\sup_S \frac{\Pr[ A(x) \in S ]}{\Pr[ A(x') \in S] } \leq e^\varepsilon?
\Omega( \frac{\log n}{\log\log\log n})
# A TCS Quiver

## FOCS 2019 Workshop: Saturday, November 9 (Baltimore)

**8:30-8:40**Clément Canonne**8:40-10:00**Omri Weinstein*Coffee break***10:15-11:35**Anindya De*Lunch break***13:00-14:20**Steven Wu**14:20-14:30**Clément Canonne

Cell-sampling is an elementary information theoretic technique for proving unconditional lower bounds on the “locality” of algorithms, via a compression-style argument. Despite its simplicity, cell-sampling yields state-of-art lower bounds in many computational models, such as static and dynamic data structures, hashing, locally-decodable codes (LDCs) and matrix rigidity. I will sketch some of those applications, including time-space tradeoffs for near-neighbor search and the Katz–Trevisan lower bound for general LDCs.

The central limit theorem is one of the cornerstones of modern probability theory — in recent years, probably to no one's surprise, the theorem and its variants have found applications in several areas of theoretical computer science including complexity theory, learning theory and algorithmic theory among others. In this talk, I will talk about some of these variants, their applications and some approaches that are used to prove central limit theorems.

- Anindya De is an Assistant Professor at the University of Pennsylvania. Previously, he was an Assistant Professor at Northwestern University, a postdoc at IAS / DIMACS and a graduate student at Berkeley. If you ask him what is he working on, a somewhat likely response is “I have been reading about this central limit theorem ...” (and hence the talk).
- Steven Wu also is.
- Omri Weinstein is an assistant professor in Columbia University. He is interested in the interplay between information theory, complexity and data structures. He was a PhD student at Princeton University, and a Simons Society Junior Fellow at Courant Institute.