Zobrazeno 1 - 10
of 4 800
pro vyhledávání: '"Mijatović, A."'
We study the second-order asymptotics around the superdiffusive strong law~\cite{MMW} of a multidimensional driftless diffusion with oblique reflection from the boundary in a generalised parabolic domain. In the unbounded direction we prove the limit
Externí odkaz:
http://arxiv.org/abs/2412.14267
We develop two novel couplings between general pure-jump L\'evy processes in $\R^d$ and apply them to obtain upper bounds on the rate of convergence in an appropriate Wasserstein distance on the path space for a wide class of L\'evy processes attract
Externí odkaz:
http://arxiv.org/abs/2411.03609
Stochastic gradient descent is a classic algorithm that has gained great popularity especially in the last decades as the most common approach for training models in machine learning. While the algorithm has been well-studied when stochastic gradient
Externí odkaz:
http://arxiv.org/abs/2410.16340
Autor:
Mijatovic, Gorana, Antonacci, Yuri, Javorka, Michal, Marinazzo, Daniele, Stramaglia, Sebastiano, Faes, Luca
Many complex systems in science and engineering are modeled as networks whose nodes and links depict the temporal evolution of each system unit and the dynamic interaction between pairs of units, which are assessed respectively using measures of auto
Externí odkaz:
http://arxiv.org/abs/2408.15617
Quantifying the predictive capacity of a neural system, intended as the capability to store information and actively use it for dynamic system evolution, is a key component of neural information processing. Information storage (IS), the main measure
Externí odkaz:
http://arxiv.org/abs/2408.15875
Autor:
Brešar, Miha, Mijatović, Aleksandar
Denoising diffusion probabilistic models (DDPMs) represent a recent advance in generative modelling that has delivered state-of-the-art results across many domains of applications. Despite their success, a rigorous theoretical understanding of the er
Externí odkaz:
http://arxiv.org/abs/2408.13799
In this paper we consider the modeling of measurement error for fund returns data. In particular, given access to a time-series of discretely observed log-returns and the associated maximum over the observation period, we develop a stochastic model w
Externí odkaz:
http://arxiv.org/abs/2408.07405
Autor:
Trilla, Alexandre, Mijatovic, Nenad
A fundamental task in science is to determine the underlying causal relations because it is the knowledge of this functional structure what leads to the correct interpretation of an effect given the apparent associations in the observed data. In this
Externí odkaz:
http://arxiv.org/abs/2408.00399
This paper describes the development of a causal diagnosis approach for troubleshooting an industrial environment on the basis of the technical language expressed in Return on Experience records. The proposed method leverages the vectorized linguisti
Externí odkaz:
http://arxiv.org/abs/2407.20700
Autor:
Trilla, Alexandre, Rajendran, Rajesh, Yiboe, Ossee, Possamaï, Quentin, Mijatovic, Nenad, Vitrià, Jordi
This paper describes the development of a counterfactual Root Cause Analysis diagnosis approach for an industrial multivariate time series environment. It drives the attention toward the Point of Incipient Failure, which is the moment in time when th
Externí odkaz:
http://arxiv.org/abs/2407.11056