166 citations · 309 across the 26 of their papers we have counts for
6 papers · 1 filter
Improving Ensemble Filters with Flow Matching
Haoyuan Chen, Alexandre Thiéry
Data assimilation estimates a dynamical state from partial and noisy observations. Classical ensemble filters are efficient but restrict analysis updates through finite sample cova…
Computational Doob's h-transforms for Online Filtering of Discretely Observed Diffusions
Nicolas Chopin, Andras Fulop, Jeremy Heng +1
This paper is concerned with online filtering of discretely observed nonlinear diffusion processes. Our approach is based on the fully adapted auxiliary particle filter, which invo…
On Data-Augmentation and Consistency-Based Semi-Supervised Learning
Atin Ghosh, Alexandre H. Thiery
Recently proposed consistency-based Semi-Supervised Learning (SSL) methods such as the -model, temporal ensembling, the mean teacher, or the virtual adversarial training, have a…
Uncertainty Quantification and Deep Ensembles
Rahul Rahaman, Alexandre H. Thiery
Deep Learning methods are known to suffer from calibration issues: they typically produce over-confident estimates. These problems are exacerbated in the low data regime. Although…
On importance-weighted autoencoders
Axel Finke, Alexandre H. Thiery
The importance weighted autoencoder (IWAE) (Burda et al., 2016) is a popular variational-inference method which achieves a tighter evidence bound (and hence a lower bias) than stan…
Consistency and fluctuations for stochastic gradient Langevin dynamics
Yee Whye Teh, Alexandre Thiéry, Sebastian Vollmer
Applying standard Markov chain Monte Carlo (MCMC) algorithms to large data sets is computationally expensive. Both the calculation of the acceptance probability and the creation of…