Two following questions will be discussed: 1. How does embedding low dimensional structures in high dimensional spaces decreases the learning complexity significantly? I will consider the simplest model, that is a linear transformation with additive noise. 2. Modern datasets are accumulated (and in some cases even stored) in a distributed or decentralized manner. Can distributed algorithms be designed to fit a global model over such datasets while retaining the performance of centralized estimators?
The talk will be based on the following two papers: