研究成果

2025

Journal

  • Xiaofeng Lin, Han Bao, Yan Cui, Koh Takeuchi, Hisashi Kashima.
    Scalable Individual Treatment Effect Estimator for Large Graphs.
    Machine Learning, 2025.
    # 相互干渉する対象同士が及ぼす影響を考慮しつつ効率よく介入効果を推定する手法を提案

International Conference

  • Yasuaki Sumita, Koh Takeuchi, Hisashi Kashima.
    Cognitive Biases in Large Language Models: A Survey and Mitigation Experiments.
    In Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing (SAC), 2025.
    # 大規模言語モデル(LLM)のもつさまざまな認知バイアスのサーベイとその軽減方法の検討
  • Junki Mori, Kosuke Kihara, Taiki Miyagawa, Akinori Ebihara, Isamu Teranishi, Hisashi Kashima.
    Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Data.
    In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025.
    # 訓練済みのモデルをラベルなしデータのみを用いてドメイン適応を行う連合学習法を提案

2024

Journal

  • Hisashi Kashima, Satoshi Oyama, Hiromi Arai, Junichiro Mori.
    Trustworthy Human Computation: A Survey.
    Artificial Intelligence Review, 2024.
    # ヒューマンコンピュテーションにおける「信頼」を定義し、関連研究の網羅的調査を実施
  • 山口晃広, 植野研, 鹿島久嗣.
    長さと形を学習可能な判別波形パターン.
    電子情報通信学会論文誌, Vol.J108-D, No.05, 2024.
    # 可変長のShapelet(部分時系列)特徴量をもつ時系列分類器の学習手法を提案
  • Koji Maruhashi, Hisashi Kashima, Satoru Miyano, Heewon Park.
    Meta Graphical Lasso: Uncovering Hidden Interactions Among Latent Mechanisms.
    Scientific Reports, 2024.
    # 複数のデータセットに共通する変数間の疎構造を抽出する方法を提案
  • 吉村 皐亮, 鹿島 久嗣.
    群衆からの学習のためのラベル選択手法.
    人工知能学会論文誌, Vol.39, No.5,
    # 選択的予測の枠組みによってクラウドソーシングラベルを取捨選択しながら学習する方法を提案
  • 白上 龍, 北原 稔也, 竹内 孝, 鹿島 久嗣.
    交通理論に基づいた深層学習による渋滞長予測.
    人工知能学会論文誌, Vol.39, No.2,
    # 交通工学理論を取り入れ渋滞長予測を行う深層学習モデルを提案

