Empirical risk minimization

Known as: ERM 
Empirical risk minimization (ERM) is a principle in statistical learning theory which defines a family of learning algorithms and is used to give… (More)
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Highly Cited
2018
Highly Cited
2018
Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial… (More)
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2018
2018
We address the problem of algorithmic fairness: ensuring that sensitive variables do not unfairly influence the outcome of a… (More)
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Highly Cited
2015
Highly Cited
2015
We consider a generic convex optimization problem associated with regularized empirical risk minimization of linear predictors… (More)
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Highly Cited
2014
Highly Cited
2014
Convex empirical risk minimization is a basic tool in machine learning and statistics. We provide new algorithms and matching… (More)
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Highly Cited
2012
Highly Cited
2012
We consider differentially private algorithms for convex empirical risk minimization (ERM). Differential privacy (Dwork et al… (More)
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Highly Cited
2011
Highly Cited
2011
Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as… (More)
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Highly Cited
2010
Highly Cited
2010
Let (X,Y ) be a random couple in S × T with unknown distribution P. Let (X1, Y1), . . . , (Xn,Yn) be i.i.d. copies of (X,Y ), Pn… (More)
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2007
2007
Given a finite set F of estimators, the problem of aggregation is to construct a new estimator whose risk is as close as possible… (More)
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2004
2004
We present sharp bounds on the risk of the empirical minimization algorithm under mild assumptions on the class. We introduce the… (More)
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Highly Cited
1995
Highly Cited
1995
A general notion of universal consistency of nonparametric estimators is introduced that applies to regression estimation… (More)
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