Zobrazeno 1 - 10
of 257
pro vyhledávání: '"Arzamasov, A. A."'
Autor:
Arzamasov, Vadim, Böhm, Klemens
In smart grids, the use of smart meters to measure electricity consumption at a household level raises privacy concerns. To address them, researchers have designed various load hiding algorithms that manipulate the electricity consumption measured. T
Externí odkaz:
http://arxiv.org/abs/2408.06460
Autor:
Matteucci, Federico, Arzamasov, Vadim, Cribeiro-Ramallo, Jose, Heyden, Marco, Ntounas, Konstantin, Böhm, Klemens
Experimental studies are a cornerstone of machine learning (ML) research. A common, but often implicit, assumption is that the results of a study will generalize beyond the study itself, e.g. to new data. That is, there is a high probability that rep
Externí odkaz:
http://arxiv.org/abs/2406.17374
Outlier detection in high-dimensional tabular data is an important task in data mining, essential for many downstream tasks and applications. Existing unsupervised outlier detection algorithms face one or more problems, including inlier assumption (I
Externí odkaz:
http://arxiv.org/abs/2404.14451
Outlier generation is a popular technique used for solving important outlier detection tasks. Generating outliers with realistic behavior is challenging. Popular existing methods tend to disregard the 'multiple views' property of outliers in high-dim
Externí odkaz:
http://arxiv.org/abs/2402.03846
Autor:
Yuriy G. Arzamasov
Publikováno v:
RUDN Journal of Law, Vol 28, Iss 4, Pp 954-959 (2024)
Externí odkaz:
https://doaj.org/article/92551b6a038d4e72bed892abe747efc4
Categorical encoders transform categorical features into numerical representations that are indispensable for a wide range of machine learning models. Existing encoder benchmark studies lack generalizability because of their limited choice of (1) enc
Externí odkaz:
http://arxiv.org/abs/2307.09191
Autor:
Heyden, Marco, Fouché, Edouard, Arzamasov, Vadim, Fenn, Tanja, Kalinke, Florian, Böhm, Klemens
Change detection is of fundamental importance when analyzing data streams. Detecting changes both quickly and accurately enables monitoring and prediction systems to react, e.g., by issuing an alarm or by updating a learning algorithm. However, detec
Externí odkaz:
http://arxiv.org/abs/2306.12974
We study the stochastic Budgeted Multi-Armed Bandit (MAB) problem, where a player chooses from $K$ arms with unknown expected rewards and costs. The goal is to maximize the total reward under a budget constraint. A player thus seeks to choose the arm
Externí odkaz:
http://arxiv.org/abs/2306.07071
Machine-learning models are ubiquitous. In some domains, for instance, in medicine, the models' predictions must be interpretable. Decision trees, classification rules, and subgroup discovery are three broad categories of supervised machine-learning
Externí odkaz:
http://arxiv.org/abs/2112.13285
Autor:
Yurii A. Vasilev, Inna V. Goncharova, Anton V. Vladzymyrskii, Igor M. Shulkin, Kirill M. Arzamasov
Publikováno v:
Наука и инновации в медицине, Vol 8, Iss 4, Pp 271-280 (2023)
Aim to study the prevalence of paracardial fat as a risk factor for cardiovascular diseases in Moscow population using an automated analysis of the results of radiological examinations. Material and methods. The research was designed as descriptiv
Externí odkaz:
https://doaj.org/article/19862d5d2790445b8e3011af7d05db00