International Conference

  • Yuki Takezawa, Han Bao, Ryoma Sato, Kenta Niwa, Makoto Yamada.
    Polyak Meets Parameter-free Clipped Gradient Descent.
    In Advances in Neural Information Processing Systems (NeurIPS), 2024.
    # ハイパーパラメータ調整不要のクリップ付き勾配降下法を提案
  • Tomas Rigaux, Hisashi Kashima.
    Enhancing Chess Reinforcement Learning with Graph Representation.
    In Advances in Neural Information Processing Systems (NeurIPS), 2024.
    # グラフニューラルネットワークを用いてチェスにおける強化学習の性能と汎用性を向上させた
  • Sho Yokoi, Han Bao, Hiroto Kurita, Hidetoshi Shimodaira.
    Zipfian Whitening.
    In Advances in Neural Information Processing Systems 37 (NeurIPS), 2024.
    # 単語埋め込みの従う指数型分布族の正規化を単語事前分布で重み付けすると性能向上することを示した
  • Xiaotian Lu, Jiyi Li, Koh Takeuchi, Hisashi Kashima.
    AHP-Powered LLM Reasoning for Multi-Criteria Evaluation of Open-Ended Responses.
    In Findings of the Association for Computational Linguistics: EMNLP, 2024.
    # LLMにAHP(階層分析法)使わせることで様々な意見を複数の評価軸で総合的に評価する方法を提案
  • Ryota Maruo, Hisashi Kashima.
    Efficient Preference Elicitation in Iterative Combinatorial Auctions with Many Participants.
    In Proceedings of the 25th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA), 2024.
    # 組合せオークションにおける多数の参加者の選好関数をマルチタスク学習を用いて学習
  • Shin’ya Yamaguchi.
    Analyzing Diffusion Models on Synthesizing Training Datasets.
    In Proceedings of the 16th Asian Conference on Machine Learning (ACML), 2024.
  • Akihiro Yamaguchi, Ken Ueno, Ryusei Shingaki, Hisashi Kashima.
    Learning Counterfactual Explanations with Intervals for Time-series Classification.
    In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM), 2024.
    # 時系列分類モデルの予測を解釈・説明する複数の時系列区間を提示する方法を提案
  • Shinsaku Sakaue, Han Bao, Taira Tsuchiya, Taihei Oki.
    Online Structured Prediction with Fenchel–Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss.
    In Proceedings of the 37th Annual Conference on Learning Theory (COLT), 2024.
    # ランダム復号化に基づくオンライン構造予測を提案し時間に対して定数リグレットを達成
  • Han Bao, Ryuichiro Hataya, Ryo Karakida.
    Self-attention Networks Localize When QK-eigenspectrum Concentrates.
    In Proceedings of the 41st International Conference on Machine Learning (ICML), 2024.
    # Transformer の query-key パラメータ行列の固有値分布が集中すると注意機構の分布も集中することを示した
  • Yohei Kodama, Yuki Akeyama, Yusuke Miyazaki, Koh Takeuchi.
    Travel Demand Prediction with Application to Commuter Demand Estimation on Urban Railways.
    In Proceedings of the 2024 ACM Web Conference (WWW), 2024.
    # 新たな施設の開業が旅行需要をどのように変化させるか予測する手法を提案
  • Yu Mitsuzumi, Akisato Kimura, Hisashi Kashima.
    Understanding and Improving Source-free Domain Adaptation from a Theoretical Perspective.
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
    # 既存のソースなしドメイン適応手法に対する理論的な解釈とこれに基づく改良手法を提案
  • Shin’ya Yamaguchi, Sekitoshi Kanai, Kazuki Adachi, Daiki Chijiwa.
    Adaptive Random Feature Regularization on Fine-tuning Deep Neural Networks.
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
    # 深層学習モデルのファインチューニングにおいて、適応的に更新されるランダム参照ベクトルで特徴抽出器を罰則する軽量かつ高性能な訓練手法を提案
  • Xiaotian Lu, Jiyi Li, Zhen Wan, Xiaofeng Lin, Koh Takeuchi, Hisashi Kashima.
    Evaluating Saliency Explanations in NLP by Crowdsourcing.
    In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING), 2024.
    # 自然言語処理タスクにおける説明手法の自動評価指標と人間の評価を比較
  • Yuki Wakai, Koh Takeuchi, Hisashi Kashima.
    Recovering Population Dynamics from a Single Point Cloud Snapshot.
    In Proceedings of the 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2024.
    # 点群の一時点でのスナップショットのみから、その動きを推定する問題と解法を提案
  • Lin Xiaofeng, Hisashi Kashima.
    Treatment Effect Estimation Under Unknown Interference.
    In Proceedings of the 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2024.
    # 対象の集団への介入がお互いに(未知の)干渉をする場合の介入効果推定法を提案

2023

Journal

  • Yuki Takezawa, Han Bao, Kenta Niwa, Ryoma Sato, Makoto Yamada.
    Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data.
    Transactions on Machine Learning Research, 2023.
    # 分散学習において、各クライアントの持つ訓練データの分布が異なっている場合でも高い精度を達成できる手法を提案
  • 山口晃広, 植野研, 鹿島久嗣.
    変形可能な判別波形パターンの学習法.
    電子情報通信学会論文誌, Vol.J106-D, No.05, 2023.

