Search Results for author:
Found 12 papers, 3 papers with code
Adaptive importance sampling for heavy-tailed distributions via $α$-divergence minimization
We then show that the $\alpha$-divergence can be approximated by a generalized notion of effective sample size and leverage this new perspective to adapt the tail parameter with Bayesian optimization.
Graphs in State-Space Models for Granger Causality in Climate Science
Granger causality (GC) is often considered not an actual form of causality.
Differentiable Bootstrap Particle Filters for Regime-Switching Models
Differentiable particle filters are an emerging class of particle filtering methods that use neural networks to construct and learn parametric state-space models.
Cooperation in the Latent Space: The Benefits of Adding Mixture Components in Variational Autoencoders
In this work, we also demonstrate that increasing the number of mixture components improves the latent-representation capabilities of the VAE on both image and single-cell datasets.
Hamiltonian Adaptive Importance Sampling
Importance sampling (IS) is a powerful Monte Carlo (MC) methodology for approximating integrals, for instance in the context of Bayesian inference.
Multiple Importance Sampling ELBO and Deep Ensembles of Variational Approximations
In variational inference (VI), the marginal log-likelihood is estimated using the standard evidence lower bound (ELBO), or improved versions as the importance weighted ELBO (IWELBO).
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing
In many inference problems, the evaluation of complex and costly models is often required.
Compressed Monte Carlo with application in particle filtering
In its basic version, C-MC is strictly related to the stratification technique, a well-known method used for variance reduction purposes.
Optimized Auxiliary Particle Filters: adapting mixture proposals via convex optimization
In this work, we propose optimized auxiliary particle filters, a framework where the traditional APF auxiliary variables are interpreted as weights in an importance sampling mixture proposal.
Computation
A probabilistic incremental proximal gradient method
In this paper, we propose a probabilistic optimization method, named probabilistic incremental proximal gradient (PIPG) method, by developing a probabilistic interpretation of the incremental proximal gradient algorithm.
On the Relationship between Online Gaussian Process Regression and Kernel Least Mean Squares Algorithms
We study the relationship between online Gaussian process (GP) regression and kernel least mean squares (KLMS) algorithms.
