PPT-Sublinear

Algorihms for Big Data Grigory Yaroslavtsev httpgrigoryus Lecture 1 Part 0 Introduction Disclaimers Logistics Materials Name Correct Grigory Gregory easiest and highly recommended. cornelledu Ping Li Department of Statistics and Biostatistics Department of Computer Science Rutgers University Piscataway NJ 08854 USA pinglistatrutgersedu Abstract We present the 64257rst provably sublinear time hashing algorithm for approximate Ma . Algorihms. for Big Data. Grigory. . Yaroslavtsev. http://grigory.us. Lecture 1. Part 0: Introduction. Disclaimers. Logistics. Materials. …. Name. Correct:. Grigory. Gregory (easiest and highly recommended!). Talya Eden, . Tel Aviv . University. Amit Levi, . University of Waterloo. Dana . Ron, . Tel Aviv . University. C. . Seshadhri. , . UC Santa Cruz. Counting Triangles. Basic graph-theoretic algorithmic . Huijia. Lin (USB), . Rafael Pass . (Cornell). Karn. . Seth . (Cornell -> Google). Sid . Telang. (Cornell -> Google). IO. Plethora of Applications. For example: SW14, BCP14, BZ14, GGHR14, BGL. Dana Ron . Tel-Aviv University. ADGA, October 2015. Efficient (Centralized) . Algorithms. Usually, when we say that an algorithm is . efficient . we mean that it runs in time . polynomial. in the input size . Dana Ron . Tel-Aviv University. ADGA, October 2015. Efficient (Centralized) . Algorithms. Usually, when we say that an algorithm is . efficient . we mean that it runs in time . polynomial. in the input size . Lecture . 10: . Sublinear. Algorithm. Zhu Han. University of Houston. Thanks for Professor Dan Wang’s slides. 1. outline. Motivations. Inequalities and classifications . Examples. Applications. 2. Talya Eden, . Tel Aviv . University. Amit Levi, . University of Waterloo. Dana . Ron, . Tel Aviv . University. C. . Seshadhri. , . UC Santa Cruz. Counting Triangles. Basic graph-theoretic algorithmic . COMS E6998-9. . F15. Administrivia. , Plan. Admin:. My office hours after class (CSB517). Plan:. Finalize . embeddings. Sublinear-time algorithms. Projects. Scriber?. 2. Embeddings of various metrics . 2. Alex Andoni. Plan. 2. Dimension reduction. Application: Numerical Linear Algebra. Sketching. Application: Streaming. Application: Nearest Neighbor Search. and more…. Dimension reduction: . linear . 3. Alex Andoni. Plan. 2. Dimension reduction. Application: Numerical Linear Algebra. Sketching. Application: Streaming. Application: Nearest Neighbor Search. and more…. Dimension reduction: . linear . Lecture . 10: . Sublinear. Algorithm. Zhu Han. University of Houston. Thanks for Professor Dan Wang’s slides. 1. outline. Motivations. Inequalities and classifications . Examples. Applications. 2. Niangjun Chen . Joint work with Anish Agarwal, Lachlan Andrew, . Siddharth. Barman, and Adam Wierman. 1.  .  .  .  . 2.  .  .  .  .  .  . 3.  .  .  .  .  .  .  .  . online.  . switching cost. Block Sparse Fourier Transform. Volkan. . Cevher. Michael . Kapralov. Jonathan Scarlett. Amir . Zandieh. EPFL. 1. Discrete Fourier transform. . . root of unity . Fast Fourier Transform.