Networks (ICANN), 2019.
    # マルチラベル分類問題に対する新しいモデル「相互依存モデル」を提案
  • Takeru Sunahase, Yukino Baba, Hisashi Kashima.
    Probabilistic Modeling of Peer Correction and Peer Assessment.
    In Proceedings of the 12th International Conference on Educational Data Mining (EDM), 2019.
    # MOOC等のオンライン学習環境での相互添削情報を利用した学習者の能力推定手法を提案
  • Shogo Hayashi, Akira Tanimoto, Hisashi Kashima.
    Long-Term Prediction of Small Time-Series Data Using Generalized Distillation.
    In Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), 2019.
    # 一般化蒸留に基づく時系列の長期予測手法を提案
  • Yusuke Sakata, Yukino Baba, Hisashi Kashima, Hisashi Kashima.
    CrowNN: Human-in-the-loop Network with Crowd Crowd-generated Inputs.
    In Proceedings of the 44th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019.
    # 人間が入力を生成する人間参加型ニューラルネットワークの提案
  • Makoto Yamada*, Denny Wu*, Yao-Hung Hubert Tsai, Hirofumi Ohta, Ruslan Salakhutdinov, Ichiro Takeuchi, Kenji Fukumizu (* equal contribution)
    Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator.
    In Proceedings of the 7th International Conference on Learning Representations (ICLR), 2019.
    # MMDに基づく新しい選択的推論法の提案
  • Jill-Jênn Vie, Hisashi Kashima.
    Factorization Machines for Knowledge Tracing.
    In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI), 2019.
    # Factorization Machinesに基づく学習者のパフォーマンス予測モデルを提案

国内会議・研究会


2018

ジャーナル

  • Nao Nitta, Takeaki Sugimura, Akihiro Isozaki, Hideharu Mikami, Kei Hiraki, Shinya Sakuma, Takanori Iino, Fumihito Arai, Taichiro Endo, Yasuhiro Fujiwaki, Hideya Fukuzawa, Misa Hase, Takeshi Hayakawa, Kotaro Hiramatsu, Yu Hoshino, Mary Inaba, Takuro Ito, Hiroshi Karakawa, Yusuke Kasai, Kenichi Koizumi, SangWook Lee, Cheng Lei, Ming Li, Takanori Maeno, Satoshi Matsusaka, Daichi Murakami, Atsuhiro Nakagawa, Yusuke Oguchi, Minoru Oikawa, Tadataka Ota, Kiyotaka Shiba, Hirofumi Shintaku, Yoshitaka Shirasaki, Kanako Suga, Yuta Suzuki, Nobutake Suzuki, Yo Tanaka, Hiroshi Tezuka, Chihana Toyokawa, Yaxiaer Yalikun, Makoto Yamada, Mai Yamagishi, Takashi Yamano, Atsushi Yasumoto, Yutaka Yatomi, Masayuki Yazawa, Dino Di Carlo, Yoichiroh Hosokawa, Yasuyuki Ozeki, Keisuke Goda.
    Intelligent Image-Activated Cell Sorting.
    Cell, Volume 175, ISSUE 1, P266-276.e13, September 20, 2018.
  • Cheng Lei, Hirofumi Kobayashi, Yi Wu, Ming Li, Akihiro Isozaki, Atsushi Yasumoto, Hideharu Mikami, Takuro Ito, Nao Nitta, Takeaki Sugimura, Makoto Yamada, Yutaka Yatomi, Dino Di Carlo, Yasuyuki Ozeki,Keisuke Goda.
    High-throughput imaging flow cytometry by optofluidic time-stretch microscopy.
    Nature Protocols. volume 13, pages1603–1631 (2018)
  • Heewon Park, Makoto Yamada, Seiya Imoto, Satoru Miyano.
    Robust sample-specific stability selection with effective error control.
    Journal of Computational Biology.
  • Kishan Wimarawarne, Makoto Yamada, Hiroshi Mamitsuka.
    Convex Coupled Matrix and Tensor Completion.
    Neural Computation, 2018.
  • Makoto Yamada, Jiliang Tang, Jose Lugo-Martinez, Ermin Hodzic, Raunak Shrestha,  Avishek Saha, Hua Ouyang,  Dawei Yin, Hiroshi Mamitsuka, Cenk Sahinalp, Predrag Radivojac, Philipo Menczer, Yi Chang.
    Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data.
    IEEE Transactions on Knowledge and Data Engineering (TKDE), 2018.
  • Yue Wang, Dawei Yin, Roger Jie Luo, Penguyan Wnag, Makoto Yamada, Yi Chang, Qiaozhu Mei.
    Optimizing Whole-Page Presentation for Web Search.
    ACM Transactions on the Web (TWEB), 2018.
  • Wisdom of Crowds for Synthetic Accessibility Evaluation.
    Yukino Baba, Tetsu Isomura, Hisashi Kashima.
    Journal of Molecular Graphics and Modelling, Vol.80, pp.217-223, 2018.
  • Atsuto Seko, Hiroyuki Hayashi, Hisashi Kashima, Isao Tanaka.
    Matrix- and Tensor-based Recommender Systems for the Discovery of Currently Unknown Inorganic Compounds.
    Physical Review Materials, Vol.2, No.1, 2018.
  • Takuya Kuwahara, Yukino Baba, Hisashi Kashima, Takeshi Kishikawa, Junichi Tsurumi, Tomoyuki Haga, Yoshihiro Ujiie, Takamitsu Sasaki, Hideki Matsushima.
    Supervised and Unsupervised Intrusion Detection Based on CAN Message Frequencies for In-Vehicle Network.
    Journal of Information Processing, 2018.

