Saturday Morning Videos : Spectral Algorithms: From Theory to Practice (Simons Institute @ Berkeley)

 

 

I will also come back to some of the videos and slides of this workshop on Spectral Algorithms: From Theory to Practice organized at the Simons Institute at Berkeley. But in the meantime here are the links to pages that generally feature both the slides and the videos (and sometimes none of the two).
 

• Topic Modeling: A Provable Spectral Method,  Ravi Kannan, Microsoft Research India
• Exact Recovery via Convex Relaxations, Moses Charikar, Princeton University
• The Impact of Regularization in Spectral Clustering, Bin Yu, UC Berkeley
• Multiscale Analysis on and of Graphs, Mauro Maggioni, Duke University
• Multiresolution Graph Models,  Risi Kondor, University of Chicago
• Some Probabilistic Uses of Dirichlet Eigenvectors,  Persi Diaconis, Stanford University
• Independent Component Analysis: From Theory to Practice and Back, Santosh Vempala, Georgia Institute of Technology
• Tensor Methods for Learning Latent Variable Models: Theory and Practice,  Animashree Anandkumar, UC Irvine
• Random Walks on Directed Graphs,  Fan Chung, UC San Diego
• Random Embeddings, Matrix-valued Kernels and Deep Learning,  Vikas Sindhwani, IBM T.J. Watson Research Center
• A Statistical Model for Tensor Principal Component Analysis,  Andrea Montanari, Stanford University
• Graph Matching: Relax or Not?,  Alex Bronstein, Tel Aviv University
• Random Walks on Simplicial Complexes and Isoperimetric Inequalities,  Sayan Mukherjee, Duke University
• Connection Laplacian, Hodge Laplacian, and Tensor Laplacian of a Graph,  Lek-Heng Lim, University of Chicago
• Spectral Algorithms for Learning Latent Variable Models,  Sham Kakade, Microsoft Research New England
• Comparing the Theory and Practice of Spectral Algorithms to Combinatorial Flow Algorithms for Expander Ratio, Normalized Cut, Clustering and Conductance,  Dorit Hochbaum, UC Berkeley
• On the Estimation of the Cheeger Constant, Ery Arias-Castro, UC San Diego
• Applied Hodge Theory, Yuan Yao, Peking University
• The Hidden Convexity of Spectral Clustering, Luis Rademacher, Ohio State University
• Robust Spectral Diffusions for Data Applications,  David Gleich, Purdue University
• Learning Functions and Sets with Spectral Regularization,  Lorenzo Rosasco, Università di Genova and Massachusetts Institute of Technology
• Some Applications in Human Behavior Modeling,  Jerry Zhu, University of Wisconsin-Madison
• Spectral Approaches to Nearest Neighbor Search, Alexandr Andoni
• An Efficient Parallel Solver for SDD Linear Systems,  Richard Peng, Massachusetts Institute of Technology
• Graph Based Processing of Big Images,  Hui Han Chin, DSO National Laboratories

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