Slobodan Mitrovic: Matchings in MPC frameworks Wednesday, November 29, 2017  4:00pm to 5:00pm The last decade has witnessed the success of a number of \emph{massive parallel computation} (MPC) frameworks, such as MapReduce, Hadoop, Dryad, or Spark. These frameworks allow for much more local computation, compared to the classical PRAM models. 

Jiantao Jiao: Instanceoptimal learning of the total variation distance Thursday, November 16, 2017  4:00pm to 5:00pm The total variation distance (statistical distance) plays fundamental roles in statistics, machine learning, and theoretical computer science. 

LiYang Tan: Fooling intersections of lowweight halfspaces Thursday, November 2, 2017  3:00pm to 4:00pm A weight$t$ halfspace is a Boolean function $f(x)=\mathrm{sign}(w_1 x_1 + \cdots + 

Andrej Risteski: Beyond Logconcavity: Provable Guarantees for Sampling Multimodal Distributions using Simulated Tempering Langevin Monte Carlo Wednesday, November 1, 2017  4:00pm to 5:00pm A key task in Bayesian statistics is sampling from distributions that are only specified up to a partition function (i.e., constant of proportionality). 

Pritish Kamath: NonInteractive Agreement & Dimension Reduction for Polynomials Wednesday, October 25, 2017  4:30pm to 5:30pm The "Noninteractive Agreement Distillation" problem, specified by a joint distribution P(x,y) and a target alphabet size k, is defined as follows: Two players, Alice and Bob, observe sequences (X_1, ... , X_n) and (Y_1, ... 

Lijie Chen:On The Power of Statistical Zero Knowledge Wednesday, October 11, 2017  4:00pm to 5:00pm We examine the power of statistical zero knowledge proofs (captured by the complexity class SZK) and their variants. 

Yuval Dagan: Trading Information Complexity for Error Thursday, September 28, 2017  3:00pm to 4:00pm We consider the standard twoparty communication model. The central 

Paul Hand: Deep Compressed Sensing Wednesday, September 27, 2017  4:00pm to 5:00pm Combining principles of compressed sensing with deep neural networkbased generative image priors has recently been empirically shown to require 10X fewer measurements than traditional compressed sensing in certain scenarios. 

Yuval Dagan: Detecting Correlations with Little Memory and Communication Wednesday, March 21, 2018  4:00pm to 5:00pm We study the problem of identifying correlations in multivariate data, under information constraints: Either on the amount of memory that can be used by the algorithm, or the amount of communication when the data is distributed across several machines. 

Paul Hand: Deep Compressed Sensing Wednesday, September 27, 2017  4:00pm to 5:00pm Combining principles of compressed sensing with deep neural networkbased generative image priors has recently been empirically shown to require 10X fewer measurements than traditional compressed sensing in certain scenarios. 