Workshops

Fall 2013

Parallel and Distributed Algorithms for Inference and Optimization

Oct 21, 2013 to Oct 24, 2013

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Click on the titles of individual talks for abstract, slides and archived video.

All talks will take place in Sibley Auditorium, Bechtel Engineering Center, UC Berkeley. The reception on Tuesday afternoon will be held at Calvin Lab.

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