Videos: Parallel and Distributed Algorithms for Inference and Optimization

The Simons Institute at Berkeley just finished hosting the Parallel and Distributed Algorithms for Inference and Optimization workshop on Oct. 21 – Oct. 24, 2013. Here is the program, if you click on the title you should get to the abstract page that also now features a video of the talk and the attendant slides. Thanks to the Organizers: Michael Mahoney (Stanford University; chair), Guy Blelloch (Carnegie Mellon University), John Gilbert (UC Santa Barbara), Chris Ré (Stanford University), Martin Wainwright (UC Berkeley) for making it a reality.

Monday, October 21st, 2013

8:30 am – 8:50 am		Coffee and Check-In
8:50 am – 9:00 am		Opening Remarks
9:00 am – 10:00 am		MapReduce Algorithmics Sergei Vassilvitskii, Google
10:00 am – 10:30 am		Break
10:30 am – 11:00 am		Large Scale Response Prediction to Serve Ads on LinkedIn Deepak Agarwal, LinkedIn
11:00 am – 11:30 am		Three Goals in Parallel Graph Computations: High Performance, High Productivity, and Reduced Communication Aydin Buluç, Lawrence Berkeley National Laboratory
11:30 am – 12:00 pm		Scalable Graph-Parallel Inference Algorithms and Systems Joseph Gonzalez, UC Berkeley
12:00 pm – 1:45 pm		Lunch Break
1:45 pm – 2:15 pm		Cost Models for Locality and Parallelism Harsha Vardhan Simhadri, Carnegie Mellon University
2:15 pm – 2:45 pm		Extreme Multiclass John Langford, Microsoft Research New York
2:45 pm – 3:15 pm		Break
3:15 pm – 3:45 pm		Parallel Active Learning Alekh Agarwal, Microsoft Research New York
3:45 pm – 4:15 pm		MLbase: A Distributed Machine Learning System Tim Kraska, Brown University
4:15 pm – 4:45 pm		Sampling Algorithms to Count Frequent Patterns in Graphs Ali Pinar, Sandia National Laboratories

Tuesday, October 22nd, 2013

8:30 am – 9:00 am		Coffee and Check-In
9:00 am – 10:00 am		Big Data on Smallish Machines Guy Blelloch, Carnegie Mellon University
10:00 am – 10:30 am		Break
10:30 am – 11:00 am		Communication-Optimal Parallel Algorithm for Strassen’s Matrix Multiplication Grey Ballard, Sandia National Laboratories
11:00 am – 11:30 am		Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments Michael Mahoney, Stanford University
11:30 am – 12:00 pm		Tall-and-skinny QR Factorizations in MapReduce David Gleich, Purdue University
12:00 pm – 1:45 pm		Lunch Break
1:45 pm – 2:15 pm		From Sparse Matrices to Graphs and Back Again John Gilbert, UC Santa Barbara
2:15 pm – 2:45 pm		On the O(1/k) Convergence of the Asynchronous Distributed Alternating Direction Method of Multipliers Asu Ozdaglar, Massachusetts Institute of Technology and Ermin Wei, Massachusetts Institute of Technology
2:45 pm – 3:15 pm		Break
3:15 pm – 3:45 pm		Distributed Learning, Communication Complexity, and Privacy Nina Balcan, Georgia Institute of Technology
4:00 pm – 5:30 pm		Reception at Calvin Lab

Wednesday, October 23rd, 2013

8:30 am – 9:00 am		Coffee and Check-In
9:00 am – 10:00 am		Parallel Coordinate Descent Methods for Big Data Optimization Peter Richtarik, University of Edinburgh
10:00 am – 10:30 am		Break
10:30 am – 11:00 am		An Algebraic View of the New Randomized Kaczmarz Linear Solver Sivan Toledo, Tel Aviv University
11:00 am – 11:30 am		Randomized Asynchronous Linear Solvers Haim Avron, IBM T.J. Watson Research Center
11:30 am – 12:00 pm		Parallelization by Approximation Kanat Tangwongsan, IBM
12:00 pm – 1:45 pm		Lunch Break
1:45 pm – 2:15 pm		Highly Scalable Branch-and-Bound for Maximum Monomial Agreement Cynthia Phillips, Sandia National Laboratories
2:15 pm – 2:45 pm		Provably Efficient Algorithms for Training Deep Networks Shai Shalev-Shwartz, Hebrew University of Jerusalem
2:45 pm – 3:15 pm		Break
3:15 pm – 3:45 pm		Peeling Algorithms Michael Mitzenmacher, Harvard University

Thursday, October 24th, 2013

8:30 am – 9:00 am		Coffee and Check-In
9:00 am – 10:00 am		Parallelism in Linear and Mixed Integer Programming Ed Rothberg, Gurobi Optimization
10:00 am – 10:30 am		Break
10:30 am – 11:00 am		Using Data-Oblivious Algorithms for Private Cloud Storage Access Michael Goodrich, UC Irvine
11:00 am – 11:30 am		Communication Avoiding Algorithms for Linear Algebra and Beyond Jim Demmel, UC Berkeley
11:30 am – 12:00 pm		Consensus-Based Distributed Online Prediction and Optimization Michael Rabbat, McGill University
12:00 pm – 1:30 pm		Lunch Break
1:30 pm – 2:00 pm		Large-Scale Generalized Matching Rainer Gemulla, Max Planck Institute, Saarbrücken
2:00 pm – 2:30 pm		Stochastic Optimization for Big Data Machine Learning Problems Tong Zhang, Rutgers University
2:30 pm – 3:00 pm		Break
3:00 pm – 3:30 pm		First Order Methods for Distributed Network Optimization Angelia Nedich, University of Illinois, Urbana-Champaign

Credit: NASA/ESA, SOHO
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