5 papers · 1 filter
Rethinking Learning from Label Proportions via Moment Matching
Tianhao Ma, Wei Wang, Yivan Zhang +4
Learning from label proportions (LLP) is a weakly supervised setting where training data are grouped into bags with only bag-level label proportions observed, and the goal is to le…
Asymptotically Exact Negative Guidance of Diffusion Models via Positive-Unlabeled Learning
Hirohane Takagi, Masashi Sugiyama
Negative guidance of diffusion models generates samples that avoid an undesired condition, specified either by reference samples or by a conditional score alongside an unconditiona…
Recent advances in weakly supervised learning: New supervision paradigms, assumption relaxations, and practical solutions
Wei Wang, Gang Niu, Masashi Sugiyama
Deep learning has achieved great success in recent years thanks to the availability of high-quality, well-annotated training data. However, this requirement is often not met in rea…
Learning Robust Diffusion Models from Imprecise Supervision
Dong-Dong Wu, Jiacheng Cui, Wei Wang +2
Conditional diffusion models have achieved remarkable success in various generative tasks recently, but their training typically relies on large-scale datasets that inevitably cont…
Accessible, Realistic, and Fair Evaluation of Positive-Unlabeled Learning Algorithms
Wei Wang, Dong-Dong Wu, Ming Li +3
Positive-unlabeled (PU) learning is a weakly supervised binary classification problem, in which the goal is to learn a binary classifier from only positive and unlabeled data, with…