Zobrazeno 1 - 10
of 166
pro vyhledávání: '"Chouzenoux, Émilie"'
Black-box global optimization aims at minimizing an objective function whose analytical form is not known. To do so, many state-of-the-art methods rely on sampling-based strategies, where sampling distributions are built in an iterative fashion, so t
Externí odkaz:
http://arxiv.org/abs/2402.01277
Adaptive importance sampling (AIS) algorithms are widely used to approximate expectations with respect to complicated target probability distributions. When the target has heavy tails, existing AIS algorithms can provide inconsistent estimators or ex
Externí odkaz:
http://arxiv.org/abs/2310.16653
In this article, we study the convergence of algorithms for solving monotone inclusions in the presence of adjoint mismatch. The adjoint mismatch arises when the adjoint of a linear operator is replaced by an approximation, due to computational or ph
Externí odkaz:
http://arxiv.org/abs/2310.06402
Ensemble learning leverages multiple models (i.e., weak learners) on a common machine learning task to enhance prediction performance. Basic ensembling approaches average the weak learners outputs, while more sophisticated ones stack a machine learni
Externí odkaz:
http://arxiv.org/abs/2310.05566
The {\lambda}-exponential family has recently been proposed to generalize the exponential family. While the exponential family is well-understood and widely used, this it not the case of the {\lambda}-exponential family. However, many applications re
Externí odkaz:
http://arxiv.org/abs/2310.05781
Autor:
Benfenati, Alessandro, Chouzenoux, Emilie, Franchini, Giorgia, Latva-Aijo, Salla, Narnhofer, Dominik, Pesquet, Jean-Christophe, Scott, Sebastian J., Yousefi, Mahsa
Several decades ago, Support Vector Machines (SVMs) were introduced for performing binary classification tasks, under a supervised framework. Nowadays, they often outperform other supervised methods and remain one of the most popular approaches in th
Externí odkaz:
http://arxiv.org/abs/2308.16858
Granger causality (GC) is often considered not an actual form of causality. Still, it is arguably the most widely used method to assess the predictability of a time series from another one. Granger causality has been widely used in many applied disci
Externí odkaz:
http://arxiv.org/abs/2307.10703
Stochastic gradient optimization methods are broadly used to minimize non-convex smooth objective functions, for instance when training deep neural networks. However, theoretical guarantees on the asymptotic behaviour of these methods remain scarce.
Externí odkaz:
http://arxiv.org/abs/2307.06987
Autor:
Chouzenoux, Emilie, Elvira, Victor
Time-series datasets are central in machine learning with applications in numerous fields of science and engineering, such as biomedicine, Earth observation, and network analysis. Extensive research exists on state-space models (SSMs), which are powe
Externí odkaz:
http://arxiv.org/abs/2307.03210
Denoising, detrending, deconvolution: usual restoration tasks, traditionally decoupled. Coupled formulations entail complex ill-posed inverse problems. We propose PENDANTSS for joint trend removal and blind deconvolution of sparse peak-like signals.
Externí odkaz:
http://arxiv.org/abs/2307.01761