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Highly Cited

2016

Highly Cited

2016

The contraction inequality for Rademacher averages is extended to Lipschitz functions with vector-valued domains, and it is also… Expand

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Highly Cited

2010

Highly Cited

2010

Sequential algorithms of active learning based on the estimation of the level sets of the empirical risk are discussed in the… Expand

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Highly Cited

2009

Highly Cited

2009

We develop a technique for deriving data-dependent error bounds for transductive learning algorithms based on transductive… Expand

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Highly Cited

2008

Highly Cited

2008

This paper presents the first Rademacher complexity-based error bounds for non-i.i.d. settings, a generalization of similar… Expand

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Highly Cited

2008

Highly Cited

2008

The Fast Johnson–Lindenstrauss Transform (FJLT) was recently discovered by Ailon and Chazelle as a novel technique for performing… Expand

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Highly Cited

2006

Highly Cited

2006

Let F be a class of measurable functions f : S → [0, 1] defined on a probability space (S, A, P). Given a sample (X 1 ,..., X n… Expand

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Highly Cited

2002

Highly Cited

2002

We investigate the use of certain data-dependent estimates of the complexity of a function class, called Rademacher and Gaussian… Expand

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Highly Cited

2001

Highly Cited

2001

We suggest a penalty function to be used in various problems of structural risk minimization. This penalty is data dependent and… Expand

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Highly Cited

2000

Highly Cited

2000

We construct data dependent upper bounds on the risk in function learning problems. The bounds are based on local norms of the… Expand

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Highly Cited

1990

Highly Cited

1990

We find upper and lower bounds for Pr(Z ?x, > t), where x1, x2 . ... are real numbers. We express the answer in terms of the K… Expand

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