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. …
Video: https://vimeo.com/190728913 Many types of multi-dimensional data have a natural division into two “views”, such as audio and video or images and text. Multi-view learning refers to techniques that use multiple views of data to learn improved models for each of the views. Theoretical and empirical results indicate that multi-view …
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 …
Video: https://vimeo.com/189185951 Fluorescence light-sheet microscopy such as Selective Plane Illumination Microscopy (SPIM) [1] is a powerful tool to image developmental processes in vivo with high-resolution over large tissue volumes [2]. The main advantages of the technology are the minimal phototoxicity due to the confined fluorescence excitation and the fast full-frame …
Video: https://vimeo.com/185807382 Stochastic processes with dynamics at multiple time scales are pervasive in science and engineering. In neuroscience experiments, the spiking dynamics of neurons can exhibit variability both within a given trial (fast time scale) and across trials (fast time scale). The behavior of users on social media often exhibits …