Search Results for author:
Found 17 papers, 9 papers with code
Robust and Conjugate Gaussian Process Regression
To enable closed form conditioning, a common assumption in Gaussian process (GP) regression is independent and identically distributed Gaussian observation noise.
A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods
We establish the first mathematically rigorous link between Bayesian, variational Bayesian, and ensemble methods.
Robust and Scalable Bayesian Online Changepoint Detection
This paper proposes an online, provably robust, and scalable Bayesian approach for changepoint detection.
Generalised Bayesian Inference for Discrete Intractable Likelihood
Discrete state spaces represent a major computational challenge to statistical inference, since the computation of normalisation constants requires summation over large or possibly infinite sets, which can be impractical.
Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap
Simulator-based models are models for which the likelihood is intractable but simulation of synthetic data is possible.
Uncertainty-aware deep learning methods for robust diabetic retinopathy classification
Automatic classification of diabetic retinopathy from retinal images has been widely studied using deep neural networks with impressive results.
Robust Generalised Bayesian Inference for Intractable Likelihoods
Generalised Bayesian inference updates prior beliefs using a loss function, rather than a likelihood, and can therefore be used to confer robustness against possible mis-specification of the likelihood.
Generalized Posteriors in Approximate Bayesian Computation
Approximate Bayesian computation (ABC) has emerged as a key method in simulation-based inference, wherein the true model likelihood and posterior are approximated using samples from the simulator.
Transforming Gaussian Processes With Normalizing Flows
Gaussian Processes (GPs) can be used as flexible, non-parametric function priors.
Robust Bayesian Inference for Discrete Outcomes with the Total Variation Distance
Models of discrete-valued outcomes are easily misspecified if the data exhibit zero-inflation, overdispersion or contamination.
Optimal Continual Learning has Perfect Memory and is NP-hard
Continual Learning (CL) algorithms incrementally learn a predictor or representation across multiple sequentially observed tasks.
Frequentist Consistency of Generalized Variational Inference
This paper investigates Frequentist consistency properties of the posterior distributions constructed via Generalized Variational Inference (GVI).
Robust Deep Gaussian Processes
This report provides an in-depth overview over the implications and novelty Generalized Variational Inference (GVI) (Knoblauch et al., 2019) brings to Deep Gaussian Processes (DGPs) (Damianou & Lawrence, 2013).
Generalized Variational Inference: Three arguments for deriving new Posteriors
We advocate an optimization-centric view on and introduce a novel generalization of Bayesian inference.
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with \beta-Divergences
The resulting inference procedure is doubly robust for both the predictive and the changepoint (CP) posterior, with linear time and constant space complexity.
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with $β$-Divergences
The resulting inference procedure is doubly robust for both the parameter and the changepoint (CP) posterior, with linear time and constant space complexity.
Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection
Bayesian On-line Changepoint Detection is extended to on-line model selection and non-stationary spatio-temporal processes.
