A Mixture of Past, Present, and Future

Arya Mazumdar, Associate Professor, UC San Diego

Abstract: The problems of heterogeneity pose major challenges in extracting meaningful information from data as well as in the subsequent decision making or prediction tasks. Heterogeneity brings forward some very fundamental theoretical questions of machine learning. For unsupervised learning, a standard technique is the use of mixture models for statistical inference. However for supervised learning, labels can be generated via a mixture of functional relationships. We will provide a survey of results on parameter learning in mixture models, some unexpected connections with other problems, and some interesting future directions.

The event is finished.

Date

Nov 17 2021
Expired!

Time

10:00 - 11:00

Local Time

  • Timezone: America/New_York
  • Date: Nov 17 2021
  • Time: 13:00 - 14:00

Location

Virtual

Organizer

TILOS

Speaker