News.

Paper @ NIPS: Cyclades: Asynchronous Machine Learning

August 12, 2015
NIPS,
Dec 5 - 10, 2016


Our paper on "Cyclades: Conflict-free Asynchronous Machine Learning" has been accepted at NIPS 2016, Barcelona, Spain for poster presentation. The paper is available via the publications page.

Paper @ OPT Workshop: Analysis of Async Stochastic Opt

November 10th, 2015
Paper @ OPT,
Dec 11, 2015


Our paper on "Perturbed Iterate Analysis for Asynchronous Stochastic Optimization" has been accepted at OPT workshop at NIPS for presentation on Dec 11, 2015.

Paper @ NIPS: Parallel Correlation Clustering

September 4th, 2015
NIPS,
Dec 7 - 12, 2015


Our paper on "Parallel Correlation Clustering on Big Graphs" has been accepted at NIPS 2015, Montreal, Quebec, Canada for poster on Dec 8, 2015. The paper and code are available via the publications page.

Paper @ DISCML Workshop: Parallel Correlation Clustering

November 7th, 2014
Talk @ DISCML, Dec 13

Our paper on "Scaling up Correlation Clustering through Parallelism and Concurrency Control" has been accepted at DISCML workshop at NIPS for presentation on Dec 13, 2014.

Presentation @ BayLearn 2014

October 21th, 2014
Bay Area Machine Learning Symposium

We presented our work on Parallel Double Greedy Submodular Maximization at BayLearn. This is our second presentation at BayLearn discussing the application of concurrency control techniques to parallelizing machine learning algorithms.

Paper @ NIPS: Parallel Submodular Maximization

September 9th, 2014
Poster @ NIPS, Dec 9

Our paper on "Parallel Double Greedy Submodular Maximization" has been accepted at NIPS 2014, Montreal, Quebec, Canada for poster on Dec 9, 2014. We will be making the paper and code available on this website soon.

Big Learning Workshop

August 31st, 2013
Big Learning @ NIPS, Dec 9 or 10, 2013

We're organizing the workshop on Big Learning: Advances in Algorithms and Data Management to be held at NIPS, Lake Tahoe, NV, on December 9 or 10, 2013. This year, the workshop aims to bring together the Large-scale Machine Learning and Database Systems communities to facilitate the cross-pollination of ideas.

Presentation @ BayLearn 2013

August 28th, 2013
Bay Area Machine Learning Symposium

We presented our work on Optimistic Concurrency Control for Distributed Unsupervised Learning at BayLearn. In this work we explore the application of optimistic concurrency control (OCC) to the design of scalable, provably correct machine learning algorithms, and demonstrate its application to DP-means, a novel clustering algorithm.

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