Zobrazeno 1 - 10
of 68
pro vyhledávání: '"Kalogeratos, Argyris"'
Accurate electricity demand forecasting is essential for several reasons, especially as the integration of renewable energy sources and the transition to a decentralized network paradigm introduce greater complexity and uncertainty. The proposed meth
Externí odkaz:
http://arxiv.org/abs/2408.17366
Autor:
Serré, Gaëtan, Beja-Battais, Perceval, Chirrane, Sophia, Kalogeratos, Argyris, Vayatis, Nicolas
In this paper, we propose simple yet effective empirical improvements to the algorithms of the LIPO family, introduced in [Malherbe2017], that we call LIPO+ and AdaLIPO+. We compare our methods to the vanilla versions of the algorithms over standard
Externí odkaz:
http://arxiv.org/abs/2406.19723
This paper addresses the multiple two-sample test problem in a graph-structured setting, which is a common scenario in fields such as Spatial Statistics and Neuroscience. Each node $v$ in fixed graph deals with a two-sample testing problem between tw
Externí odkaz:
http://arxiv.org/abs/2402.05715
In this paper, we present a flow-based method for global optimization of continuous Sobolev functions, called Stein Boltzmann Sampling (SBS). SBS initializes uniformly a number of particles representing candidate solutions, then uses the Stein Variat
Externí odkaz:
http://arxiv.org/abs/2402.04689
Estimating the number of clusters k while clustering the data is a challenging task. An incorrect cluster assumption indicates that the number of clusters k gets wrongly estimated. Consequently, the model fitting becomes less important. In this work,
Externí odkaz:
http://arxiv.org/abs/2312.11323
Quantifying the difference between two probability density functions, $p$ and $q$, using available data, is a fundamental problem in Statistics and Machine Learning. A usual approach for addressing this problem is the likelihood-ratio estimation (LRE
Externí odkaz:
http://arxiv.org/abs/2311.01900
The standard paired-sample testing approach in the multidimensional setting applies multiple univariate tests on the individual features, followed by p-value adjustments. Such an approach suffers when the data carry numerous features. A number of stu
Externí odkaz:
http://arxiv.org/abs/2309.16274
Online Centralized Non-parametric Change-point Detection via Graph-based Likelihood-ratio Estimation
Consider each node of a graph to be generating a data stream that is synchronized and observed at near real-time. At a change-point $\tau$, a change occurs at a subset of nodes $C$, which affects the probability distribution of their associated node
Externí odkaz:
http://arxiv.org/abs/2301.03011
This paper investigates how interacting agents arrive to a consensus or a polarized state. We study the opinion formation process under the effect of a global steering mechanism (GSM), which aggregates the opinion-driven stochastic agent states at th
Externí odkaz:
http://arxiv.org/abs/2210.12274
Publikováno v:
Artificial Neural Networks and Machine Learning - ICANN 2022: 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6-9, 2022, Proceedings, Part I. Springer-Verlag, Berlin, Heidelberg, 660-672
When analyzing a dataset, it can be useful to assess how smooth the decision boundaries need to be for a model to better fit the data. This paper addresses this question by proposing the quantification of how much should the 'rigid' decision boundari
Externí odkaz:
http://arxiv.org/abs/2210.03672