International Conference

  • Sho Otao, Makoto Yamada.
    A linear time approximation of Wasserstein distance with word embedding selection.
    In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023.
    # 重要な特徴グループを自動選択できる木構造ワッサースタイン距離の提案
  • Yuki Takezawa, Ryoma Sato, Han Bao, Kenta Niwa, Makoto Yamada.
    Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence.
    In Advances in Neural Information Processing Systems (NeurIPS), 2023.
    # 分散学習において通信効率と収束速度のバランスに優れたグラフを提案
  • Shin’ya Yamaguchi, Daiki Chijiwa, Sekitoshi Kanai, Atsutoshi Kumagai, Hisashi Kashima.
    Regularizing Neural Networks with Meta-Learning Generative Models.
    In Advances in Neural Information Processing Systems (NeurIPS), 2023.
    # 生成モデルをメタ最適化することで合成サンプルを分類器の学習の状況に応じて生成し特徴抽出器の学習に用いる正則化を提案
  • Shin’ya Yamaguchi.
    Generative Semi-supervised Learning with Meta-Optimized Synthetic Samples.
    In Proceedings of the 15th Asian Conference on Machine Learning (ACML), 2023.
    # 大規模基盤生成モデルをメタ最適化することで教師なし合成サンプルを学習状況に応じて生成する半教師つき学習法を提案
  • Akihiro Yamaguchi, Ken Ueno, Hisashi Kashima.
    Time-series Shapelets with Learnable Lengths.
    In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM), 2023.
    # 可変長のShapelet(部分時系列)特徴量をもつ時系列分類器の学習手法を提案
  • Kosuke Yoshimura, Hisashi Kashima.
    Label Selection Approach to Learning from Crowds.
    In Proceedings of the 30th International Conference on Neural Information Processing (ICONIP), 2023.
    # 選択的予測の枠組みによってクラウドソーシングラベルを取捨選択しながら学習する方法を提案
  • Ryuichiro Hataya, Han Bao, Hiromi Arai.
    Will Large-scale Generative Models Corrupt Future Datasets?
    In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2023.
    # 生成 AI の生成物が社会的に与える影響を検証するため、特に生成画像による下流タスク性能を評価
  • Jill-Jênn Vie, Hisashi Kashima.
    Deep Knowledge Tracing is an Implicit Dynamic Multidimensional Item Response Theory Model.
    In Proceedigs of the 31st International Conference on Computers in Education (ICCE), 2023.
    # 深層知識追跡(Deep Knowledge Tracing)のシンプルな拡張によって予測精度を向上
  • Xiaofeng Lin, Guoxi Zhang, Xiaotian Lu, Han Bao, Koh Takeuchi, Hisashi Kashima.
    Estimating Treatment Effects Under Heterogeneous Interference.
    In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2023.
    # 複数種類の関係をもつグラフ上での介入効果推定手法を提案
  • Koh Takeuchi, Ryo Nishida, Hisashi Kashima, Masaki Onishi.
    Causal Effect Estimation on Hierarchical Spatial Graph Data.
    In Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2023.
    # 階層的な空間グラフデータから空間介入効果を推定するSpatial Intervention Neural Network (SINet) を提案
  • Ryu Shirakami, Toshiya Kitahara, Koh Takeuchi, Hisashi Kashima.
    QTNet: Theory-based Queue Length Prediction for Urban Traffic.
    In Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2023.
    # 交通工学理論に基づくQueueing-theory-based Neural Network (QT-Net)を提案し、東京都交通網における渋滞長予測を高精度化
  • Han Bao.
    Proper Losses, Moduli of Convexity, and Surrogate Regret Bounds.
    In Proceedings of the 36th Conference on Learning Theory (COLT), 2023.
    # 分類等で一般的に用いられる proper loss の収束率が一般化エントロピー関数の凸度(modulus of convexity)で支配されることを証明
  • Yuki Arase, Han Bao, Sho Yokoi.
    Unbalanced Optimal Transport for Unbalanced Word Alignment.
    In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), 2023.
    # 対応なし単語組が多発する単一言語内フレーズアライメントにおいて不均衡最適輸送の振る舞いを評価
  • Ryosuke Ueda, Koh Takeuchi, Hisashi Kashima.
    Fair Opinion Aggregation for Voter Attribute Bias.
    In Proceedings of 6th AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2023.
    # 属性の偏った集団において、公平な意見統合を行う手法を提案
  • Ryoma Sato.
    Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure.
    In Proceedings of the 40th International Conference on Machine Learning (ICML), 2023.
    # 入力ノード特徴量に含まれない新しい有用なノード特徴量をグラフニューラルネットワークが自ら創出できることを証明
  • Xiaotian Lu, Jiyi Li, Koh Takeuchi, Hisashi Kashima.
    Multiview Representation Learning from Crowdsourced Triplet Comparisons.
    In Proceedings of the Web Conference (WWW), 2023.
    # クラウドソーシングで収集した類似度比較データから様々な視点での表現を獲得する手法を提案
  • Ryoma Sato.
    Active Learning from the Web.
    In Proceedings of the Web Conference (WWW), 2023.
    # ウェブ上のデータを能動学習の巨大なプールとみなしてモデル訓練に有益なデータを取得する手法を提案
  • Guoxi Zhang, Hisashi Kashima.
    Behavior Estimation from Multi-Source Data for Offline Reinforcement Learning.
    In Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI), 2023.
    # 複数の異なるポリシーから得られた混合データからのオフライン強化学習手法を提案

