MMDS 2008. Workshop on Algorithms for Modern Massive Data Sets,
Stanford, CA, June 25–28, 2008.
MMDS 2008. Workshop on Algorithms for Modern Massive Data Sets,
Stanford, CA, June 25–28, 2008.
Reports of this workshop have appeared in the SIAM News (October 2006) and the NA Digest (July 2006):
| Time | Talk |
|---|---|
| 10:00 -11:00 | Tutorial: Ravi Kannan Sampling in large matrices |
| 11:00 -11:30 | Santosh Vempala Related paper: Matrix approximation and projective clustering via volume sampling |
| 11:30 -12:00 | Petros Drineas Subspace sampling and relative error matrix approximation |
| 1:30 - 2:30 | Tutorial: Dianne O'Leary Matrix factorizations for information retrieval |
| 2:30 - 3:00 | Pete Stewart Sparse reduced rank approximations to sparse matrices |
| 3:00 - 3:30 | Haesun Park Adaptive discriminant analysis by regularized minimum squared errors |
| 4:00 - 4:30 | Michael Mahoney CUR matrix decompositions for improved data analysis |
| 4:30 - 5:00 | Daniel Spielman Fast algorithms for graph partitioning, sparsifications, and solving SDD systems |
| 5:00 - 5:30 | Anna Gilbert/Martin Strauss List decoding of noisy Reed-Muller-like codes |
| 5:30 - 6:00 | Bob Plemmons Low-rank nonnegative factorizations for spectral imaging applications |
| 6:00 - 6:30 | Art Owen A hybrid of multivariate regression and factor analysis |
| Time | Talk |
|---|---|
| 9:00 -10:00 | Tutorial: Prabhakar Raghavan The changing face of web search |
| 10:00 -10:30 | Tong Zhang Statistical ranking problem |
| 11:00 -11:30 | Michael Berry Text-mining approaches for email surveillance |
| 11:30 -12:00 | Hongyuan Zha Incorporating query difference for learning retrieval functions |
| 12:00 -12:30 | Trevor Hastie/Ping Li Efficient L2 and L1 dimension reduction in massive databases |
| 2:00 - 3:00 | Tutorial: Muthu Muthukrishnan An algorithmer's view of sparse approximation problems |
| 3:00 - 3:30 | Inderjit Dhillon Kernel learning with Bregman matrix divergences |
| 3:30 - 4:00 | Bruce Hendrickson Latent semantic analysis and Fiedler retrieval |
| 4:30 - 5:00 | Piotr Indyk Near optimal hashing algorithms for approximate near(est) neighbor problem |
| 5:00 - 5:30 | Moses Charikar Compact data representations and their applications |
| 5:30 - 6:00 | Sudipto Guha At the confluence of streams; order, information, and signals |
| 6:00 - 6:30 | Frank McSherry Preserving privacy in large-scale data analysis |
Gene Golub, Michael Mahoney, Petros Drineas, Lek-Heng Lim
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