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Sample complexity

The sample complexity of a machine learning algorithm represents the number of training-samples that it needs in order to successfully learn a target… 
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Papers overview

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Highly Cited
2018
Highly Cited
2018
Optimal transport (OT) and maximum mean discrepancies (MMD) are now routinely used in machine learning to compare probability… 
Highly Cited
2017
Highly Cited
2017
This paper addresses the optimal control problem known as the linear quadratic regulator in the case when the dynamics are… 
Highly Cited
2014
Highly Cited
2014
In the design and analysis of revenue-maximizing auctions, auction performance is typically measured with respect to a prior… 
Highly Cited
2012
Highly Cited
2012
Intuitively, if a density operator has small rank, then it should be easier to estimate from experimental data, since in this… 
Highly Cited
2010
Highly Cited
2010
We describe and explore a new perspective on the sample complexity of active learning. In many situations where it was generally… 
Highly Cited
2010
Highly Cited
2010
The hypothesis that high dimensional data tends to lie in the vicinity of a low dimensional manifold is the basis of a collection… 
Highly Cited
2005
Highly Cited
2005
We characterize the sample complexity of active learning problems in terms of a parameter which takes into account the… 
Highly Cited
2004
Highly Cited
2004
We consider the Multi-armed bandit problem under the PAC (“probably approximately correct”) model. It was shown by Even-Dar et al… 
Highly Cited
2003
Highly Cited
2003
This thesis is a detailed investigation into the following question: how much data must an agent collect in order to perform… 
Highly Cited
1998
Highly Cited
1998
Sample complexity results from computational learning theory, when applied to neural network learning for pattern classification…