Low Dimensional Manifolds Reading Group

We meet at 10:00am on Fridays in GHC 7101. This page is maintained by Don Sheehy and Babis Tsourakakis. Email Babis (ctsourak at cs.cmu.edu) with any questions or comments about this group.

5/14

Jonathan Huang will present

Fourier Theoretic Probabilistic Inference over Permutations

by Jonathan Huang, Carlos Guestrin, and Leonidas Guibas

Date	Paper	Presenter
5/14	Fourier Theoretic Probabilistic Inference over Permutations Jonathan Huang, Carlos Guestrin, and Leonidas Guibas Shorter conference version available here.	Jonathan Huang
5/7	Geometry of Convex Inequalities (continued) Yingyu Ye	Richard Peng
4/30	Geometry of Convex Inequalities Yingyu Ye	Richard Peng
4/23	Random Walks with Random Projections Purnamrita Sarkar and Geoffrey J. Gordon	Purnamrita Sarkar
4/16	Generalized Buneman pruning for inferring the most parsimonious multi-state phylogeny Navodit Misra, Guy Blelloch, R. Ravi, and Russell Schwartz	Navodit Misra
4/2	Spectral Rounding and Image Segmentation David A. Tolliver	David Tolliver
3/26	Topological Inference via Meshing (continued) Benoit Hudson, Gary L. Miller, Steve Y. Oudot, and Donald R. Sheehy	Don Sheehy
3/19	Topological Inference via Meshing Benoit Hudson, Gary L. Miller, Steve Y. Oudot, and Donald R. Sheehy	Don Sheehy
2/26	No meeting this week. Prospective students visiting!
2/19	Graph Sparsification by Effective Resistance Daniel A. Spielman and Nikhil Srivastava	Richard Peng
2/12	Minimax Estimation of Manifolds	Larry Wasserman
2/5	Colored Maximum Variance Unfolding Le Song, Alex Smola, Karsten Borgwardt, and Arthur Gretton	Le Song
1/15	Fitting a Graph to Vector Data Samuel I. Daitch, Jonathan Kelner, and Daniel A. Spielman	Samuel Daitch
12/11	An Elementary Proof of the Johnson-Lindenstrauss Lemma S. Dasgupta and A. Gupta	Richard Peng
12/4	Faster generation of random spanning trees Jonathan Kelner	Jonathan Kelner
11/20	The infinite Gaussian Mixture Model Carl Edward Rasmussen	Sarah Loos
11/6	Disk Packings and Planar Separators Dan Spielman and Sheng-Hua Teng	Todd Phillips
10/23	Discrete Laplace Operator on Meshed Surfaces Mikhail Belkin, Jian Sun, and Yusu Wang	Luis Coelho
10/9	A Unified View of Kernel k-means, Spectral Clustering and Graph Cuts Inderjit Dhillon, Yuqiang Guan and Brian Kulis	Kanat Tangwongsan
10/2	Continued from last time: Inferring Tree Models for Oncogenesis from Comparative Genome Hybridization Data Richard Desper, Feng Jiang, Olli-P. Kallioniemi, Holger Moch, Christos Papadimitriou, and Alejandro Schaffer	Babis Tsourakakis
9/18	Inferring Tree Models for Oncogenesis from Comparative Genome Hybridization Data Richard Desper, Feng Jiang, Olli-P. Kallioniemi, Holger Moch, Christos Papadimitriou, and Alejandro Schaffer	Babis Tsourakakis
9/11	Computing Betti Numbers via Combinatorial Laplacians Joel Friedman This brings together two popular topics for this reading group, Betti numbers and Laplacians. I believe it is identical, but there is also the Version from STOC.	Don Sheehy
7/16	From Graphs to Manifolds - Weak and Strong Pointwise Consistency of Graph Laplacians Hein, M., J. Audibert and U. von Luxburg See also the journal version.	Liu Yang
7/2	Triangulating Topological Spaces Herbert Edelsbrunner and Nimish R. Shah	Don Sheehy
6/25	Learning the structure of manifolds using random projections Yoav Freund, Sanjoy Dasgupta, Mayank Kabra, and Nakul Verma See also the journal version.	Liu Yang
5/28	Reconstruction Using Witness Complexes Leonidas J. Guibas and Steve Y. Oudot Their later paper with Jean-Daniel Boissonat (listed below) generalized this result to arbitrary dimensions.	Don Sheehy
5/21	Fitting a Graph to Vector Data Samuel I. Daitch, Jonathan Kelner, and Daniel A. Spielman	Babis Tsourakakis
5/14	A Duality View of Spectral Methods for Dimensionality Reduction L. Xiao, J. Sun, and S. Boyd The paper addresses Maximum Variance Unfolding in particular and relates the method to a family of spectral techniques for finding low dimensional euclidean embeddings of high dimensional data (represented by an input graph). The original paper for MVU can be found here: Unsupervised learning of image manifolds by semidefinite programming	Dave Tolliver
5/7	Continued from last week Spectral Methods	Field Cady
4/30	Spectral Methods for Dimensionality Reduction Lawrence K. Saul, Kilian Q. Weinberger, Fei Sha, Jihun Ham, and Daniel D. Lee	Field Cady
4/16	Laplacian Eigenmaps for Dimensionality Reduction and Data Representation Mikhail Belkin and Partha Niyogi	Babis Tsourakakis
4/9	Dimension Detection via Slivers Siu-Wing Cheng and Man-Kwun Chiu	Todd Phillips
4/2	Topological Persistence and Simplification Herbert Edelsbrunner, David Letscher, and Afra Zomorodian	Don Sheehy

1	Manifold Reconstruction from Point Samples Siu-Wing Cheng, Tamal K. Dey, and Edgar A. Ramos
2	Towards Persistence-Based Reconstruction in Euclidean Spaces Frederic Chazal and Steven Y. Oudot
3	Analysis of Scalar Fields over Point Cloud Data Frederic Chazal, Leonidas J. Guibas, Steven Y. Oudot, and Primoz Skraba
4	Manifold Reconstruction in Arbitrary Dimensions Using Witness Complexes Jean-Daniel Boissonnat, Leonidas J. Guibas, and Steven Y. Oudot
5	Tighter Bounds for Random Projections of Manifolds Kenneth L. Clarkson

Low Dimnesional Manifolds: A reading group on Computational Geometry, Topology, Meshing and Spectral Methods

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