2 citations · 2 across the 1 of their papers we have counts for
4 papers
VarGrad: A Low-Variance Gradient Estimator for Variational Inference
Lorenz Richter, Ayman Boustati, Nikolas Nüsken +2
We analyse the properties of an unbiased gradient estimator of the ELBO for variational inference, based on the score function method with leave-one-out control variates. We show t…
Amortized variance reduction for doubly stochastic objectives
Ayman Boustati, Sattar Vakili, James Hensman +1
Approximate inference in complex probabilistic models such as deep Gaussian processes requires the optimisation of doubly stochastic objective functions. These objectives incorpora…
Generalized Bayesian Filtering via Sequential Monte Carlo
Ayman Boustati, Ömer Deniz Akyildiz, Theodoros Damoulas +1
We introduce a framework for inference in general state-space hidden Markov models (HMMs) under likelihood misspecification. In particular, we leverage the loss-theoretic perspecti…
Non-linear Multitask Learning with Deep Gaussian Processes
Ayman Boustati, Theodoros Damoulas, Richard S. Savage
We present a multi-task learning formulation for Deep Gaussian processes (DGPs), through non-linear mixtures of latent processes. The latent space is composed of private processes…