Better Algorithms and Generalization Performance for Structured Data

Speaker:        Hongyang Zhang
                Stanford University

Title:          "Better Algorithms and Generalization Performance for
                 Structured Data"

Date:           Monday, 25 February 2019

Time:           4:00pm - 5:00pm

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

Abstract:

Dealing with large-scale data from modern social and Web systems has been
an interesting challenge for algorithm design and machine learning
recently. Formalizing such challenges often require better modeling of the
underlying data, as well as better modeling of the optimization paradigm
in practice. My research aims to provide new algorithms and better models
for these settings.

This talk will show a few results. First, we study non-convex methods and
their generalization performance (or sample efficiency) for common ML
tasks. We consider over-parameterized models such as matrix and tensor
factorizations. This is motivated by the curious observation that in
practice neural networks are often trained with more parameters than
number of observations. We show that the generalization performance
crucially depends on the initialization in this setting. Meanwhile, adding
parameters helps optimization by avoiding bad local minima. Next, we
consider the problem of predictng the missing entries of tensors. We show
that understanding the generalization performance can inform the choice of
tensor models for this task. Lastly, we revisit the distance sketching
problem on large graphs. We provide new insight on this classic problem by
formalizing the structures of social network data. Our results help
explain the empirical success that has been achieved by recent work.


******************
Biography:

Hongyang Zhang is a Ph.D. candidate studying CS at Stanford University,
co-advised by Ashish Goel and Greg Valiant. His research interests lie in
machine learning and algorithms, including topics related to neural
networks, matrix and tensor factorizations, non-convex optimization,
social network analysis and game theory. He is a co-author on the best
paper at COLT'18.