Modern Stochastic Optimization Methods for Big Data Machine Leraning

Speaker:        Dr. Tong ZHANG
                Tencent AI Lab

Title:          "Modern Stochastic Optimization Methods for Big Data
                 Machine Leraning"

Date:           Friday, 2 Nov 2018

Time:           3:00pm - 4:00pm

Venue:          Lecture Theater F (near lift 25/26), HKUST


In classical optimization, one needs to calculate a full (deterministic) 
gradient of the objective function at each step, which can be extremely 
costly for modem applications of big data machine learning. A remedy to 
this problem is to approximate each full gradient with a random sample 
over the data. This approach reduces the computational cost at each step, 
but introduces statistical variance.

In this talk, I will present some recent progresses on applying variance 
reduction techniques previously developed for statistical Monte Carlo 
methods to this new problem setting. The resulting stochastic optimization 
methods are highly effective for practical big data problems in machine 
learning, and the new methods have strong theoretical guarantees that 
significantly improve the computational lower bounds of classical 
optimization algorithms.

Collaborators: Rie Johnson, Shai Shalev-Schwartz, Jialei Wang