4 citations · 7 across the 17 of their papers we have counts for
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A mirror descent approach to maximum likelihood estimation in latent variable models
Francesca R. Crucinio
We introduce an approach based on mirror descent and sequential Monte Carlo (SMC) to perform joint parameter inference and posterior estimation in latent variable models. This appr…
Solving Fredholm Integral Equations of the Second Kind via Wasserstein Gradient Flows
Francesca R. Crucinio, Adam M. Johansen
Motivated by a recent method for approximate solution of Fredholm equations of the first kind, we develop a corresponding method for a class of Fredholm equations of the \emph{seco…
Proximal Interacting Particle Langevin Algorithms
Paula Cordero Encinar, Francesca R. Crucinio, O. Deniz Akyildiz
We introduce a class of algorithms, termed proximal interacting particle Langevin algorithms (PIPLA), for inference and learning in latent variable models whose joint probability d…
A connection between Tempering and Entropic Mirror Descent
Nicolas Chopin, Francesca R. Crucinio, Anna Korba
This paper explores the connections between tempering (for Sequential Monte Carlo; SMC) and entropic mirror descent to sample from a target probability distribution whose unnormali…
Properties of Marginal Sequential Monte Carlo Methods
Francesca R. Crucinio, Adam M. Johansen
We provide a framework which admits a number of ``marginal'' sequential Monte Carlo (SMC) algorithms as particular cases -- including the marginal particle filter [Klaas et al., 20…
Interacting Particle Langevin Algorithm for Maximum Marginal Likelihood Estimation
Ö. Deniz Akyildiz, Francesca Romana Crucinio, Mark Girolami +2
We develop a class of interacting particle systems for implementing a maximum marginal likelihood estimation (MMLE) procedure to estimate the parameters of a latent variable model.…