| 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 |
| Time | Talk |
|---|---|
| 9:00 -10:00 | Tutorial: Dimitris Achlioptas Applications of random matrices in spectral computations and machine learning |
| 10:00 -10:30 | Tomaso Poggio Learning: theory, engineering applications, and neuroscience |
| 11:00 -11:30 | Stephen Smale Related paper: Finding the homology of submanifolds with high confidence from random samples |
| 11:30 -12:00 | Gunnar Carlsson Algebraic topology and analysis of high dimensional data |
| 12:00 -12:30 | Vin de Silva Point-cloud topology via harmonic forms |
| 2:00 - 2:30 | Dan Boley Fast clustering leads to fast support vector machine training and more |
| 2:30 - 3:00 | Chris Ding On the equivalence of (semi-)nonnegative matrix factorization and k-means |
| 3:00 - 3:30 | Al Inselberg Parallel coordinates: visualization & data mining for high dimensiona datasets |
| 3:30 - 4:00 | Joel Tropp One sketch for all: a sublinear approximation scheme for heavy hitters |
| 5:00 - 5:30 | Rob Tibshirani Prediction by supervised principal components |
| 5:30 - 6:00 | Tao Yang/Apostolos Gerasoulis |
If you have problems downloading the slides please contact David Gleich at dgleich@stanford.edu
Updated Monday: July 6, 2006 at 10:11 PDT.