国際会議

  • Tam Le, Makoto Yamada.
    Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams.
    In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS 2018).
  • Tanmoy Mukherjee, Makoto Yamada, Timothy Hospedales.
    Learning UnsupervisedWord TranslationsWithout Adversaries. 2018
    In Proceedings of the conference on Empirical Methods in Natural Language Processing (EMNLP), 2018.
  • Kosuke Kikui, Yuta Itoh, Makoto Yamada, Yuta Sugiura, Maki Sugimoto.
    Intra-/Inter-user Adaptation Framework for Wearable Gesture Sensing Device.
    In Proceedings of the International Symposium on Wearable Computers (ISWC) 2018.
  • Hirotaka Akita, Kosuke Nakago, Tomoki Komatsu, Yohei Sugawara, Shin-ichi Maeda, Yukino Baba, Hisashi Kashima.
    BayesGrad: Explaining Predictions of Graph Convolutional Networks.
    In Proceedings of the 25th International Conference on Neural Information Processing (ICONIP), 2018.
  • Ryoma Sato, Takehiro Yamamoto, Hisashi Kashima.
    Short-term Precipitation Prediction with Skip-connected PredNet.
    In Proceedings of the 27th International Conference on Artificial Neural Networks (ICANN), 2018.
  • Jiyi Li, Hisashi Kashima.
    Incorporating Worker Similarity for Label Aggregation in Crowdsourcing.
    In Proceedings of the 27th International Conference on Artificial Neural Networks (ICANN), 2018.
  • Makoto Yamada, Yuta Umezu, Kenji Fukumizu, Ichiro Takeuchi.
    Post Selection Inference with Kernels.
    In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.
  • Jiyi Li, Yukino Baba, Hisashi Kashima.
    Simultaneous Clustering and Ranking from Pairwise Comparisons.
    In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), pp.XX-XX, 2018.
  • Guoxi Zhang, Tomoharu Iwata, Hisashi Kashima.
    On Reducing Dimensionality of Labeled Data Efficiently.
    In Proceedings of the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2018.
  • Takuya Kuwahara, Yukino Baba, Hisashi Kashima, Takeshi Kishikawa, Junichi Tsurumi, Tomoyuki Haga, Yoshihiro Ujiie, Takamitsu Sasaki, Hideki Matsushima.
    Payload-based Statistical Intrusion Detection for In-vehicle Networks.
    In Proceedings of the Australian Workshop on Machine Learning for Cyber-security (co-located with PAKDD 2018), 2018
  • Ryusuke Takahama, Yukino Baba, Nobuyuki Shimizu, Sumio Fujita, Hisashi Kashima.
    AdaFlock: Adaptive Feature Discovery for Human-in-the-loop Predictive Modeling.
    In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI), 2018.
  • Yukino Baba, Tomoumi Takase, Kyohei Atarashi, Satoshi Oyama, Hisashi Kashima.
    Data Analysis Competition Platform for Educational Purposes: Lessons Learned and Future Challenges.
    In Proceedings of the 8th Symposium on Educational Advances in Artificial Intelligence (EAAI), 2018.
  • Junpei Naito, Yukino Baba, Hisashi Kashima, Takenori Takaki, Takuya Funo.
    Predictive Modeling of Learning Continuation in Preschool Education Using Temporal Patterns of Development Tests
    In Proceedings of the 8th Symposium on Educational Advances in Artificial Intelligence (EAAI), 2018.

国内会議・研究会


2017

ジャーナル

国際会議

国内会議・研究会


2016

ジャーナル

国際会議


国内会議・研究会

その他


2015

ジャーナル

国際会議


国内会議・研究会

その他

2014

ジャーナル


国際会議


国内会議・研究会

その他


2014年3月以前(当研究室発足以前)の研究業績についてはこちらもご覧ください。