ECE SEMINAR
Ian A. Kash, Microsoft Research Canada, United Kingdom
Invited by Professor Krzysztof Czarnecki
ALL ARE WELCOME!
Abstract:
Markets
are
a
powerful
tool
for
producing
desirable
outcomes
in
an
engineered
system.
As
computers
allow
us
to
build
new
and
more
complex
markets,
we
need
algorithmic
tools
to
understand
and
improve
them.
In
this
talk
I'll
discuss
two recent
lines
of
work
in
this
space.
Advertising
is
the
main
source
of
revenue
for
search
engines
such
as
Bing
and
Google.
Decisions
about
how
to which
ads
to
show
where impose
trade-offs
between
objectives
such
as
revenue
and
welfare.
In
this
talk,
I'll
discuss
how
these
trade-offs
should
be
made,
beginning
with
a
new
ranking
algorithm
based
on
the
revenue-optimal
auction
that
uses
a
reserve
price
to
change
the
way
ads
are
ranked,
not
merely
as
a
minimum
bid.
From
there,
I'll
discuss
the
optimal
way
to
make
such
trade-offs
in
general and
evaluate
it
using
numerical
simulations
and
Bing
data.
Our tools to understand auctions rely on characterization theorems regarding convex functions and their sub gradients. Similar characterizations arise in applications in statistics, finance, and machine learning. I'll present a unifying framework that brings the characterizations from these disparate domains together, and discuss some applications to machine learning.
Biography:
Ian Kash is a researcher in the Networks, Economics, and Algorithms group at Microsoft Research in Cambridge, UK. Previously he was a postdoctoral research fellow at the Center for Research on Computation and Society at Harvard University advised by David Parkes. He received his Ph.D. from Cornell University, where he was advised by Eric Friedman and Joe Halpern.