Systems | Information | Learning | Optimization
 

location: Orchard View Room

Digital humans, virtual surgery and fast fluids; do they have more in common than their hunger for performance?

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, …

A variational perspective for accelerated methods in optimization

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 …

Finding low-rank solutions via the Burer-Monteiro approach, efficiently and provably

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 …

Deep Learning in the Enhanced Cloud

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 …

Stochastic Nested Composition Optimization and Beyond

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. …

Mixed-Integer Convex Optimization

Video: https://vimeo.com/189163584 Mixed-integer convex optimization problems are convex problems with the additional (non-convex) constraints that some variables may take only integer values. Despite the past decades’ advances in algorithms and technology for both mixed-integer *linear* and *continuous, convex* optimization, mixed-integer convex optimization problems have remained relatively more challenging and less …