Gongguo ——————————– Title: Going off the grid with limited data Most of the recent activity on sparse signal recovery has focused on signals with sparse representations in finite discrete dictionaries. However, signals encountered in applications such as imaging, radar, sonar, sensor array, communication, seismology, and remote sensing are usually specified …
Aggregating data from a large number of users is integral part of the business model of many companies. As a result, people are increasingly concerned about privacy of their data. In this talk, I will present a few privacy-preserving algorithms for aggregating data from a large number of users at …
The statement that price aggregates dispersed information has been a longstanding feature underscoring the importance of markets. Yet, formalizing how exactly price may incorporate individually held information has been a challenging task. I present one particular approach to information aggregation through price. The framework is a double auction mechanism modeled …
Profesor Wright’s abstract: GROUSE is an incremental algorithm for subspace identification based on incomplete information, proposed and studied by Laura Balzano, Rob Nowak, and Ben Recht at Madison. This talk discusses recent results on the local convergence behavior of GROUSE, showing an expected linear convergence rate. Stronger results are possible …
The goal of this talk is to give some examples of metrics spaces and to look at properties of different types of maps between them. One class of examples I will talk about are delta hyperbolic spaces. These are spaces that are important in certain areas of pure mathematics but …
Quantum computation and quantum information are of great current interest in computer science, mathematics, physical sciences and engineering. They will likely lead to a new wave of technological innovations in communication, computation and cryptography. As the theory of quantum physics is fundamentally stochastic, randomness and uncertainty are deeply rooted in …
Finding models with a low-dimensional structure, given a number of linear observations much smaller than the ambient dimension, has been well-studied in recent years. Examples of such models are sparse vectors, low-rank matrices, and the sum of sparse and low-rank matrices, among others. In many signal processing and machine learning …
Gautam’s Talk: TItle: Active Learning on Graphs Label prediction on graphs, i.e., the prediction of the labels of the vertices of a given graph using the labels of a subset of vertices, is a problem that commonly occurs in many areas of machine learning and data analysis. In this talk …
We consider multistage adaptive sensing of signals and spectra where the components of interest are sparse. The advantage of adaptation in this context is the ability to focus more of the sensing budget on regions where signal components exist, thereby improving the signal-to-noise ratio. A dynamic programming formulation is derived …
Problems of packing shapes with maximal density, possibly into a container of restricted size, are classical in discrete mathematics. We describe here the problem of packing ellipsoids of given (but varying) dimensions into a finite container, in a way that minimizes the maximum overlap between adjacent ellipsoids. A bilevel optimization …