19 citations · 42 across the 5 of their papers we have counts for
6 papers · 1 filter
Learning from Noisy Similar and Dissimilar Data
Soham Dan, Han Bao, Masashi Sugiyama
With the widespread use of machine learning for classification, it becomes increasingly important to be able to use weaker kinds of supervision for tasks in which it is hard to obt…
Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification
Han Bao, Masashi Sugiyama
Complex classification performance metrics such as the F-measure and Jaccard index are often used, in order to handle class-imbalanced cases such as information retrieval and…
Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization
Takuya Shimada, Han Bao, Issei Sato +1
Pairwise similarities and dissimilarities between data points might be easier to obtain than fully labeled data in real-world classification problems, e.g., in privacy-aware situat…
Imitation Learning from Imperfect Demonstration
Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao +2
Imitation learning (IL) aims to learn an optimal policy from demonstrations. However, such demonstrations are often imperfect since collecting optimal ones is costly. To effectivel…
Unsupervised Domain Adaptation Based on Source-guided Discrepancy
Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao +3
Unsupervised domain adaptation is the problem setting where data generating distributions in the source and target domains are different, and labels in the target domain are unavai…
Classification from Pairwise Similarity and Unlabeled Data
Han Bao, Gang Niu, Masashi Sugiyama
Supervised learning needs a huge amount of labeled data, which can be a big bottleneck under the situation where there is a privacy concern or labeling cost is high. To overcome th…