Zobrazeno 1 - 10
of 2 433
pro vyhledávání: '"Elvira, Victor"'
Autor:
Branchini, Nicola, Elvira, Víctor
An essential problem in statistics and machine learning is the estimation of expectations involving PDFs with intractable normalizing constants. The self-normalized importance sampling (SNIS) estimator, which normalizes the IS weights, has become the
Externí odkaz:
http://arxiv.org/abs/2406.19974
Mixture variational distributions in black box variational inference (BBVI) have demonstrated impressive results in challenging density estimation tasks. However, currently scaling the number of mixture components can lead to a linear increase in the
Externí odkaz:
http://arxiv.org/abs/2406.07083
Differentiable particle filters are an emerging class of models that combine sequential Monte Carlo techniques with the flexibility of neural networks to perform state space inference. This paper concerns the case where the system may switch between
Externí odkaz:
http://arxiv.org/abs/2405.04865
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
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
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
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
Autor:
Cox, Benjamin, Elvira, Victor
Publikováno v:
IEEE Transactions on Signal Processing, vol. 71, pp. 1922-1937, 2023
State-space models (SSMs) are a powerful statistical tool for modelling time-varying systems via a latent state. In these models, the latent state is never directly observed. Instead, a sequence of data points related to the state are obtained. The l
Externí odkaz:
http://arxiv.org/abs/2306.11652
Autor:
Chouzenoux, Emilie, Elvira, Victor
Publikováno v:
Proceedings of ICASSP 2023
State-space models (SSMs) are a common tool for modeling multi-variate discrete-time signals. The linear-Gaussian (LG) SSM is widely applied as it allows for a closed-form solution at inference, if the model parameters are known. However, they are ra
Externí odkaz:
http://arxiv.org/abs/2303.12569