Video: https://vimeo.com/215712950 Many big data applications involve difficult combinatorial optimization that can be cast as finding a minimal graph that covers a subset of data points. Examples include computing minimal spanning trees over visual features in computer vision, finding a shortest path over an image database between image pairs, or …
Video: https://vimeo.com/216507007 The central goal of this talk is to identify parameters that explain the complexity of Integer linear programming defined as follows: Let P be a polyhedron. Determine an integral point in P that maximizes a linear function. It is obvious that the number of integer variables is such …
Video: https://vimeo.com/217685407 Physics-based modeling research in graphics has been consistently conscious of advances in modern parallel hardware, leveraging new performance capabilities to improve the scope and scale of simulation techniques. An exciting consequence of such developments is that a number of performance-hungry emerging applications, including computer-aided healthcare and medical training, …
Video: https://vimeo.com/195845240 Accelerated gradient methods play a central role in optimization, achieving optimal rates in many settings. While many generalizations and extensions of Nesterov’s original acceleration method have been proposed, it is not yet clear what is the natural scope of the acceleration concept. In this work, we study accelerated …
Video: https://vimeo.com/195638161 For commodities of a temporal nature, such as electricity and computing resources on a cloud platform, demand and supply fluctuate stochastically over time, and time-of-use pricing is an effective way to balance supply and demand. For a single time period in isolation, determining the right price to set …
Video: https://vimeo.com/193929729 Speaker 1: Justin Khim Title: Adversarial influence maximization Abstract: We consider the problem of influence maximization in fixed networks. The goal is to select a subset of nodes of a specified size to infect so that the number of infected nodes at the conclusion of the epidemic is …
Video: https://vimeo.com/192676538 A low rank matrix can be described as the outer product of two tall matrices, where the total number of variables is much smaller. One could exploit this observation in optimization: e.g., consider the minimization of a convex function f over low-rank matrices, where the low-rank set is …
Video: https://vimeo.com/192473161 Deep Learning has emerged as a singularly critical technology for enabling human-like intelligence in online services such as Azure, Office 365, Bing, Cortana, Skype, and other high-valued scenarios at Microsoft. While Deep Neural Networks (DNNs) have enabled state-of-the-art accuracy in many intelligence tasks, they are notoriously expensive and …
Video: https://vimeo.com/191080400 Classical stochastic optimization models usually involve expected-value objective functions. However, they do not apply to the minimization of a composition of two or multiple expected-value functions, i.e., the stochastic nested composition optimization problem. Stochastic composition optimization finds wide application in estimation, risk-averse optimization, dimension reduction and reinforcement learning. …