PPT-Uniform Sampling for Matrix Approximation

Author : olivia-moreira | Published Date : 2016-07-02

Michael Cohen Yin Tat Lee Cameron Musco Christopher Musco Richard Peng Aaron Sidford MIT Outline Reducing Row Count Row S ampling and Leverage Scores Adaptive

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Uniform Sampling for Matrix Approximation: Transcript

Michael Cohen Yin Tat Lee Cameron Musco Christopher Musco Richard Peng Aaron Sidford MIT Outline Reducing Row Count Row S ampling and Leverage Scores Adaptive Uniform Sampling. . of Edit Distance. Robert Krauthgamer, . Weizmann Institute of Science. SPIRE 2013. TexPoint. fonts used in EMF. . Read the . TexPoint. manual before you delete this box. .: . A. A. A. A. A. A. A. Ravishankar. . Krishnaswamy. (joint work with Nikhil . Bansal. and . Barna. . Saha. ). Approximating Set Cover. Given . m sets, n elements. Find . minimum cost. collection of sets . to cover . Determinantal. Assignment Problem. John . Leventides. . City University London. &. University of Athens. Tensor . Approximations (1). Rank 1 approximation of . tensors. An object of . parameters . Princeton University. Game Theory Meets. Compressed Sensing. Based on joint work with:. Volkan. Cevher. Robert. Calderbank. Rob. Schapire. Compressed Sensing. Main tasks:. Design a . sensing . matrix. Richard Peng. M.I.T.. Joint work with . Dehua. Cheng, Yu Cheng, Yan Liu and . Shanghua. . Teng. (U.S.C.). Outline. Gaussian sampling, linear systems, matrix-roots. Sparse factorizations of . L. p. Richard Peng. M.I.T.. OUtline. Structure preserving sampling. Sampling as a recursive ‘driver’. Sampling the inaccessible. What can sampling preserve?. Random Sampling. Collection of many objects. Sketching. for streaming. Alexandr. . Andoni. (MSR). Application: Streaming. IP. Frequency. 131.107.65.14. 3. 18.0.1.12. 2. 80.97.56.20. 2. 131.107.65.14. 131.107.65.14. 131.107.65.14. 18.0.1.12. 18.0.1.12. Radford M. Neal. The Annals of Statistics (Vol. 31, No. 3, 2003). Introduction. Sampling from a non-standard distribution. Metropolis algorithm is sensitive to choice of proposal distribution. Proposing changes that are too small leads to inefficient random walk. Richard Peng. M.I.T.. Joint work with . Dehua. Cheng, Yu Cheng, Yan Liu and . Shanghua. . Teng. (U.S.C.). Outline. Gaussian sampling, linear systems, matrix-roots. Sparse factorizations of . L. p. Grigory. . Yaroslavtsev. . Penn State + AT&T Labs - Research (intern). Joint work with . Berman (PSU). , . Bhattacharyya (MIT). , . Makarychev. (IBM). , . Raskhodnikova. (PSU). Directed. Spanner Problem. 1. Today’s Topics. Intro – The problem. The streaming model:. Definition. Algorithm example. Frequency moments of data streams. #Distinct Elements in a Data Stream. 2-Universal (Pairwise Independent) Hash Functions. Recall:. . To solve the (. e+ion. ) problem we compute. . ion . wavefunctions. . first, . independently using . . Superstructure or similar atomic structure code. . The coupled-channel (CC) approximation couples the free electron . Stefano Ermon. Cornell University. August 16, 2012. Joint work with Carla P. Gomes and Bart Selman. Motivation: significant progress in combinatorial reasoning. SAT/MIP: . From 100 variables, 200 constraints (early 90’s) to. Tae Jun Ham. , Sung Jun Jung, . Seonghak. Kim, Young H. Oh, . Yeonhong. Park, . Yoonho. Song, Jung-Hun Park, . Sanghee. Lee, . Kyoung. Park, Jae W. Lee, . Deog-Kyoon. . Jeong. SEOUL NATIONAL.

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