2022

ジャーナル

  • 原田 将之介, 鹿島 久嗣.
    GraphITE: グラフ介入に対する介入効果推定.
    人工知能学会論文誌, Vol.37, No.6, 2022.
    # 介入がグラフ構造をもつ場合の介入効果推定法を提案
  • Guoxi Zhang, Hisashi Kashima.
    Learning State Importance for Preference-based Reinforcement Learning.
    Machine Learning, 2022.
    # エピソードの一対比較からの強化学習(PbRL)において、状態の重要度を推定する方法を提案
  • Shonosuke Harada, Hisashi Kashima.
    InfoCEVAE: Treatment Effect Estimation with Hidden Confounding Variables Matching.
    Machine Learning, 2022.
    # 交絡変数が観測されない場合にこれを推定しながら介入効果推定をする方法を提案
  • Yoichi Chikahara, Shinsaku Sakaue, Akinori Fujino, Hisashi Kashima.
    Making Individually Fair Predictions with Causal Pathways.
    Data Mining and Knowledge Discovery (DAMI), 2022.
    # 「因果パスウェイ」に基づく精密な公平性をもつ予測器の学習法を提案
  • 中村 周, 竹内 孝, 鹿島 久嗣, 岸川 剛, 芳賀 智之, 佐々木 崇光.
    異なる車種をまたいだ車載ネットワークへの攻撃検知.
    人工知能学会論文誌, Vol.37, No. 5, 2022.
    # 転移学習を用いた異車種横断攻撃検知法を提案
  • Yang Liu, Hisashi Kashima.
    Chemical Property Prediction Under Experimental Biases.
    Scientific Reports, 2022.
    # 実験バイアスを含むデータからの化合物物性予測
  • Akira Tanimoto, So Yamada, Takashi Takenouchi, Masashi Sugiyama, Hisashi Kashima.
    Improving Imbalanced Classification Using Near-miss Instances.
    Expert Systems with Applications (ESWA), 2022.
    # 「ヒヤリハット」ラベル(弱い正例)を利用した判別学習
  • Ryoma Sato, Makoto Yamada, Hisashi Kashima.
    Poincare: Recommending Publication Venues via Treatment Effect Estimation.
    Journal of Informetrics, 2022.
    # ポアンカレ:著者の投稿バイアスを考慮した論文投稿先推薦システム
  • 原田 将之介, 鹿島 久嗣.
    反事実伝播: 介入効果推定のための半教師付き学習.
    人工知能学会論文誌, Vol.37, No.3, 2022.
    # 介入効果推定における半教師付き学習問題とラベル伝播法に基づく解法の提案
  • Maya Okawa, Tomoharu Iwata, Yusuke Tanaka, Hiroyuki Toda, Takeshi Kurashima, Hisashi Kashima.
    Context-aware Spatio-temporal Event Prediction via Convolutional Hawkes Processes.
    Machine Learning, 2022.
    # 深層点過程によって時空間的なイベントを予測
  • Naoya Takeishi, Keisuke Fujii, Koh Takeuchi, Yoshinobu Kawahara.
    Discriminant Dynamic Mode Decomposition for Labeled Spatio-Temporal Data Collections.
    SIAM Journal on Applied Dynamical Systems (SIADS), 2022.
    # 識別動的モード分解法を提案
  • Ryoma Sato, Makoto Yamada, Hisashi Kashima.
    Constant Time Graph Neural Networks.
    ACM Transactions on Knowledge Discovery from Data (TKDD), 2022.
    # 定数時間で計算できるグラフニューラルネットワークの提案

