203 citations · 1.6k across the 78 of their papers we have counts for
8 papers · 1 filter
Deep State-Space Model for Predicting Cryptocurrency Price
Shalini Sharma, Angshul Majumdar, Emilie Chouzenoux +1
Our work presents two fundamental contributions. On the application side, we tackle the challenging problem of predicting day-ahead crypto-currency prices. On the methodological si…
Adaptive importance sampling for heavy-tailed distributions via -divergence minimization
Thomas Guilmeau, Nicola Branchini, Emilie Chouzenoux +1
Adaptive importance sampling (AIS) algorithms are widely used to approximate expectations with respect to complicated target probability distributions. When the target has heavy ta…
On variational inference and maximum likelihood estimation with the λ-exponential family
Thomas Guilmeau, Emilie Chouzenoux, Víctor Elvira
The λ-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 t…
Graphs in State-Space Models for Granger Causality in Climate Science
Víctor Elvira, Émilie Chouzenoux, Jordi Cerdà +1
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 ano…
Sparse Graphical Linear Dynamical Systems
Emilie Chouzenoux, Victor Elvira
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.…
Sparse Bayesian Estimation of Parameters in Linear-Gaussian State-Space Models
Benjamin Cox, Victor Elvira
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. Inste…
GraphIT: Iterative reweighted algorithm for sparse graph inference in state-space models
Emilie Chouzenoux, Victor Elvira
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…
Differentiable Bootstrap Particle Filters for Regime-Switching Models
Wenhan Li, Xiongjie Chen, Wenwu Wang +2
Differentiable particle filters are an emerging class of particle filtering methods that use neural networks to construct and learn parametric state-space models. In real-world app…