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In this short note, we provide a sample complexity lower bound for learning linear predictors with respect to the squared loss.
The importance of convexity in learning with squared loss
W. Lee, P. Bartlett, and R. Williamson · 1998
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Neural network learning: Theoretical foundations
M. Anthony and P. Bartlett · 1999
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Relative loss bounds for on-line density estimation with the exponential family of distributions
K. Azoury and M. Warmuth · 2001
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Competitive on-line statistics
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Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems: Ecole d’Eté de Probabilités de Saint-Flour XXXVIII-2008 , volume 2033
V. Koltchinskii · 2011
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Random design analysis of ridge regression
D. Hsu, S. M Kakade, and T. Zhang · 2014
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Performance of empirical risk minimization in linear aggregation
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Suboptimality of constrained least squares and improvements via non-linear predictors
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