国際会議

  • Jill-Jênn Vie, Tomas Rigaux, Hisashi Kashima.
    Variational Factorization Machines for Preference Elicitation in Large-Scale Recommender Systems.
    In Proceedings of the 2022 IEEE International Conference on Big Data (BigData), 2022.
    # 因子分解マシンの変分推論手法の提案と推薦システムへの応用
  • Guoxi Zhang, Jiyi Li, Hisashi Kashima.
    Improving Pairwise Rank Aggregation via Querying for Rank Difference.
    In Proceedings of the 9th IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2022.
    # 差の大小関係を比較したデータを用いることで、より詳細な順序付けを学習する手法を提案
  • Ryoma Sato.
    Towards Principled User-side Recommender Systems.
    In Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM), 2022.
    # ユーザーサイドの推薦システムの理論的な実現可能性を示し、望ましい性質をもつユーザーサイドの推薦アルゴリズムを提案
  • Ryoma Sato.
    CLEAR: A Fully User-side Image Search System.
    In Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM), 2022.
    # ユーザーが設定したスコア関数を元に画像検索を行うブラウザで動作するデモを公開
  • Ryoma Sato, Makoto Yamada, Hisashi Kashima.
    Twin Papers: A Simple Framework of Causal Inference for Citations via Coupling.
    In Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM), 2022.
    # 「双子論文」に基づく因果推論を用いた研究インパクトの分析手法を提案
  • Guoxi Zhang, Hisashi Kashima.
    Batch Reinforcement Learning from Crowds.
    In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2022.
    # 人間によるエピソードの比較評価に基づく強化学習法
  • Mathis Petrovich, Chao Liang, Ryoma Sato, Yanbin Liu, Yao-Hung Hubert Tsai, Linchao Zhu, Yi Yang, Ruslan Salakhutdinov, Makoto Yamada.
    Feature Robust Optimal Transport for High-dimensional Data.
    In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2022.
  • Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai, Sekitoshi Kanai, Masanori Yamada, Yuuki Yamanaka, Hisashi Kashima.
    Learning Optimal Priors for Task-Invariant Representations in Variational Autoencoders.
    In Proceedings of the 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2022.
    # 異なるタスクをまたいで普遍的な潜在変数を獲得する条件付きVAEを提案
  • Ryosuke Ueda, Koh Takeuchi, Hisashi Kashima.
    Mitigating Observation Biases in Crowdsourced Label Aggregation.
    In Proceedings of the 26th International Conference on Pattern Recognition (ICPR), 2022.
    # クラウドソーシングの回答に観測バイアスが存在する場合の回答統合手法を提案
  • Yoichi Chikahara, Makoto Yamada, Hisashi Kashima.
    Feature Selection for Discovering Distributional Treatment Effect Modifiers.
    In Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI), 2022.
    # 介入効果推定のための特徴選択法を提案
  • Ryoma Sato, Makoto Yamada, Hisashi Kashima.
    Re-evaluating Word Mover’s Distance.
    In Proceedings of the 39th International Conference on Machine Learning (ICML), 2022.
    # 適切に正規化した bag-of-words は Word Mover’s Distance に迫ることを発見
  • Ryoma Sato.
    Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem.
    In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL), 2022.
    # 大規模な語彙(単語集合)を一次元に並べる手法を提案
  • Yuki Takezawa, Ryoma Sato, Zornitsa Kozareva, Sujith Ravi, Makoto Yamada.
    Fixed Support Tree-Sliced Wasserstein Barycenter.
    In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.
    # 超高速なBarycenter推定手法の提案
  • Benjamin Poignard, Peter Naylor, Héctor Climente, Makoto Yamada.
    Feature Screening with Kernel Knockoff.
    In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.
    # カーネル法とKnockoff filterに基づいた選択的推論の提案
  • Ryoma Sato.
    Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data?
    In Proceedings of the SIAM International Conference on Data Mining (SDM), 2022.
    # ウェブサービスのユーザーが非公開データにアクセスすることなく独自の推薦システムを構築する方法を提案
  • Akihiro Yamaguchi, Ken Ueno, Hisashi Kashima.
    Learning Time-series Shapelets Enhancing Discriminability.
    In Proceedings of the SIAM International Conference on Data Mining (SDM), 2022.
    # 時系列のクラス分類に貢献するShapelet(部分時系列)特徴量を学習する手法を提案
  • Sein Minn, Jill-Jenn Vie, Koh Takeuchi, Hisashi Kashima, Feida Zhu.
    Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations.
    In Proceedings of the 12th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI), 2022.
    # eラーニング上の生徒の習熟度を特徴量構造の発見によって推定する手法を提案
  • Akihiro Yamguchi, Ken Ueno, Hisashi Kashima.
    Learning Evolvable Time-series Shapelets.
    In Proceedings of the 38th International Conference on Data Engineering (ICDE), 2022.
    # 時間変化するShapelet(部分時系列)特徴量をもつ時系列分類手法
  • Ryoma Sato.
    Enumerating Fair Packages for Group Recommendations.
    In Proceedings of the Fifteenth International Conference on Web Search and Data Mining (WSDM), 2022.
    # グループに対するアイテム集合の公平な推薦方法の高速な列挙法
  • Ryoma Sato.
    Retrieving Black-box Optimal Images from External Databases.
    In Proceedings of the Fifteenth International Conference on Web Search and Data Mining (WSDM), 2022.
    # インターネット上の画像データベースから所望の画像を取得する手法を提案

