203 citations · 1.6k across the 78 of their papers we have counts for
9 papers · 1 filter
Hybrid Population Monte Carlo
Ali Mousavi, Víctor Elvira
Importance sampling (IS) is a powerful Monte Carlo (MC) technique for approximating intractable integrals, for instance in Bayesian inference. The performance of IS relies heavily…
A Proximal Newton Adaptive Importance Sampler
Víctor Elvira, Émilie Chouzenoux, O. Deniz Akyildiz
Adaptive importance sampling (AIS) algorithms are a rising methodology in signal processing, statistics, and machine learning. An effective adaptation of the proposals is key for t…
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks
Benjamin Cox, Santiago Segarra, Victor Elvira
State-space models are a popular statistical framework for analysing sequential data. Within this framework, particle filters are often used to perform inference on non-linear stat…
GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models
Benjamin Cox, Emilie Chouzenoux, 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…
Differentiable Interacting Multiple Model Particle Filtering
John-Joseph Brady, Yuhui Luo, Wenwu Wang +2
We propose a sequential Monte Carlo algorithm for parameter learning when the studied model exhibits random discontinuous jumps in behaviour. To facilitate the learning of high dim…
Generalizing self-normalized importance sampling with couplings
Nicola Branchini, Víctor Elvira
An essential problem in statistics and machine learning is the estimation of expectations involving PDFs with intractable normalizing constants. The self-normalized importance samp…
Regime Learning for Differentiable Particle Filters
John-Joseph Brady, Yuhui Luo, Wenwu Wang +2
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 infere…
Efficient Mixture Learning in Black-Box Variational Inference
Alexandra Hotti, Oskar Kviman, Ricky Molén +2
Mixture variational distributions in black box variational inference (BBVI) have demonstrated impressive results in challenging density estimation tasks. However, currently scaling…
A divergence-based condition to ensure quantile improvement in black-box global optimization
Thomas Guilmeau, Emilie Chouzenoux, Víctor Elvira
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…