PPT-Adam: A Method For Stochastic Optimization

Diederik P Kingma Jimmy Lei Ba Presented by Xinxin Zuo 10202017 Outline What is Adam The optimization algorithm Bias correction Bounded update Relations with Other approaches. &#x/MCI; 0 ;&#x/MCI; 0 ;STOCHASTIC PTIMIZATION &#x/Att;¬he; [/;ott;&#xom ];&#x/BBo;&#xx [1; 3;.25;“ 1; 5;
.02;t ];&#x/Sub;&#xtype;&#x /Fo;&#xote Regrets and . Kidneys. Intro to Online Stochastic Optimization. Data revealed over time. Distribution . of future events is known. Under time constraints. Limits amount of . sampling/simulation. Solve these problems with two black boxes:. Non-Convex Utilities and Costs. Michael J. Neely. University of Southern California. http://www-rcf.usc.edu/~mjneely. Information Theory and Applications Workshop (ITA), Feb. 2010. *Sponsored in part by the DARPA IT-MANET Program,. Pritam. . Sukumar. & Daphne Tsatsoulis. CS 546: Machine Learning for Natural Language Processing. 1. What is Optimization?. Find the minimum or maximum of an objective function given a set of constraints:. Lecture 3 – Part 3. M. Pawan Kumar. pawan.kumar@ecp.fr. Slides available online http://. cvn.ecp.fr. /personnel/. pawan. /. Solving Linear Programs. s.t.. A . x. ≤ . b. max. x. . c. T. x. Optimization. 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. Policies. October 25. , 2012. Warren Powell. CASTLE Laboratory. Princeton University. http://www.castlelab.princeton.edu . © . 2012 . Warren B. Powell, Princeton University. © 2012 Warren B. Powell. via Correlated Scheduling. Michael J. Neely. University of Southern California. http://www-bcf.usc.edu/~mjneely. 1. 2. Fusion . Center. Observation . ω. 1. (t). Observation . ω. 2. (t). 1. Distributed sensor reports. 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.. in Gaussian 03. Dr. Ivan Rostov. Australian National University,. Canberra. E-mail: Ivan.Rostov@anu.edu.au. Outline. Basics of ONIOM method. Overview of ONIOM features implemented. in Gaussian. 03. Anupam Gupta. Carnegie Mellon University. SODA . 2018, New Orleans. stochastic optimization. Question. : . How to . model and solve problems with . uncertainty in . input/actions?. data . not . yet . 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. Employ randomness strategically to help explore design space. Randomness can help escape local minima. Increases chance of searching near the global minimum. Typically rely on . pseudo-random number generators .