Doctoral Thesis: Channel Comparison Methods and Statistical Problems on Graphs

Wednesday, April 26
10:30 am - 12:00 pm


Yuzhou Gu

Initially driven by channel coding, information theory has developed a
large collection of tools for measuring and comparing effectiveness of
information channels. These tools have found applications in various
fields such as statistics, probability, and theoretical computer
science. This thesis explores several applications of these tools to
statistical problems related to graphs.

Part I focuses on information channels and channel comparison methods,
including $f$-divergences, strong data processing inequalities, and
preorders between channels. While these theories have been
well-established for binary memoryless symmetric (BMS) channels, there
remains much to discover for channels with larger input alphabets. We
develop a theory of $q$-ary input-symemtric (FMS) channels,
generalizing the theory of BMS channels. We demonstrate that while FMS
channels exhibit more complex behavior than BMS channels, some
properties of BMS channels can be extended to FMS channels.
Furthermore, we perform tight analysis on contraction properties of
the Potts channels, the simplest examples of FMS channels.

In Part II, we apply the information theoretical methods established
in Part I to solve problems related to random graph models with
community structures. The random graph models include the stochastic
block model (SBM) and its variants, which hold significance in
statistics, machine learning, and network science. Central problems
for these models ask about the feasibility and quality of recovering
hidden community structures from unlabeled graphs. By utilizing the
relationship between random graphs and random Galton-Watson trees, we
demonstrate that many important problems on these graphical models can
be reduced to problems on trees. We apply various channel comparison
methods to solve these tree problems, demonstrating that different
methods are effective for different problems and that selecting the
correct tool for a problem is crucial. Problems we study include (for
SBMs) weak recovery, optimal recovery algorithms, mutual information
formula, and (for broadcasting on trees) reconstruction, robust
reconstruction, uniqueness of belief propagation fixed points,
boundary irrelevance, and so on.


  • Date: Wednesday, April 26
  • Time: 10:30 am - 12:00 pm
  • Category:
  • Location: 32-D677
Additional Location Details:

Thesis Committee: Professors Yury Polyanskiy (chair, supervisor), Guy Bresler, Elchanan Mossel