[AI Seminar] Fwd: This Week at Theory Lunch: Ellen Vitercik
Adams Wei Yu
weiyu at cs.cmu.edu
Mon Oct 9 23:20:40 EDT 2017
FYI, the theory lunch this week (Wednesday) might be of interest to you.
Please see the message below.
---------- Forwarded message ----------
From: Nicolas Resch <nresch at cs.cmu.edu>
Date: Mon, Oct 9, 2017 at 8:50 AM
Subject: This Week at Theory Lunch: Ellen Vitercik
To: theory-announce at cs.cmu.edu
Please join us Wednesday at noon in GHC 6115 for a talk by Ellen Vitercik,
where lunch will be provided. A video recording of the talk will be
available on the CMU Youtube Theory channel
*Location/Time:* Wednesday, October 11, 12-1pm
*Speaker: *Ellen Vitercik
*Title:* Differentially private algorithm and auction configuration
Algorithm configuration is an important aspect of modern data science and
algorithm design. Algorithms regularly depend on tunable parameters which
have a substantial effect on run-time and solution quality. Researchers
have developed machine learning techniques for automated parameter tuning
which use a training set of problem instances to determine a configuration
with high expected performance over future instances. This line of work has
inspired breakthroughs in diverse fields including combinatorial auctions,
scientific computing, vehicle routing, and SAT. The resulting configuration
depends on the training set, implying that without proper precautions, it
may leak sensitive information about problem instances contained therein.
In this work, we address this problem by showing that a natural adaptation
of the exponential mechanism provides strong privacy and utility guarantees
for a wide range of algorithm configuration problems. We uncover a
structural property shared among many problems which ensures this
algorithm’s success: these problems reduce to maximizing piecewise
L-Lipschitz functions. We call upon techniques from high-dimensional
geometry to efficiently implement this algorithm when the algorithm
parameter space is multi-dimensional. We apply our differentially private
algorithm to many configuration problems where privacy preservation is
essential, such as integer quadratic programming and greedy algorithm
configuration. We also show that our differentially private algorithm can
be used to design auctions and pricing mechanisms based on consumer data,
thus further exhibiting the algorithm’s broad applicability.
See you there!
Ellis and Nic
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