11 citations · 15 across the 7 of their papers we have counts for
7 papers
Control Variate Design for Score-Function Gradient Estimators in Importance-Weighted Variational Inference
Kamélia Daudel, Minh-Ngoc Tran, Cheng Zhang
Importance-weighted variational inference approximates intractable densities by optimizing multi-sample Monte Carlo objectives whose tightness increases with the number of importan…
Learning with Importance Weighted Variational Inference
Kamélia Daudel, François Roueff
Several variational bounds involving importance weighting ideas generalize the Evidence Lower BOund (ELBO) for marginal likelihood optimization, such as the Importance-weighted Aut…
Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics
Kamélia Daudel, Joe Benton, Yuyang Shi +1
Several algorithms involving the Variational Rényi (VR) bound have been proposed to minimize an alpha-divergence between a target posterior distribution and a variational distribut…
Mixture weights optimisation for Alpha-Divergence Variational Inference
Kamélia Daudel, Randal Douc
This paper focuses on -divergence minimisation methods for Variational Inference. More precisely, we are interested in algorithms optimising the mixture weights of any given mix…
Monotonic Alpha-divergence Minimisation for Variational Inference
Kamélia Daudel, Randal Douc, François Roueff
In this paper, we introduce a novel family of iterative algorithms which carry out -divergence minimisation in a Variational Inference context. They do so by ensuring a systemat…
Infinite-dimensional gradient-based descent for alpha-divergence minimisation
Kamélia Daudel, Randal Douc, François Portier
This paper introduces the -descent, an iterative algorithm which operates on measures and performs -divergence minimisation in a Bayesian framework. This gradient-based…
The -Divergence Expectation Iteration Scheme
Kamélia Daudel, Randal Douc, François Portier +1
This paper introduces the -EI algorithm, a novel iterative algorithm which operates on measures and performs -divergence minimisation in a Bayesian framework. We prove t…