Search Results for author: Artem Artemev

Found 12 papers, 3 papers with code

Memory Safe Computations with XLA Compiler

1 code implementation28 Jun 2022 Artem Artemev, Tilman Roeder, Mark van der Wilk

We believe that further focus on removing memory constraints at a compiler level will widen the range of machine learning methods that can be developed in the future.

Improved Inverse-Free Variational Bounds for Sparse Gaussian Processes

no code implementations pproximateinference AABI Symposium 2022 Mark van der Wilk, Artem Artemev, James Hensman

The need for matrix decompositions (inverses) is often named as a major impediment to scaling Gaussian process (GP) models, even in efficient approximations.

Gaussian Processes

Barely Biased Learning for Gaussian Process Regression

no code implementations NeurIPS Workshop ICBINB 2021 David R. Burt, Artem Artemev, Mark van der Wilk

We suggest a method that adaptively selects the amount of computation to use when estimating the log marginal likelihood so that the bias of the objective function is guaranteed to be small.

Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients

no code implementations16 Feb 2021 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.

Gaussian Processes

Scalable Thompson Sampling using Sparse Gaussian Process Models

no code implementations NeurIPS 2021 Sattar Vakili, Henry Moss, Artem Artemev, Vincent Dutordoir, Victor Picheny

We provide theoretical guarantees and show that the drastic reduction in computational complexity of scalable TS can be enjoyed without loss in the regret performance over the standard TS.

A Framework for Interdomain and Multioutput Gaussian Processes

1 code implementation2 Mar 2020 Mark van der Wilk, Vincent Dutordoir, ST John, Artem Artemev, Vincent Adam, James Hensman

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 small variations in the model or inference.

Gaussian Processes

Doubly Sparse Variational Gaussian Processes

no code implementations15 Jan 2020 Vincent Adam, Stefanos Eleftheriadis, Nicolas Durrande, Artem Artemev, James Hensman

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.

Gaussian Processes

Ordinal Bayesian Optimisation

no code implementations5 Dec 2019 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 is the case in particular for ill-conditioned or discontinuous objectives.

Bayesian Optimisation

Variational Gaussian Process Models without Matrix Inverses

no code implementations pproximateinference AABI Symposium 2019 Mark van der Wilk, ST John, Artem Artemev, James Hensman

We present a variational approximation for a wide range of GP models that does not require a matrix inverse to be performed at each optimisation step.

Bayesian Image Classification with Deep Convolutional Gaussian Processes

no code implementations15 Feb 2019 Vincent Dutordoir, Mark van der Wilk, Artem Artemev, James Hensman

We also demonstrate that our fully Bayesian approach improves on dropout-based Bayesian deep learning methods in terms of uncertainty and marginal likelihood estimates.

Classification Decision Making +5

Cannot find the paper you are looking for? You can Submit a new open access paper.