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 …
Video: https://vimeo.com/185052120 I will talk about three simple combinatorial ideas to speed up parallel and distributed learning algorithms. We will start off with serial equivalence in asynchronous parallel ML, its significance, and how we can guarantee it using a recent phase transition result on graphs. We continue on the issue …
Video: https://vimeo.com/184352560 Concepts in geometry often have parallels in information theory; for example, volume and entropy, surface area and Fisher information, sphere-packing and channel coding, and Euclidean balls and Gaussian distributions, to name a few. These similarities provide a simple way to posit theorems in one area by translating the …