## November 2018

## Functional Representation of Random variables and Applications

Abbas El Gamal (Stanford University)

32-141

The functional representation lemma says that given random variables X and Y, there exists a random variable Z, independent of X, and a function g(x,z) such that Y=g(X,Z). This lemma has had several applications in information theory aimed at simplifying computations of certain information functional. I will present a strengthened version of this lemma and applications to several one-shot coding problems. The first application is to channel simulation with common randomness, where we obtain an improved bound on the achievable…

Find out more »## October 2018

## Computing with Assemblies

Christos Papadimitriou (Columbia University)

32-155

Computation in the brain has been modeled productively at many scales, ranging from molecules to dendrites, neurons, and synapses, all the way to the whole brain models useful in cognitive science. I will discuss recent work on an intermediate layer, involving assemblies of neurons --- that is to say, sets of neurons firing together in a repetitive pattern whenever we think of a particular memory, concept or idea. Assemblies have been conjectured six decades ago by Hebb, and have been…

Find out more »## Distributed Statistical Estimation of High-Dimensional Distributions and Parameters under Communication Constraints

Ayfer Ozgur Aydin (Stanford University)

32-155

Modern data sets are often distributed across multiple machines and processors, and bandwidth and energy limitations in networks and within multiprocessor systems often impose significant bottlenecks on the performance of algorithms. Motivated by this trend, we consider the problem of estimating high-dimensional distributions and parameters in a distributed network, where each node in the network observes an independent sample from the underlying distribution and can communicate it to a central processor by writing at most k bits on a public…

Find out more »## Augmented Lagrangians and Decomposition in Convex and Nonconvex Programming

Terry Rockafellar (University of Washington)

32-155

Multiplier methods based on augmented Lagrangians are attractive in convex and nonconvex programming for their stabilizing and even convexifying properties. They have widely been seen, however, as incompatible with taking advantage of a block-separable structure. In fact, when articulated in the right way, they can produce decomposition algorithms in which low-dimensional subproblems can be solved in parallel. Convergence in the nonconvex case is, of course, just local, but is available under a broad analog of the strong second-order sufficient condition…

Find out more »## September 2018

## The Power of Multiple Samples in Generative Adversarial Networks

Sewoong Oh (University of Illinois, Urbana-Champaign)

32-155

We bring the tools from Blackwellâ€™s seminal result on comparing two stochastic experiments from 1953, to shine a new light on a modern application of great interest: Generative Adversarial Networks (GAN). Binary hypothesis testing is at the center of training GANs, where a trained neural network (called a critic) determines whether a given sample is from the real data or the generated (fake) data. By jointly training the generator and the critic, the hope is that eventually the trained generator…

Find out more »## Regret of Queueing Bandits

Sanjay Shakkotai (University of Texas, Austin)

32-155

We consider a variant of the multiarmed bandit (MAB) problem where jobs or tasks queue for service, and service rates of different servers (agents) may be unknown. Such (queueing+learning) problems are motivated by a vast range of service systems, including supply and demand in online platforms (e.g., Uber, Lyft, Airbnb, Upwork, etc.), order flow in financial markets (e.g., limit order books), communication systems, and supply chains. We study algorithms that minimize queue-regret: the expected difference between the queue-lengths (backlogs) obtained…

Find out more »## Streaming Analytics for the Smart Grid

Le Xie (Texas A&M University)

32-155

How to conduct real-time analytics of streaming measurement data in the power grid? This talk offers a dynamic systems approach to utilizing data of different time scale for improved monitoring of the grid cyber and physical security. The first example of the talk presents how to leverage synchrophasor data dimensionality reduction and Robust Principal Component Analysis for early anomaly detection, visualization, and localization. The second example presents an online framework to detect cyber-attacks on automatic generation control (AGC). A cyber-attack…

Find out more »## May 2018

## LIDS Seminar Series – Vivek Borkar

Vivek Borkar (Indian Institute of Technology Bombay)

32-141

## A Rationally Designed Biomolecular Integral Feedback Control System for Robust Gene Regulation

Mustafa Khammash ( ETH Zurich )

32-141

Abstract Humans have been influencing the DNA of plants and animals for thousands of years through selective breeding. Yet it is only over the last 3 decades or so that we have gained the ability to manipulate the DNA itself and directly alter its sequences through the modern tools of genetic engineering. This has revolutionized biotechnology and ushered in the era of synthetic biology. Among the possible applications enabled by synthetic biology is the design and engineering of feedback control…

Find out more »## April 2018

## LIDS Seminar Series: Jose M. F. Moura

Jose M F Moura (Carnegie Mellon University)

32-141