2021

ジャーナル

国際会議

  • Hiroaki Yamada, Makoto Yamada.
    Dynamic Sasvi: Strong Safe Screening for Norm-Regularized Least Squares
    Advances in Neural Information Processing Systems (NeurIPS), 2021
    # 高速な変数スクリーニング法の提案
  • Tam Le, Truyen Nguyen, Makoto Yamada, Jose Blanchet, Viet Anh Nguyen.
    Adversarial Regression with Doubly Non-negative Weighting Matrices
    Advances in Neural Information Processing Systems (NeurIPS), 2021
  • Jiyi Li, Lucas Ryo Endo, Hisashi Kashima.
    Label Aggregation for Crowdsourced Triplet Similarity Comparisons.
    In Proceedings of the 28th International Conference on Neural Information Processing (ICONIP), 2021.
    # クラウドソーシングによる3オブジェクトの相対類似度比較の統合手法を提案
  • Koh Takeuchi, Masaaki Imaizumi, Shunsuke Kanda, Keisuke Fujii, Masakazu Ishihata, Takuya Maekawa, Ken Yoda, Yasuo Tabei.
    Fréchet Kernel for Trajectory Data Analysis.
    In Proceedings of 29th ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL), 2021.
    # 空間上の移動軌跡データを解析するFréchetカーネルを提案
  • Shonosuke Harada, Hisashi Kashima.
    GraphITE: Estimating Individual Effects of Graph-structured Treatments.
    In Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM), 2021.
    # 化合物のようなグラフ構造をもつ介入がある場合の介入効果推定法
  • Toshihiro Kamishima, Shotaro Akaho, Yukino Baba, Hisashi Kashima.
    Preliminary Experiments to Examine the Stability of Bias-Aware Techniques.
    In Proceedings of the 2nd International Workshop on Algorithmic Bias in Search and Recommendation (BIAS), 2021.
  • Takako Onishi, Hisashi Kashima.
    Machine Failure Diagnosis by Combining Software Log and Sensor Data.
    In Proceedings of IEEE International Conference on Electrical, Control and Instrumentation Engineering (ICECIE), 2021.
    # ソフトウェアログとセンサーデータに基づくグラフベース故障診断
  • Xiaotian Lu, Arseny Tolmachev, Tatsuya Yamamoto, Koh Takeuchi, Seiji Okajima, Tomoyoshi Takebayashi, Koji Maruhashi, Hisashi Kashima.
    Crowdsourcing Evaluation of Saliency-based XAI Methods.
    In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2021.
    # クラウドソーシングによるXAI手法(AIの判断の解釈手法)の定量評価
  • Luu Huu Phuc, Koh Takeuchi, Seiji Okajima, Arseny Tolmachev, Tomoyoshi Takebayashi, Koji Maruhashi, Hisashi Kashima.
    Inter-domain Multi-relational Link Prediction.
    In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2021.
    # 複数ドメイン・複数種の関係グラフ上のリンク予測法
  • Yanbin Liu, Makoto Yamada, Yao-Hung Hubert Tsai, Tam Le, Ruslan Salakhutdinov, Yi Yang.
    LSMI-Sinkhorn: Semi-supervised Squared-Loss Mutual Information Estimation with Optimal Transport.
    In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2021.
    # 最適輸送を用いた半教師付き相互情報量推定
  • Shu Nakamura, Koh Takeuchi, Hisashi Kashima, Takeshi Kishikawa, Takashi Ushio, Tomoyuki Haga, Takamitsu Sasaki.
