Rules for laptops

Leave a reply

Rules for laptops

General rules:

Always ensure that JL has your serial number(s). It is your responsibility to let him know which laptop you have returned so he can document that you’ve returned a laptop. Laptops that are given out are automatically tagged with your initials. Sometimes, JL may email you asking for clarification on serial numbers – please reply to these emails.

New laptops are only given out with the agreement that an old laptop will be returned within 4 weeks of receiving the new one.

Any old laptop that is returned should be properly formatted to factory settings. This is a simple task that can be achieved following these guides:

https://support.apple.com/en-us/HT208496
https://support.apple.com/en-us/HT204904

CLEAN YOUR LAPTOP when returning. Wipe down your laptop screen, keyboard, and externally.

New laptops come with a charging cable. Please return your old charger with your old laptop.

Quarantine specific rules:

When new laptops are distributed, designated pickup days outside of the loading dock will be chosen. Lab members receiving new laptops must be on time in the designated time frame.

Laptop returns will be done on a designated “return” day, similar to receiving.

Statseminars CS Distinguished Colloquium, Wednesday, February 19, 2020 – 4:00pm

Leave a reply

Event time:

Wednesday, February 19, 2020 – 4:00pm

Location:

Dunham Lab 220See map

10 Hillhouse Avenue

New Haven, CT 06511

Event description:

CS Distinguished Colloquium

Speaker: Prof. Umesh Vazirani, UC Berkeley

Host: Nisheeth Vishnoi

Title: Theoretical Reflections on Quantum Supremacy

Abstract:

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.

Statseminars S&DS Talk, Speaker: Weijie Su, 10/28, “Gaussian Differential Privacy”, DL220

Leave a reply

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

For more details and upcoming events visit our website at
http://statistics.yale.edu/ .