Search Results for author: Artem Artemev

Found 15 papers, 5 papers with code

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

Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees

1 code implementation14 Oct 2022 Alexander Terenin, David R. Burt, Artem Artemev, Seth Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge

For low-dimensional tasks such as geospatial modeling, we propose an automated method for computing inducing points satisfying these conditions.

Bayesian Optimization Decision Making +1

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.

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

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 Thompson Sampling

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 valid

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.

Thompson Sampling

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 regression

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.

regression

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.

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

Recommendations for Baselines and Benchmarking Approximate Gaussian Processes

no code implementations15 Feb 2024 Sebastian W. Ober, Artem Artemev, Marcel Wagenländer, Rudolfs Grobins, Mark van der Wilk

To address this, we make recommendations for comparing GP approximations based on a specification of what a user should expect from a method.

Benchmarking Gaussian Processes

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