LIDS Seminar Series

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…

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…

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…

Electric power: done wrong, it drags the economy and environment down; done right, it could help to create a more efficient, brighter, and cleaner future. Better policy, planning, and operations models--both simple analytical, and complex computational ones--are essential if we're going to do it right. Better modeling is also fun, as the math of electricity…

Resilience of a transportation system is its ability to operate under adverse events like incidents and storms. Availability of real-time traffic data provides new opportunities for predicting travelers’ routing behavior and implementing network control operations during adverse events. In this talk, we will discuss two problems: controlling highway corridors in response to disruptions and modeling…

Prof. Leonard will present nonlinear dynamics for distributed decision-making that derive from principles of symmetry and bifurcation. Inspired by studies of animal groups, including house-hunting honeybees and schooling fish, the nonlinear dynamics describe a group of interacting agents that can manage flexibility as well as stability in response to a changing environment. Bio: Prof. Naomi…

The increasing ubiquity of large-scale infrastructures for surveillance and data analysis has made understanding the impact of privacy a pressing priority in many domains. We propose a framework for studying a fundamental cost vs. privacy tradeoff in dynamic decision-making problems. The central question is: how can a decision maker take actions that are efficient for…

This talk introduces "Coded Computing”, a new framework that brings concepts and tools from information theory and coding into distributed computing to mitigate several performance bottlenecks that arise in large-scale distributed computing and machine learning, such as resiliency to stragglers and bandwidth bottleneck. Furthermore, coded computing can enable (information-theoretically) secure and private learning over untrusted…

Joint work with Adrien Taylor (INRIA) and Francois Glineur (UCLouvain). We show that the exact worst-case performances of a wide class of first-order convex optimization algorithms can be obtained as solutions to semi-definite programs, which provide both the performance bounds and functions on which these are reached.  Our formulation is based on a necessary and…

We consider a seller who can dynamically adjust the price of a product at the individual customer level, by utilizing information about customers’ characteristics encoded as a $d$-dimensional feature vector. We assume a personalized demand model, parameters of which depend on $s$ out of the $d$ features. The seller initially does not know the relationship…