Anatoli Juditsky

TL;DR: It is intended to demonstrate that a properly modified SA approach can be competitive and even significantly outperform the SAA method for a certain class of convex stochastic problems.

TL;DR: What are the common features in the different approaches, the choices that have to be made and what considerations are relevant for a successful system-identification application of these techniques are described, from a user's perspective.

TL;DR: Convergence with probability one is proved for a variety of classical optimization and identification problems and it is demonstrated for these problems that the proposed algorithm achieves the highest possible rate of convergence.

TL;DR: Different approximation methods are considered, and the acquired approximation experience is applied to estimation problems, and wavelet and ‘neuron’ approximations are introduced, and shown to be spatially adaptive.

TL;DR: In this article, the average derivative estimator (ADE) of the index vector is used for improving the quality of gradient estimation by extending the weighting kernel in a direction of small directional derivative, and the whole procedure requires at most 2 $\log n$ iterations and the resulting estimator is $\sqrt{n}$-consistent under relatively mild assumptions on the model independently of the dimensionality.