Machine Learning from Weak Supervision: Towards Accurate Classification with Low Labeling Costs
Speaker:
Masashi Sugiyama, RIKEN and The University of Tokyo
Date and Time:
Tuesday, November 12, 2019 - 2:30pm to 3:00pm
Location:
Fields Institute, Stewart Library
Abstract:
Machine learning from big labeled data is highly successful in areas such as speech recognition, image understanding and natural language translation. However, there are still various application domains where human labor is involved in the data collection process and thus the use of massive labeled data is prohibited. In this talk, I will introduce our recent advances in machine learning techniques from limited supervision based on empirical risk minimization.
Speaker Bio:
