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In this talk, Prof. Masashi Sugiyama will introduce his recent research on weakly-supervised learning and reliable learning.

The motivation for weakly-supervised learning is to accurately perform machine learning only from "weak" data that can be collected more easily/cheaply than fully-labeled data. In the first half of this talk, he gives an overview of our recently developed empirical risk minimization framework for weakly-supervised classification, covering binary classification only from PU data, PNU data, Pconf data, UU data, SU data, and Comp data (P:positive, N:negative, U:unlabeled, Conf:confidence, S:similar, and Comp:complementary).

For reliable deployment of machine learning systems in the real world, various types of robustness is needed. In the latter half of this talk, Prof. Masashi Sugiyama will give an overview of his recent work on robust learning towards noisy training data, changing environments, and adversarial test input.

Finally, he will briefly introduce our RIKEN Center for Advanced Intelligence Project (AIP), which is a national AI project in Japan started in 2016. AIP covers a wide range of topics from generic AI research (machine learning, optimization, applied math., etc.), goal-oriented AI research (material, disaster, cancer, etc.), and AI-in-society research (ethics, data circulation, laws, etc.).

Lien vers le site web: http://www.idiap.ch/en/talks

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