65 citations · 115 across the 15 of their papers we have counts for
15 papers
Improved denoising diffusion probabilistic models with efficient non-diagonal covariance modeling
Rui Xia, Ayan Das, Artem Artemev +3
The sampling process of Denoising Diffusion Probabilistic Models (DDPMs) can be accelerated by leveraging second-order information in the form of approximations to the denoising po…
Exploiting weight-space symmetries for approximating curvature
Artem Artemev, Rui Xia, Benjamin M. Boyd +4
Many machine learning techniques rely on approximating a loss function's curvature, but this is notoriously hard to do at the scale of modern deep networks. Surprisingly, no previo…
Recommendations for Baselines and Benchmarking Approximate Gaussian Processes
Sebastian W. Ober, Artem Artemev, Marcel Wagenländer +2
Gaussian processes (GPs) are a mature and widely-used component of the ML toolbox. One of their desirable qualities is automatic hyperparameter selection, which allows for training…
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
Victor Picheny, Joel Berkeley, Henry B. Moss +13
We present Trieste, an open-source Python package for Bayesian optimization and active learning benefiting from the scalability and efficiency of TensorFlow. Our library enables th…
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
Alexander Terenin, David R. Burt, Artem Artemev +4
Gaussian processes are frequently deployed as part of larger machine learning and decision-making systems, for instance in geospatial modeling, Bayesian optimization, or in latent…
Memory Safe Computations with XLA Compiler
Artem Artemev, Tilman Roeder, Mark van der Wilk
Software packages like TensorFlow and PyTorch are designed to support linear algebra operations, and their speed and usability determine their success. However, by prioritising spe…
Barely Biased Learning for Gaussian Process Regression
David R. Burt, Artem Artemev, Mark van der Wilk
Recent work in scalable approximate Gaussian process regression has discussed a bias-variance-computation trade-off when estimating the log marginal likelihood. We suggest a method…
GPflux: A Library for Deep Gaussian Processes
Vincent Dutordoir, Hugh Salimbeni, Eric Hambro +7
We introduce GPflux, a Python library for Bayesian deep learning with a strong emphasis on deep Gaussian processes (DGPs). Implementing DGPs is a challenging endeavour due to the v…
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients
Artem Artemev, David R. Burt, Mark van der Wilk
We propose a lower bound on the log marginal likelihood of Gaussian process regression models that can be computed without matrix factorisation of the full kernel matrix. We show t…
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation
Victor Picheny, Vincent Dutordoir, Artem Artemev +1
Many machine learning models require a training procedure based on running stochastic gradient descent. A key element for the efficiency of those algorithms is the choice of the le…
Scalable Thompson Sampling using Sparse Gaussian Process Models
Sattar Vakili, Henry Moss, Artem Artemev +2
Thompson Sampling (TS) from Gaussian Process (GP) models is a powerful tool for the optimization of black-box functions. Although TS enjoys strong theoretical guarantees and convin…
A Framework for Interdomain and Multioutput Gaussian Processes
Mark van der Wilk, Vincent Dutordoir, ST John +3
One obstacle to the use of Gaussian processes (GPs) in large-scale problems, and as a component in deep learning system, is the need for bespoke derivations and implementations for…
Doubly Sparse Variational Gaussian Processes
Vincent Adam, Stefanos Eleftheriadis, Nicolas Durrande +2
The use of Gaussian process models is typically limited to datasets with a few tens of thousands of observations due to their complexity and memory footprint. The two most commonly…
Ordinal Bayesian Optimisation
Victor Picheny, Sattar Vakili, Artem Artemev
Bayesian optimisation is a powerful tool to solve expensive black-box problems, but fails when the stationary assumption made on the objective function is strongly violated, which…
Bayesian Image Classification with Deep Convolutional Gaussian Processes
Vincent Dutordoir, Mark van der Wilk, Artem Artemev +1
In decision-making systems, it is important to have classifiers that have calibrated uncertainties, with an optimisation objective that can be used for automated model selection an…