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Найдено 15 публикаций
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Статья
Shnourkoff P. V., Novikov D. A. Working papers by Cornell University. Series math "arxiv.org". 2018. No. arXiv:1811.10993 [q-fin.GN]. P. 1-15.
Добавлено: 26 февраля 2019
Статья
Bonelli G., Gavrylenko P., Tanzini A. et al. Working papers by Cornell University. Series math "arxiv.org". 2019.
Добавлено: 13 ноября 2019
Статья
Dunin-Barkowski P., Popolitov A., Shadrin S. et al. Working papers by Cornell University. Series math "arxiv.org". 2017. Vol. 1712. No. 08614. P. 1-38.
Добавлено: 2 января 2018
Статья
Gayfullin S., Gaifullin A. A. Working papers by Cornell University. Series math "arxiv.org". 2013.
Добавлено: 15 ноября 2013
Статья
Le Gouic T., Paris Q., Rigollet P. et al. Working papers by Cornell University. Series math "arxiv.org". 2019. P. 1-17.
Добавлено: 14 ноября 2019
Статья
Polyakov N. L. Working papers by Cornell University. Series math "arxiv.org". 2018. P. 1-22.
Добавлено: 9 октября 2018
Статья
Olshanski G. Working papers by Cornell University. Series math "arxiv.org". 2020.
Добавлено: 19 января 2021
Статья
Moulines E., Durmus A., Naumov A. et al. Working papers by Cornell University. Series math "arxiv.org". 2021. No. 2102.00185. P. 1-39.

This paper studies the exponential stability of  random matrix products driven by a general (possibly unbounded) state space Markov chain. It is a  cornerstone in the analysis of stochastic algorithms in machine learning (e.g. for parameter tracking in online-learning or reinforcement learning). The existing results impose strong  conditions  such as uniform boundedness of the matrix-valued functions and uniform ergodicity of the Markov chains.  Our main contribution is an exponential stability result for the p-th moment of random matrix product, provided  that (i) the underlying Markov chain satisfies a super-Lyapunov drift condition, (ii) the growth of the matrix-valued functions is controlled by an appropriately defined function (related to the drift condition). Using this result, we give finite-time p-th moment bounds for constant and decreasing stepsize linear stochastic approximation schemes with Markovian noise on general state space. We illustrate these findings for linear value-function estimation in reinforcement learning. We provide finite-time p-th moment bound for various members of temporal difference (TD) family of algorithms.

Добавлено: 24 февраля 2021
Статья
Nikolai L. Poliakov, Saveliev D. I. Working papers by Cornell University. Series math "arxiv.org". 2018. P. 1-46.
Добавлено: 18 февраля 2019
Статья
P. V. Shnurkov. Working papers by Cornell University. Series math "arxiv.org". 2017. No. 1709.03442v1. P. 1-16.
Добавлено: 13 декабря 2017
Статья
Belomestny D., Moulines E., Naumov A. et al. Working papers by Cornell University. Series math "arxiv.org". 2021. No. 2102.00199.

We undertake a precise study of the non-asymptotic properties of vanilla generative adversarial networks (GANs) and derive theoretical guarantees in the problem of  estimating an unknown $d$-dimensional density $p$  under a proper choice of the class of generators and discriminators. We prove that the resulting  density estimate converges to $p$ in terms of  Jensen-Shannon (JS) divergence at the rate $n^{-2\beta/(2\beta+d)}$ where $n$ is the sample size and $\beta$ determines the smoothness of $p$. This is the first result in the literature on density estimation using vanilla GANs with JS rates faster than $n^{-1/2}$ in the regime $\beta>d/2$.

Добавлено: 24 февраля 2021
Статья
P. V. Shnurkov, K. A. Adamova. Working papers by Cornell University. Series math "arxiv.org". 2019. No. arXiv:1906.05824v1. P. 1-14.
Добавлено: 17 июня 2019
Статья
Paris Q. Working papers by Cornell University. Series math "arxiv.org". 2020. P. 1-17.
Добавлено: 10 февраля 2020
Статья
Belomestny D., Moulines E., Samsonov S. Working papers by Cornell University. Series math "arxiv.org". 2020.
Добавлено: 31 августа 2020
Статья
Belomestny D., Iosipoi L., Zhivotovskiy N. Working papers by Cornell University. Series math "arxiv.org". 2019.
Добавлено: 22 октября 2018