# On the Optimality of Sample-Based Estimates of the Expectation of the Empirical Minimizer***

• Volume: 14, page 315-337
• ISSN: 1292-8100

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## Abstract

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We study sample-based estimates of the expectation of the function produced by the empirical minimization algorithm. We investigate the extent to which one can estimate the rate of convergence of the empirical minimizer in a data dependent manner. We establish three main results. First, we provide an algorithm that upper bounds the expectation of the empirical minimizer in a completely data-dependent manner. This bound is based on a structural result due to Bartlett and Mendelson, which relates expectations to sample averages. Second, we show that these structural upper bounds can be loose, compared to previous bounds. In particular, we demonstrate a class for which the expectation of the empirical minimizer decreases as O(1/n) for sample size n, although the upper bound based on structural properties is Ω(1). Third, we show that this looseness of the bound is inevitable: we present an example that shows that a sharp bound cannot be universally recovered from empirical data.

## How to cite

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Bartlett, Peter L., Mendelson, Shahar, and Philips, Petra. "On the Optimality of Sample-Based Estimates of the Expectation of the Empirical Minimizer***." ESAIM: Probability and Statistics 14 (2010): 315-337. <http://eudml.org/doc/250853>.

@article{Bartlett2010,
abstract = { We study sample-based estimates of the expectation of the function produced by the empirical minimization algorithm. We investigate the extent to which one can estimate the rate of convergence of the empirical minimizer in a data dependent manner. We establish three main results. First, we provide an algorithm that upper bounds the expectation of the empirical minimizer in a completely data-dependent manner. This bound is based on a structural result due to Bartlett and Mendelson, which relates expectations to sample averages. Second, we show that these structural upper bounds can be loose, compared to previous bounds. In particular, we demonstrate a class for which the expectation of the empirical minimizer decreases as O(1/n) for sample size n, although the upper bound based on structural properties is Ω(1). Third, we show that this looseness of the bound is inevitable: we present an example that shows that a sharp bound cannot be universally recovered from empirical data. },
author = {Bartlett, Peter L., Mendelson, Shahar, Philips, Petra},
journal = {ESAIM: Probability and Statistics},
keywords = {Error bounds; empirical minimization; data-dependent complexity; error bounds; empirical minimization; data-dependent complexity},
language = {eng},
month = {10},
pages = {315-337},
publisher = {EDP Sciences},
title = {On the Optimality of Sample-Based Estimates of the Expectation of the Empirical Minimizer***},
url = {http://eudml.org/doc/250853},
volume = {14},
year = {2010},
}

TY - JOUR
AU - Bartlett, Peter L.
AU - Mendelson, Shahar
AU - Philips, Petra
TI - On the Optimality of Sample-Based Estimates of the Expectation of the Empirical Minimizer***
JO - ESAIM: Probability and Statistics
DA - 2010/10//
PB - EDP Sciences
VL - 14
SP - 315
EP - 337
AB - We study sample-based estimates of the expectation of the function produced by the empirical minimization algorithm. We investigate the extent to which one can estimate the rate of convergence of the empirical minimizer in a data dependent manner. We establish three main results. First, we provide an algorithm that upper bounds the expectation of the empirical minimizer in a completely data-dependent manner. This bound is based on a structural result due to Bartlett and Mendelson, which relates expectations to sample averages. Second, we show that these structural upper bounds can be loose, compared to previous bounds. In particular, we demonstrate a class for which the expectation of the empirical minimizer decreases as O(1/n) for sample size n, although the upper bound based on structural properties is Ω(1). Third, we show that this looseness of the bound is inevitable: we present an example that shows that a sharp bound cannot be universally recovered from empirical data.
LA - eng
KW - Error bounds; empirical minimization; data-dependent complexity; error bounds; empirical minimization; data-dependent complexity
UR - http://eudml.org/doc/250853
ER -

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