    In-Vehicle Network Attack Detection Across Vehicle Models: A Supervised-Unsupervised Hybrid Approach.
    In Proceedings of the 24th IEEE International Intelligent Transportation Systems Conference (ITSC), 2021.
    # 異車種をまたいだ、車載ネットワークへの攻撃検知法を提案
  • Maya Okawa, Tomoharu Iwata, Yusuke Tanaka, Hiroyuki Toda, Takeshi Kurashima, Hisashi Kashima.
    Dynamic Hawkes Processes for Discovering Time-evolving Communities’ States behind Diffusion Processes.
    In Proceedings of the 27st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021.
    # 時空間的な拡散プロセスをモデル化してSNSや感染症などのイベントを予測
  • Yuki Takezawa, Ryoma Sato, Makoto Yamada
    Supervised Tree-Wasserstein Distance.
    In Proceedings of the 38th International Conference on Machine Learning (ICML), 2021.
    # 教師あり木構造Wasserstein 距離
  • Tobias Freidling, Benjamin Poignard, Héctor Climente-González, Makoto Yamada
    Post-selection inference with HSIC-Lasso.
    In Proceedings of the 38th International Conference on Machine Learning (ICML), 2021.
    # HSIC Lassoのための選択的推論アルゴリズム
  • Vu Nguyen, Tam Le, Makoto Yamada, Michael A Osborne
    Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search.
    In Proceedings of the 38th International Conference on Machine Learning (ICML), 2021.
    # 木構造Wasserstein距離を用いたNASの提案
  • Ayato Toyokuni, Sho Yokoi, Hisashi Kashima, Makoto Yamada
    Computationally Efficient Wasserstein Loss for Structured Labels
    EACL SRW 2021
    # 木構造Wasserstein距離を用いたマルチラベル学習の提案
  • Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima.
    Causal Combinatorial Factorization Machines for Set-wise Recommendation.
    In Proceedings of the 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2021.
    # 組合せ介入の因果効果推定法
  • Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima.
    Regret Minimization for Causal Inference on Large Treatment Space.
    In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.
    # 非常に多数の介入がある場合の因果効果推定法
  • Yoichi Chikahara, Shinsaku Sakaue, Akinori Fujino, Hisashi Kashima.
    Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint.
    In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.
  • Tam Le, Nhat Ho, Makoto Yamada.
    Flow-based Alignment Approaches for Probability Measures in Different Spaces.
    In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.
  • Ryoma Sato, Makoto Yamada, Hisashi Kashima.
    Random Features Strengthen Graph Neural Networks.
    In Proceedings of SIAM International Conference on Data Mining (SDM), 2021.
    # ノードにランダム特徴を加えることでグラフニューラルネットワークの表現を高める方法とその理論保証
  • Koh Takeuchi, Ryo Nishida, Hisashi Kashima, Masaki Onishi
    Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference.
    In Proceedings of 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2021.
    # 因果推論を用いた群衆の誘導効果予測

