The goal of this workshop is to introduce and discuss some general proof techniques or paradigms which
have had and could have applications spanning many subfields of TCS. The objective is for attendees to learn about approaches or “tricks” that could prove useful to them, and of which
they might not have been aware before: questions and discussions are strongly encouraged!
- 8:30-8:40 Clément Canonne
- 8:40-10:00 Omri Weinstein
Lower Bounds via the Cell-Sampling Method
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.
- Coffee break
- 10:15-11:35 Anindya De
Quantitative Central Limit Theorems
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.
- Lunch break
- 13:00-14:20 Steven Wu
Differential Privacy Techniques Beyond Differential Privacy
- 14:20-14:30 Clément Canonne
(Short) concluding remarks
- 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.
Organizers and support
This workshop is organized by Clément Canonne and Gautam Kamath, with the support of the FOCS Tutorial and Workshop chairs Nina Balcan and Bobby Kleinberg.