PDF-Variance Reduction for Stochastic Gradient Optimization Chong Wang Xi Chen Alex Smola

Xing Carnegie Mellon University University of California Berkeley chongwxichenepxing cscmuedu alexsmolaorg Abstract Stochastic gradient optimization is a class of widely used algorithms for training machine learning models To optimize an objective i. N with state input and process noise linear noise corrupted observations Cx t 0 N is output is measurement noise 8764N 0 X 8764N 0 W 8764N 0 V all independent Linear Quadratic Stochastic Control with Partial State Obser vation 102 br smolanictacomaulesongitusydeduau MPI for Biological Cybernetics Spemannstr 38 72076 T57512ubingen Germany arthurbernhardschoelkopf tuebingenmpgde Abstract We describe a technique for comparing distributions without the need for density estimation as Gradient Descent Methods. Jakub . Kone. čný. . (joint work with Peter . Richt. árik. ). University of Edinburgh. Introduction. Large scale problem setting. Problems are often structured. Frequently arising in machine learning. Bassily. Adam Smith . Abhradeep. Thakurta. . . . . Penn State . Yahoo! Labs. . Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds. Anupam. Gupta. Carnegie Mellon University. stochastic optimization. Question: . How to model uncertainty in the inputs?. data may not yet be available. obtaining exact data is difficult/expensive/time-consuming. Stochastic Calculus: Introduction . Although . stochastic . and ordinary calculus share many common properties, there are fundamental differences. The probabilistic nature of stochastic processes distinguishes them from the deterministic functions associated with ordinary calculus. Since stochastic differential equations so frequently involve Brownian motion, second order terms in the Taylor series expansion of functions become important, in contrast to ordinary calculus where they can be ignored. . P. Bryan . Heidorn. heidorn@email.arizona.edu. . Steven Chong. stevenchong@email.arizona.edu. University of Arizona, School of Information Resources and Library Science. Semantics for Biodiversity Symposium – TDWG 2013 Annual Conference. Courtney Schumacher for Brian’s mesoscale . class. First tropical radar bright band. Murty. et al. (1965). African squall line evolution. Chong et al. (1987). African squall line (mature stage). Chong et al. (1987). “Back of the Napkin”. Wayne Zage, Center Director. ●. “My First IAB Meeting with Alex”. Don Price, NSF Program Manager. ●. “A Detail Man”. Craig Scott, Assessment Coordinator. ●. “Alex, My Mentor”. Lecture 4. September 12, 2016. School of Computer Science. Readings:. Murphy Ch. . 8.1-3, . 8.6. Elken (2014) Notes. 10-601 Introduction to Machine Learning. Slides:. Courtesy William Cohen. Reminders. Diederik. P. . Kingma. . Jimmy Lei Ba. Presented by . Xinxin. . Zuo. 10/20/2017. Outline. What is Adam. The optimization algorithm. . Bias correction. Bounded . update. Relations with Other approaches. relaxations. via statistical query complexity. Based on:. V. F.. , Will Perkins, Santosh . Vempala. . . On the Complexity of Random Satisfiability Problems with Planted . Solutions.. STOC 2015. V. F.. . storage. . with. . stochastic. . consumption. and production. Erwan Pierre – EDF R&D. SESO 2018 International Thematic . Week. - . Smart Energy and Stochastic Optimization . High . penetration. Project: IEEE P802.15 Working Group for Wireless Personal Area Networks (WPANs). Submission Title: . pureLiFi’s. Proposals for LB-OFDM PHY . Date Submitted: . 27 April 2018 . Source:. Nikola Serafimovski, Chong Han, Stephan Berner, Mostafa Afgani (.