PPT-Near-Optimal Algorithms for Online Matrix Prediction

Elad Hazan Technion Satyen Kale Yahoo Labs Shai ShalevShwartz Hebrew University Three Prediction Problems I Online Collaborative Filtering Users 1 2 m Movies . for Linear Algebra and Beyond. Jim . Demmel. EECS & Math Departments. UC Berkeley. 2. Why avoid communication? (1/3). Algorithms have two costs (measured in time or energy):. Arithmetic (FLOPS). Communication: moving data between . . Dynamic Programming. CSE 680. Prof. Roger Crawfis. Fibonacci Numbers. . Computing the n. th. Fibonacci number recursively:. F(n) = F(n-1) + F(n-2). F(0) = 0. F(1) = 1. Top-down approach. . F. Optimization problems, Greedy Algorithms, Optimal Substructure and Greedy choice. Learning & Development Team. http://academy.telerik.com. . Telerik Software Academy. Table of Contents. Optimization Problems. Strassen's. Matrix Multiplication . Algorithms. . Sarah M. . Loos. . Undergraduate, Computer Science, Indiana University, smloos@indiana.edu . A very simple recasting of this classic 7-multiplication recursion improves its time performance for rectangular matrices of order . . Analysis of Algorithms. . Prof. Karen Daniels. . Design Patterns . for . Optimization Problems. Dynamic . Programming. Matrix Parenthesizing. Longest Common Subsequence. Activity Selection. Algorithmic Paradigm Context. Amrinder Arora. Permalink: http://standardwisdom.com/softwarejournal/presentations/. Summary. Online algorithms show up in . many. practical problems.. Even if you are considering an offline problem, consider what would be the online version of that problem.. Anne Reynolds. What is Online Dating?. Online Dating . is, “a . dating system which allows individuals, couples and groups to make contact and communicate with each other over the Internet, usually with the objective of developing a personal, romantic, or sexual . Prediction is important for action selection. The problem:. prediction of future reward. The algorithm:. temporal difference learning. Neural implementation:. dopamine dependent learning in BG. A precise computational model of learning allows one to look in the brain for “hidden variables” postulated by the model. Richard Peng. Georgia Tech. OUtline. (Structured) Linear Systems. Iterative and Direct Methods. (. Graph) . Sparsification. Sparsified. Squaring. Speeding up Gaussian Elimination. Graph Laplacians. 1. Richard Peng. Georgia Tech. OUtline. (Structured) Linear Systems. Iterative and Direct Methods. (. Graph) . Sparsification. Sparsified. Squaring. Speeding up Gaussian Elimination. Graph Laplacians. 1. Niangjun Chen . Joint work with Anish Agarwal, Lachlan Andrew, . Siddharth. Barman, and Adam Wierman. 1.  .  .  .  . 2.  .  .  .  .  .  . 3.  .  .  .  .  .  .  .  . online.  . switching cost. Jeff Chen. , Abe Dunn, Kyle Hood, . Alex Driessen and Andrea Batch. Motivation. 2. End of. Quarter. Advance. Estimate. Second. Estimate. When source . data are available. When we’d. like it to be available. Ravishankar Krishnaswamy. Microsoft Research India. Based on joint works Anupam Gupta, Varun Gupta, Amit Kumar, Janardhan Kulkarni, Debmalya Panigrahi, Sai Sandeep. PART I: ONLINE SET COVER. The Set Cover Problem. Jim . Demmel. EECS & Math Departments. UC Berkeley. Why avoid communication? . Communication = moving data. Between level of memory hierarchy. Between processors over a network. Running time of an algorithm is sum of 3 terms:.