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<p class="MsoNormal"><i><span style="font-size:12.0pt;font-family:Helvetica;color:#8B0102"> </span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:12.0pt;font-family:Helvetica;color:#8B0102">UNIVERSITY OF CHICAGO</span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:12.0pt;font-family:Helvetica;color:#8B0102">COMPUTER SCIENCE DEPARTMENT</span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:12.0pt;font-family:Helvetica;color:#8B0102">PRESENTS</span></i><o:p></o:p></p>
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<p class="MsoNormal"><b><span style="font-size:12.0pt;font-family:Helvetica">Samuel Hopkins, PhD</span></b><o:p></o:p></p>
<p class="MsoNormal"><b><span style="font-size:11.0pt;font-family:Helvetica">Massachusetts Institute of Technology</span></b><o:p></o:p></p>
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Description automatically generated with medium confidence"></span></b><b><span style="font-size:11.0pt;font-family:Helvetica;color:black"> </span></b><o:p></o:p></p>
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<p class="MsoNormal"><b><span style="font-size:11.0pt;color:black">Tuesday, </span>
</b><b><span style="font-size:11.0pt">May 16<span style="color:black">, 202</span>3<span style="color:black"> at 3:30pm</span></span></b><o:p></o:p></p>
<p class="MsoNormal"><b><span style="font-size:11.0pt;color:black;background:yellow">Kent Chemical Laboratory, Room 120 (New Room)</span></b><o:p></o:p></p>
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<p style="margin:0in"><b><i><span style="font-size:12.0pt">Title:</span></i></b><span style="font-size:12.0pt">
<span style="color:#212121">Robustness Implies Privacy in Statistical Estimation</span></span></p>
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<p style="margin:0in"><b><i><span style="font-size:12.0pt">Abstract:</span></i></b><b><span style="font-size:12.0pt">
</span></b><span style="font-size:12.0pt"> </span> <span style="font-size:12.0pt;color:black">
We study the relationship between adversarial robustness and differential privacy in high-dimensional algorithmic statistics. We give the first black-box reduction from privacy to robustness which can produce private estimators with optimal tradeoffs among
sample complexity, accuracy, and privacy for a wide range of fundamental high-dimensional parameter estimation problems, including mean and covariance estimation. We show that this reduction can be implemented in polynomial time in some important special cases.
In particular, using nearly-optimal polynomial-time robust estimators for the mean and covariance of high-dimensional Gaussians which are based on the Sum-of-Squares method, we design the first polynomial-time private estimators for these problems with nearly-optimal
samples-accuracy-privacy tradeoffs. Our algorithms are also robust to a constant fraction of adversarially-corrupted samples. Joint work with Gautam Kamath, Mahbod Majid, and Shyam Narayanan, to appear in STOC 2023.</span><span style="font-size:12.0pt"><o:p></o:p></span></p>
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<p class="MsoNormal"><b><i><span style="font-size:12.0pt">Bio:</span></i></b><span style="font-size:12.0pt">
</span><span style="font-size:12.0pt;color:#414042">Samuel Hopkins is an Assistant Professor in EECS at MIT. His research focuses on algorithms, optimization, and theoretical machine learning, especially through the lens of convex programming relaxations. He
received his PhD in Computer Science from Cornell, and his BS in Computer Science and Mathematics from the University of Washington. </span><span style="font-size:12.0pt;color:#212121"><o:p></o:p></span></p>
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<p style="margin-bottom:12.0pt"><b><span style="font-size:12.0pt;font-family:Helvetica;color:black">Host:<span class="apple-converted-space"> Aaron Potechin</span></span></b><br>
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