MMDS 2010.
Workshop on Algorithms for Modern Massive Data Sets,
Stanford, CA, June 15–18, 2010.
MMDS 2010.
Workshop on Algorithms for Modern Massive Data Sets,
Stanford, CA, June 15–18, 2010.
The 2008 Workshop on Algorithms for Modern Massive Data Sets (MMDS 2008) addressed algorithmic, mathematical, and statistical challenges in modern large-scale data analysis. The goals of MMDS 2008 were to explore novel techniques for modeling and analyzing massive, high-dimensional, and nonlinearly-structured scientific and internet data sets, and to bring together computer scientists, statisticians, mathematicians, and data analysis practitioners to promote cross-fertilization of ideas.
| Time | Talk |
|---|---|
| 10:00 - 11:00 | Tutorial: Christos Faloutsos Graph mining: laws, generators and tools |
| 11:00 - 11:30 | Deepak Agarwal Predictive discrete latent models for large incomplete dyadic data |
| 11:30 - 12:00 | Chandrika Kamath Scientific data mining: why is it difficult? |
| 2:00 - 3:00 | Tutorial: Edward Chang Challenges in mining large-scale social networks |
| 3:00 - 3:30 | Sharad Goel Predictive indexing for fast search |
| 3:30 - 4:00 | James Demmel Avoiding communication in linear algebra algorithms |
| 4:30 - 5:00 | Jun Liu Bayesian inference of interactions and associations |
| 5:00 - 5:30 | Fan Chung Four graph partitioning algorithms |
| 5:30 - 6:00 | Ronald Coifman Diffusion geometries and harmonic analysis on data sets |
| Time | Talk |
|---|---|
| 9:00 - 10:00 | Tutorial: Milena Mihail Models and algorithms for complex networks, with network elements maintaining characteristic profiles |
| 10:00 - 10:30 | Reid Andersen An algorithm for improving graph partitions |
| 11:00 - 11:30 | Michael W. Mahoney Community structure in large social and information networks |
| 11:30 - 12:00 | Nikhil Srivastava and Daniel Spielman Graph sparsification by effective resistances |
| 12:00 - 12:30 | Amin Saberi Sequential algorithms for generating random graphs |
| 2:30 - 3:00 | Pankaj K. Agarwal Modeling and analyzing massive terrain data sets |
| 3:00 - 3:30 | Leonidas Guibas Detection of symmetries and repeated patterns in 3D point cloud data |
| 3:30 - 4:00 | Yuan Yao Topological methods for exploring pathway analysis in complex biomolecular folding |
| 4:30 - 5:00 | Piotr Indyk Sparse recovery using sparse random matrices |
| 5:00 - 5:30 | Ping Li Compressed counting and stable random projections |
| 5:30 - 6:00 | Joel Tropp Algorithms for matrix column selection |
| Time | Talk |
|---|---|
| 9:00 - 10:00 | Tutorial: Jerome H. Friedman Fast sparse regression and classification |
| 10:00 - 10:30 | Tong Zhang An adaptive forward/backward greedy algorithm for learning sparse representations |
| 11:00 - 11:30 | Jitendra Malik Classification using intersection kernel SVMs is efficient |
| 11:30 - 12:00 | Elad Hazan Efficient online routing with limited feedback and optimization in the dark |
| 12:00 - 12:30 | T.S. Jayram Cascaded aggregates on data streams |
| 2:30 - 3:30 | Tutorial: Gunnar Carlsson Topology and data |
| 3:30 - 4:00 | Partha Niyogi Manifold regularization and semi-supervised learning |
| 4:30 - 5:00 | Sanjoy Dasgupta Random projection trees and low dimensional manifolds |
| 5:00 - 5:30 | Kenneth Clarkson Tighter bounds for random projections of manifolds |
| 5:30 - 6:00 | Yoram Singer Efficient projection algorithms for learning sparse representations from high dimensional data |
| 6:00 - 6:30 | Arindam Banerjee Bayesian co-clustering for dyadic data analysis |
| Time | Talk |
|---|---|
| 9:00 - 10:00 | Tutorial: Michael I. Jordan Sufficient dimension reduction |
| 10:00 - 10:30 | Nathan Srebro More data less work: SVM training in time decreasing with larger data sets |
| 11:00 - 11:30 | Inderjit S. Dhillon Rank minimization via online learning |
| 11:30 - 12:00 | Nir Ailon Efficient dimension reduction |
| 2:30 - 3:00 | Ravi Kannan Spectral algorithms |
| 3:00 - 3:30 | Chris Wiggins Inferring and encoding graph partitions |
| 3:30 - 4:00 | Anna Gilbert Combinatorial group testing in signal recovery |
| 4:30 - 5:00 | Lars Kai Hansen Generalization in high-dimensional matrix factorization |
| 5:00 - 5:30 | Holly Jin Exploring sparse nonnegative matrix factorization |
| 5:30 - 6:00 | Elizabeth Purdom Data analysis with graphs |
| 6:00 - 6:30 | Lek-Heng Lim Ranking via Hodge decompositions of graphs and skew-symmetric matrices |
Michael Mahoney, Stanford University
Lek-Heng Lim, University of California, Berkeley
Petros Drineas, Rensselaer Polytechnic Institute
Gunnar Carlsson, Stanford University
EMMDS 2009. European Workshop on Challenges in Modern Massive Data Sets, Technical University of Denmark, Lyngby, Denmark, July 1–4, 2009.
MMDS 2006. Workshop on Algorithms for Modern Massive Data Sets, Stanford, CA, June 21–24, 2006.
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