454 citations · 5.5k across the 247 of their papers we have counts for
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Bezier Simplex Fitting: Describing Pareto Fronts of Simplicial Problems with Small Samples in Multi-objective Optimization
Ken Kobayashi, Naoki Hamada, Akiyoshi Sannai +3
Multi-objective optimization problems require simultaneously optimizing two or more objective functions. Many studies have reported that the solution set of an M-objective optimiza…
Active Deep Q-learning with Demonstration
Si-An Chen, Voot Tangkaratt, Hsuan-Tien Lin +1
Recent research has shown that although Reinforcement Learning (RL) can benefit from expert demonstration, it usually takes considerable efforts to obtain enough demonstration. The…
Complementary-Label Learning for Arbitrary Losses and Models
Takashi Ishida, Gang Niu, Aditya Krishna Menon +1
In contrast to the standard classification paradigm where the true class is given to each training pattern, complementary-label learning only uses training patterns each equipped w…
Classification from Positive, Unlabeled and Biased Negative Data
Yu-Guan Hsieh, Gang Niu, Masashi Sugiyama
In binary classification, there are situations where negative (N) data are too diverse to be fully labeled and we often resort to positive-unlabeled (PU) learning in these scenario…
Dueling Bandits with Qualitative Feedback
Liyuan Xu, Junya Honda, Masashi Sugiyama
We formulate and study a novel multi-armed bandit problem called the qualitative dueling bandit (QDB) problem, where an agent observes not numeric but qualitative feedback by pulli…
Alternate Estimation of a Classifier and the Class-Prior from Positive and Unlabeled Data
Masahiro Kato, Liyuan Xu, Gang Niu +1
We consider a problem of learning a binary classifier only from positive data and unlabeled data (PU learning) and estimating the class-prior in unlabeled data under the case-contr…