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We consider unreliable distributed learning systems wherein the training data is kept confidential by external workers, and the learner has to interact closely with those workers to train a model.
Distributed Algorithms
Nancy A. Lynch · 1996
Earlier work this paper cites.
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Stephen Boyd and Lieven Vandenberghe · 2004
Earlier work this paper cites.
Introduction to machine learning
Alex Smola and SVN Vishwanathan · 2008
Earlier work this paper cites.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2009
Earlier work this paper cites.
Quantitative estimates of the convergence of the empirical covariance matrix in log-concave ensembles
Radosław Adamczak, Alexander Litvak, Alain Pajor, and Nicole Tomczak-Jaegermann · 2010
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Introduction to the non-asymptotic analysis of random matrices
Roman Vershynin · 2010
Earlier work this paper cites.
Distributed optimization and statistical learning via the alternating direction method of multipliers
Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, Jonathan Eckstein, et al · 2011
Earlier work this paper cites.
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Peter J Huber · 2011
Earlier work this paper cites.
Restricted strong convexity and weighted matrix completion: Optimal bounds with noise
Sahand Negahban and Martin J Wainwright · 2011
Earlier work this paper cites.
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T. Tao · 2012
Earlier work this paper cites.
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Yuchen Zhang, John C. Duchi, and Martin J. Wainwright · 2013
Earlier work this paper cites.
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John C. Duchi, Michael I. Jordan, and Martin J. Wainwright · 2014
Earlier work this paper cites.
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Lecture Notes on Information-theoretic Methods For High-dimensional Statistics
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