# Empirical risk minimization

## Papers overview

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Highly Cited

2018

Highly Cited

2018

- ICLR
- 2018

Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarialâ€¦Â (More)

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2018

2018

- NeurIPS
- 2018

We address the problem of algorithmic fairness: ensuring that sensitive variables do not unfairly influence the outcome of aâ€¦Â (More)

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Highly Cited

2015

Highly Cited

2015

- Journal of Machine Learning Research
- 2015

We consider a generic convex optimization problem associated with regularized empirical risk minimization of linear predictorsâ€¦Â (More)

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Highly Cited

2014

Highly Cited

2014

- IEEE 55th Annual Symposium on Foundations ofâ€¦
- 2014

Convex empirical risk minimization is a basic tool in machine learning and statistics. We provide new algorithms and matchingâ€¦Â (More)

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Highly Cited

2012

Highly Cited

2012

- 2012

We consider differentially private algorithms for convex empirical risk minimization (ERM). Differential privacy (Dwork et alâ€¦Â (More)

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Highly Cited

2011

Highly Cited

2011

- Journal of Machine Learning Research
- 2011

Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such asâ€¦Â (More)

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Highly Cited

2010

Highly Cited

2010

- 2010

Let (X,Y ) be a random couple in S Ã— T with unknown distribution P. Let (X1, Y1), . . . , (Xn,Yn) be i.i.d. copies of (X,Y ), Pnâ€¦Â (More)

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2007

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 possibleâ€¦Â (More)

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2004

2004

- COLT
- 2004

We present sharp bounds on the risk of the empirical minimization algorithm under mild assumptions on the class. We introduce theâ€¦Â (More)

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Highly Cited

1995

Highly Cited

1995

- IEEE Trans. Information Theory
- 1995

A general notion of universal consistency of nonparametric estimators is introduced that applies to regression estimationâ€¦Â (More)

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