In this correspondence, we present a simple argument that proves that under mild geometric assumptions on the class F and the set of target functions Τ, the empirical minimization algorithm cannot yield a uniform error rate that is faster than 1√k in
This report is a summary of the paper [BM06] of Peter Bartlett and Shahar Mendelson on Empirical Min...
We study some stability properties of algorithms which minimize (or almost-minimize) empirical error...
AbstractRecently, an algorithm for function minimization was presented, based upon an homogeneous, r...
We present an argument based on the multidimensional and the uniform central limit theorems, proving...
We investigate the behavior of the empirical minimization algorithm using various methods. We first ...
We study sample-based estimates of the expectation of the function produced by the empirical minimiz...
We study sample-based estimates of the expectation of the function produced by the empirical minimiz...
We study properties of algorithms which minimize (or almost minimize) empirical error over a Donsker...
We present sharp bounds on the risk of the empirical minimization algorithm under mild assumptions o...
We present sharp bounds on the risk of the empirical minimization algorithm under mild assumptions o...
We study sample-based estimates of the expectation of the function produced by the empirical minimiz...
We study some stability properties of algorithms which minimize (or almost-minimize) empirical error...
We study sample-based estimates of the expectation of the function produced by the empirical minimiz...
International audienceThe mathematical analysis of optimization algorithms involves upper and lower ...
The study of first-order optimization is sensitive to the assumptions made on the objective function...
This report is a summary of the paper [BM06] of Peter Bartlett and Shahar Mendelson on Empirical Min...
We study some stability properties of algorithms which minimize (or almost-minimize) empirical error...
AbstractRecently, an algorithm for function minimization was presented, based upon an homogeneous, r...
We present an argument based on the multidimensional and the uniform central limit theorems, proving...
We investigate the behavior of the empirical minimization algorithm using various methods. We first ...
We study sample-based estimates of the expectation of the function produced by the empirical minimiz...
We study sample-based estimates of the expectation of the function produced by the empirical minimiz...
We study properties of algorithms which minimize (or almost minimize) empirical error over a Donsker...
We present sharp bounds on the risk of the empirical minimization algorithm under mild assumptions o...
We present sharp bounds on the risk of the empirical minimization algorithm under mild assumptions o...
We study sample-based estimates of the expectation of the function produced by the empirical minimiz...
We study some stability properties of algorithms which minimize (or almost-minimize) empirical error...
We study sample-based estimates of the expectation of the function produced by the empirical minimiz...
International audienceThe mathematical analysis of optimization algorithms involves upper and lower ...
The study of first-order optimization is sensitive to the assumptions made on the objective function...
This report is a summary of the paper [BM06] of Peter Bartlett and Shahar Mendelson on Empirical Min...
We study some stability properties of algorithms which minimize (or almost-minimize) empirical error...
AbstractRecently, an algorithm for function minimization was presented, based upon an homogeneous, r...
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