Large-scale Distributed Optimization for Improving Accuracy at the Top

In this paper, we present a large-scale distributed implementation of the accuracy at the top algorithm, which is a new notion of classification accuracy based on the top τ -quantile values of a scoring function. Our implementation approach is based on the Alternating Direction Method of Multipliers (ADMM) consensus framework, written in Pregel (a unified framework for performing large-scale graph computations, [6]) and meant for solving large scale convex optimization problems in a distributed fashion.

[1]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[2]  Yoram Singer,et al.  An Efficient Boosting Algorithm for Combining Preferences by , 2013 .

[3]  Ralf Herbrich,et al.  Large margin rank boundaries for ordinal regression , 2000 .

[4]  Alexander J. Smola,et al.  Advances in Large Margin Classifiers , 2000 .

[5]  Koby Crammer,et al.  Pranking with Ranking , 2001, NIPS.

[6]  Thorsten Joachims,et al.  Optimizing search engines using clickthrough data , 2002, KDD.

[7]  Gregory N. Hullender,et al.  Learning to rank using gradient descent , 2005, ICML.

[8]  Thomas Hofmann,et al.  Large Margin Methods for Structured and Interdependent Output Variables , 2005, J. Mach. Learn. Res..

[9]  Cynthia Rudin,et al.  Margin-Based Ranking Meets Boosting in the Middle , 2005, COLT.

[10]  Thorsten Joachims,et al.  A support vector method for multivariate performance measures , 2005, ICML.

[11]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[12]  Thomas Hofmann,et al.  Learning to Rank with Nonsmooth Cost Functions , 2006, NIPS.

[13]  Quoc V. Le.  Optimization of Ranking Measures , 2007 .

[14]  Tong Zhang,et al.  Statistical Analysis of Bayes Optimal Subset Ranking , 2008, IEEE Transactions on Information Theory.

[15]  Aart J. C. Bik,et al.  Pregel: a system for large-scale graph processing , 2010, SIGMOD Conference.

[16]  Shivani Agarwal,et al.  The Infinite Push: A New Support Vector Ranking Algorithm that Directly Optimizes Accuracy at the Absolute Top of the List , 2011, SDM.

[17]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[18]  Stephen P. Boyd,et al.  Accuracy at the Top , 2012, NIPS.