2020

ジャーナル

国際会議

  • Ryoma Sato, Makoto Yamada, Hisashi Kashima.
    Fast Unbalanced Optimal Transport on Tree.
    Advances in Neural Information Processing Systems (NeurIPS 2020).
    # 木の上で定義された不均衡最適輸送問題を高速に解く手法
  • Yao-Hung Hubert Tsai, Han Zhao, Makoto Yamada, Louis-Philippe Morency, Ruslan Salakhutdinov.
    Neural Methods for Point-wise Dependency Estimation
    Advances in Neural Information Processing Systems (NeurIPS 2020).
    # 密度比推定を用いた表現学習の方法を提案
  • Shunsuke Kanda, Koh Takeuchi, Keisuke Fujii, Yasuo Tabei.
    Succinct Trit-array Trie for Scalable Trajectory Similarity Search.
    In Proceedings of 28th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL), 2020.
    # 膨大な数の移動軌跡データから、類似した移動軌跡を高速かつ省メモリに探索する手法を提案
  • Luu Huu Phuc, Koh Takeuchi, Makoto Yamada, Hisashi Kashima.
    Simultaneous Link Prediction on Unaligned Networks Using Graph Embedding and Optimal Transport.
    In Proceedings of the the 7th IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2020.
    # ノードの対応が与えられていない2つのネットワーク上でのリンク予測問題の解法を提案
  • Hitoshi Kusano, Yuji Horiguchi, Yukino Baba and Hisashi Kashima.
    Stress Prediction from Head Motion.
    In Proceedings of the the 7th IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2020.
    # 頭部の動きからユーザのストレス状態を判定する方法を提案
  • Yukino Baba, Jiyi Li, Hisashi Kashima.
    CrowDEA: Multi-view Idea Prioritization with Crowds.
    In Proceedings of the 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2020.
    # 多数のアイディアの評価と整理をクラウドソーシングによって行う方法を提案
  • Yan Gu, Jiuding Duan, Hisashi Kashima.
    An Intransitivity Model for Matchup and Pairwise Comparison.
    In Proceedings of the 25th International Conference on Pattern Recognition (ICPR), 2020.
    # 推移率を満たさないようなランキング問題に対する一般的な深層学習モデルを提案
  • Shounosuke Harada, Hisashi Kashima.
    Counterfactual Propagation for Semi-Supervised Individual Treatment Effect Estimation.
    In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2020.
    # 半教師付き因果効果推定問題とグラフ伝播法による解法を提案
  • Yasutoshi Ida, Sekitoshi Kanai,Yasuhiro Fujiwara, Tomoharu Iwata, Koh Takeuchi, Hisashi Kashima.
    Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance.
    In Proceedings of the 37th International Conference on Machine Learning (ICML), 2020.
    # 凸定式化されたCUR行列分解の決定的な高速解法を提案
  • Jiyi Li, Yasushi Kawase, Yukino Baba, Hisashi Kashima.
    Performance as a Constraint: An Improved Wisdom of Crowds Using Performance Regularization.
    In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI), pp.XX-XX, 2020.
    # 多数決が正しい結果を導かない難しい意見統合問題において、正解率を制約として用いる意見統合法を提案
  • Yanbin Liu, Linchao Zhu, Makoto Yamada, Yi Yang.
    Semantic Correspondence as an Optimal Transport Problem
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
  • Tatsuya Shiraishi, Tam Le, Hisashi Kashima, Makoto Yamada
    Topological Bayesian Optimization with Persistence Diagrams.
    In Proceedings of the 24th European Conference on Artificial Intelligence (ECAI), 2020.
  • Benjamin Poignard, Makoto Yamada
    Sparse Hilbert-Schmidt Independence Criterion Regression.
    In Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
  • Jenning Lim, Makoto Yamada, Wittawat Jitkrittum, Yoshikazu Terada, Shigeyuki Matsui, Hidetoshi Shimodaira
    More Powerful Selective Kernel Tests for Feature Selection
    In Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
  • 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.

プレプリント

2019

ジャーナル

  • Kishan, Wimalawarne, Makoto Yamada,  Hiroshi Mamitsuka.
    Scaled Coupled Norms and Coupled Higher Order Tensor Completion.
    Neural Computation (NECO), 2019.
  • Yu Saito, Kento Shin, Kei Terayama, Shaan Desai, Masaru Onga, Yuji Nakagawa, Yuki M. Itahashi, Yoshihiro Iwasa, Makoto Yamada, Koji Tsuda
    Deep-learning-based quality filtering of mechanically exfoliated 2D crystals
    npj Computational Materials volume 5, Article number: 124 (2019)
  • 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.
    # グラフニューラルネットワークの予測根拠を示す方法
  • 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.
    # ニューラルネットを用いた短期気象予測
  • 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.
    # 一対比較データからクラスタリングとランキングを同時に求める方法
  • 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.
    # 予測に有効な特徴量をアルゴリズムに組み込まれた人間が適応的に作る「人間ブースティング」
  • 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.
    # 教育用データ解析コンペティションの分析
  • 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.
    # 幼児教育におけるスキル獲得予測

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。