Basic Information

Academic Degree

• 24 Mar. 2022

  東京大学博士（情報理工学）
• 25 Mar. 2019

  東京大学修士（情報理工学）

Academic Resume (Graduate Schools)

• 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

Academic Resume (Undergraduate School/Majors)

• 東京大学, 理学部情報科学科, 卒業

ID,URL

• J-Global ID

  201801010438043202

• External link

  https://hermite.jp/

researchmap URL

• https://researchmap.jp/hanbao

Research

Research Interests

• machine learning
• 機械学習

Research Areas

• Informatics, Statistical science

Papers

• 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

  
• Calibrated Surrogate Losses for Adversarially Robust Classification.

  Han Bao 0002; Clayton Scott; Masashi Sugiyama

  CoRR, 2020

  
• Learning from Noisy Similar and Dissimilar Data.

  Soham Dan; Han Bao 0002; Masashi Sugiyama

  CoRR, 2020

  
• 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

  
• 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

  
• 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

  
• Classification From Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization.

  Takuya Shimada; Han Bao 0002; Issei Sato; Masashi Sugiyama

  Neural Computation, 2021

  
• Calibrated Surrogate Losses for Adversarially Robust Classification.

  Han Bao 0002; Clayton Scott; Masashi Sugiyama

  Conference on Learning Theory(COLT), 2020

  
• 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

  
• 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, Peer-reviewed

  
• 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, Sep. 2018, Peer-reviewed

  
• Similarity-based Classification: Connecting Similarity Learning to Binary Classification.

  Han Bao 0002; Takuya Shimada; Liyuan Xu; Issei Sato; Masashi Sugiyama

  CoRR, 2020

  

Misc.

• Classification from Pairwise Similarity and Unlabeled Data

  Han Bao; Gang Niu; Masashi Sugiyama

  35th International Conference on Machine Learning, ICML 2018, 2018, Peer-reviewed