Sampling Algorithms to Count Frequent Patterns in Graphs

Channel:
United States Simons Institute for the Theory of Computing
Subscribers:
68,700
Published on ● Video Link: https://www.youtube.com/watch?v=LphxxOa6niA


Duration: 27:01
1,406 views
3

Ali Pinar, Sandia National Laboratories
Parallel and Distributed Algorithms for Inference and Optimization
http://simons.berkeley.edu/talks/ali-pinar-2013-10-21



Other Videos By Simons Institute for the Theory of Computing


2013-10-28Parallelization by Approximation
2013-10-28Randomized Asynchronous Linear Solvers
2013-10-28An Algebraic View of the New Randomized Kaczmarz Linear Solver
2013-10-28Parallel Coordinate Descent Methods for Big Data Optimization
2013-10-28Distributed Learning, Communication Complexity, and Privacy
2013-10-28On the O(1/k) Convergence of the Asynchronous Distributed Alternating Direction Method of Mult...
2013-10-28From Sparse Matrices to Graphs and Back Again
2013-10-28Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments
2013-10-28Communication-Optimal Parallel Algorithm for Strassen's Matrix Multiplication
2013-10-28Big Data on Smallish Machines
2013-10-28Sampling Algorithms to Count Frequent Patterns in Graphs
2013-10-28MLbase: A Distributed Machine Learning System
2013-10-28Extreme Multiclass
2013-10-28Cost Models for Locality and Parallelism
2013-10-28Scalable Graph-Parallel Inference Algorithms and Systems
2013-10-28Large Scale Response Prediction to Serve Ads on LinkedIn
2013-10-28MapReduce Algorithmics
2013-10-23The Effectiveness of Convex Programming in the Information and Physical Sciences
2013-10-09Influences in Gaussian Space
2013-10-09Reversing Hypercontractivity
2013-10-09A Two-Sided Estimate for the Gaussian Noise Stability Deficit


Tags:
Simons Institute
Big Data
UC Berkeley
computer science
theory of computing
Ali Pinar