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包 含

ツツミ フクム

白眉センター 特定助教

包 含
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    Last Updated :2024/06/13

    基本情報

    学位

    • 2022年03月24日
      東京大学博士(情報理工学)
    • 2019年03月25日
      東京大学修士(情報理工学)

    出身大学院・研究科等

    • 東京大学, 大学院情報理工学系研究科修士課程コンピュータ科学専攻, 修了
    • 東京大学, 大学院情報理工学系研究科博士後期課程コンピュータ科学専攻, 修了

    出身学校・専攻等

    • 東京大学, 理学部情報科学科, 卒業

    ID,URL

    researchmap URL

    list
      Last Updated :2024/06/13

      研究

      研究キーワード

      • machine learning
      • 機械学習

      研究分野

      • 情報通信, 統計科学

      論文

      • Online Policy Learning from Offline Preferences.
        Guoxi Zhang; Han Bao 0002; Hisashi Kashima
        CoRR, 2024年
      • Online Structured Prediction with Fenchel-Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss.
        Shinsaku Sakaue; Han Bao 0002; Taira Tsuchiya; Taihei Oki
        CoRR, 2024年
      • Self-attention Networks Localize When QK-eigenspectrum Concentrates.
        Han Bao 0002; Ryuichiro Hataya; Ryo Karakida
        CoRR, 2024年
      • Fast 1-Wasserstein distance approximations using greedy strategies.
        Guillaume Houry; Han Bao 0002; Han Zhao 0002; Makoto Yamada
        AISTATS, 2024年
      • Embarrassingly Simple Text Watermarks.
        Ryoma Sato; Yuki Takezawa; Han Bao 0002; Kenta Niwa; Makoto Yamada
        CoRR, 2023年
      • Necessary and Sufficient Watermark for Large Language Models.
        Yuki Takezawa; Ryoma Sato; Han Bao 0002; Kenta Niwa; Makoto Yamada
        CoRR, 2023年
      • Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics.
        Han Bao 0002
        CoRR, 2023年
      • Estimating Treatment Effects Under Heterogeneous Interference.
        Xiaofeng Lin; Guoxi Zhang; Xiaotian Lu; Han Bao 0002; Koh Takeuchi; Hisashi Kashima
        CoRR, 2023年
      • Unbalanced Optimal Transport for Unbalanced Word Alignment.
        Yuki Arase; Han Bao 0002; Sho Yokoi
        CoRR, 2023年
      • Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence.
        Yuki Takezawa; Ryoma Sato; Han Bao 0002; Kenta Niwa; Makoto Yamada
        CoRR, 2023年
      • Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence.
        Yuki Takezawa; Ryoma Sato; Han Bao 0002; Kenta Niwa; Makoto Yamada
        NeurIPS, 2023年
      • Will Large-scale Generative Models Corrupt Future Datasets?
        Ryuichiro Hataya; Han Bao 0002; Hiromi Arai
        ICCV, 2023年
      • Proper Losses, Moduli of Convexity, and Surrogate Regret Bounds.
        Han Bao 0002
        COLT, 2023年
      • Unbalanced Optimal Transport for Unbalanced Word Alignment.
        Yuki Arase; Han Bao 0002; Sho Yokoi
        ACL (1), 2023年
      • Estimating Treatment Effects Under Heterogeneous Interference.
        Xiaofeng Lin; Guoxi Zhang; Xiaotian Lu; Han Bao 0002; Koh Takeuchi; Hisashi Kashima
        ECML/PKDD (1), 2023年
      • On the Surrogate Gap between Contrastive and Supervised Losses.
        Han Bao 0002; Yoshihiro Nagano; Kento Nozawa
        International Conference on Machine Learning(ICML), 2022年
      • Robust computation of optimal transport by β-potential regularization.
        Shintaro Nakamura; Han Bao 0002; Masashi Sugiyama
        Asian Conference on Machine Learning(ACML), 2022年
      • Approximating 1-Wasserstein Distance with Trees.
        Makoto Yamada; Yuki Takezawa; Ryoma Sato; Han Bao 0002; Zornitsa Kozareva; Sujith Ravi
        Transactions on Machine Learning Research, 2022年
      • Sparse Regularized Optimal Transport with Deformed q-Entropy.
        Han Bao 0002; Shinsaku Sakaue
        Entropy, 2022年
      • Calibrated Surrogate Maximization of Dice.
        Marcus Nordström; Han Bao 0002; Fredrik Löfman; Henrik Hult; Atsuto Maki; Masashi Sugiyama
        Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020年
      • Calibrated Surrogate Losses for Adversarially Robust Classification.
        Han Bao 0002; Clayton Scott; Masashi Sugiyama
        CoRR, 2020年
      • Learning from Noisy Similar and Dissimilar Data.
        Soham Dan; Han Bao 0002; Masashi Sugiyama
        CoRR, 2020年
      • Pairwise Supervision Can Provably Elicit a Decision Boundary.
        Han Bao 0002; Takuya Shimada; Liyuan Xu; Issei Sato; Masashi Sugiyama
        International Conference on Artificial Intelligence and Statistics(AISTATS), 2022年
      • Learning from Noisy Similar and Dissimilar Data.
        Soham Dan; Han Bao 0002; Masashi Sugiyama
        Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, 2021年
      • Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation.
        Han Bao 0002; Masashi Sugiyama
        The 24th International Conference on Artificial Intelligence and Statistics(AISTATS), 2021年
      • Classification From Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization.
        Takuya Shimada; Han Bao 0002; Issei Sato; Masashi Sugiyama
        Neural Computation, 2021年
      • Calibrated Surrogate Losses for Adversarially Robust Classification.
        Han Bao 0002; Clayton Scott; Masashi Sugiyama
        Conference on Learning Theory(COLT), 2020年
      • Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification.
        Han Bao 0002; Masashi Sugiyama
        The 23rd International Conference on Artificial Intelligence and Statistics(AISTATS), 2020年
      • Unsupervised Domain Adaptation Based on Source-Guided Discrepancy.
        Seiichi Kuroki; Nontawat Charoenphakdee; Han Bao; Junya Honda; Issei Sato; Masashi Sugiyama
        In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI2019), 2019年, 査読有り
      • Convex formulation of multiple instance learning from positive and unlabeled bags.
        Han Bao; Tomoya Sakai; Issei Sato; Masashi Sugiyama
        Neural networks : the official journal of the International Neural Network Society, 2018年09月, 査読有り
      • Similarity-based Classification: Connecting Similarity Learning to Binary Classification.
        Han Bao 0002; Takuya Shimada; Liyuan Xu; Issei Sato; Masashi Sugiyama
        CoRR, 2020年

      MISC

      • Imitation learning from imperfect demonstration
        Yueh Hua Wu; Nontawat Charoenphakdee; Han Bao; Voot Tangkaratt; Masashi Sugiyama
        36th International Conference on Machine Learning, ICML 2019, 2019年, 査読有り
      • Classification from Pairwise Similarity and Unlabeled Data
        Han Bao; Gang Niu; Masashi Sugiyama
        35th International Conference on Machine Learning, ICML 2018, 2018年, 査読有り

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