Wednesday, February 19, 2020 – 4:00pm
Dunham Lab 220See map
10 Hillhouse Avenue
New Haven, CT 06511
CS Distinguished Colloquium
Speaker: Prof. Umesh Vazirani, UC Berkeley
Host: Nisheeth Vishnoi
Title: Theoretical Reflections on Quantum Supremacy
The recent demonstration of quantum supremacy by Google is a first step towards the era of small to medium scale quantum computers. In this talk I will explain what the experiment accomplished and the theoretical work it is based on, as well as what it did not accomplish and the many theoretical and practical challenges that remain. I will also describe recent breakthroughs in the design of protocols for the testing and benchmarking of quantum computers, a task that has deep computational and philosophical implications. Specifically, this leads to protocols for scalable and verifiable quantum supremacy, certifiable quantum random generation and verification of quantum computation.
I would like to invite you to a one day conference on Imaging Mass Spectrometry and Omics Symposium that will be held at YSPH tomorrow on November 22, 2019. The flyer is attached.
Tissue Imaging Flyer2019_v3.pdf
MONDAY, OCTOBER 28, 2019
Department of Statistics and Data Science
Weijie Su, University of Pennsylvania, Wharton School
Monday, October 28, 2019
4:00PM to 5:15PM
Dunham Lab. see map
10 Hillhouse Avenue, 2nd Floor, Room 220
New Haven, https://statistics.yale.edu/seminars/weijie-su-0
Title: Gaussian Differential Privacy
Information and Abstract: Privacy-preserving data analysis has been put on a firm mathematical foundation since the introduction of differential privacy (DP) in 2006, with its deployment on iOS and Chrome lately. This privacy definition, however, has some well-known weaknesses: notably, it does not tightly handle composition. This weakness has inspired several recent relaxations of differential privacy based on Renyi divergences. We propose an alternative relaxation of differential privacy, which we term “f-DP”, which has a number of nice properties and avoids some of the difficulties associated with divergence based relaxations. First, it preserves the hypothesis testing interpretation of differential privacy, which makes its guarantees easily interpretable. It allows for lossless reasoning about composition and post-processing, and notably, a direct way to analyze privacy amplification by subsampling. We define a canonical single-parameter family of definitions within our class that is termed “Gaussian Differential Privacy”, based on hypothesis testing of two shifted normal distributions. We prove that this family is focal to f-DP by introducing a central limit theorem, which shows that the privacy guarantees of any hypothesis-testing based definition of privacy (including differential privacy) converge to Gaussian differential privacy in the limit under composition. This central limit theorem also gives a tractable analysis tool. We demonstrate the use of the tools we develop by giving an improved analysis of the privacy guarantees of noisy stochastic gradient descent.
This is joint work with Jinshuo Dong and Aaron Roth.
3:45 p.m. Pre-talk tea Dunham Lab, Suite 222, Breakroom 228
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