Variational Gaussian Process Models without Matrix Inverses. - Mark van der Wilk; S. T. John; Artem Artemev; James Hensman (AABI, 2019)

Translation Insensitivity for Deep Convolutional Gaussian Processes. - Vincent Dutordoir; Mark van der Wilk; Artem Artemev; Marcin Tomczak; James Hensman (CoRR, 2019)

Ordinal Bayesian Optimisation. - Victor Picheny; Sattar Vakili; Artem Artemev (CoRR, 2019)

Doubly Sparse Variational Gaussian Processes. - Vincent Adam; Stefanos Eleftheriadis; Artem Artemev; Nicolas Durrande; James Hensman (AISTATS, 2020)

Bayesian Image Classification with Deep Convolutional Gaussian Processes. - Vincent Dutordoir; Mark van der Wilk; Artem Artemev; James Hensman (AISTATS, 2020)

Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation. - Victor Picheny; Vincent Dutordoir; Artem Artemev; Nicolas Durrande (ECML/PKDD (3), 2020)

A Framework for Interdomain and Multioutput Gaussian Processes. - Mark van der Wilk; Vincent Dutordoir; S. T. John; Artem Artemev; Vincent Adam; James Hensman (CoRR, 2020)

Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients. - Artem Artemev; David R. Burt; Mark van der Wilk (ICML, 2021)

Scalable Thompson Sampling using Sparse Gaussian Process Models. - Sattar Vakili; Henry B. Moss; Artem Artemev; Vincent Dutordoir; Victor Picheny (NeurIPS, 2021)

GPflux: A Library for Deep Gaussian Processes. - Vincent Dutordoir; Hugh Salimbeni; Eric Hambro; John McLeod; Felix Leibfried; Artem Artemev; Mark van der Wilk; James Hensman; Marc Peter Deisenroth; S. T. John (CoRR, 2021)

Barely Biased Learning for Gaussian Process Regression. - David R. Burt; Artem Artemev; Mark van der Wilk (CoRR, 2021)

Memory safe computations with XLA compiler. - Artem Artemev; Yuze An; Tilman Roeder; Mark van der Wilk (NeurIPS, 2022)

Memory Safe Computations with XLA Compiler. - Artem Artemev; Tilman Roeder; Mark van der Wilk (CoRR, 2022)

Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow. - Victor Picheny; Joel Berkeley; Henry B. Moss; Hrvoje Stojic; Uri Granta; Sebastian W. Ober; Artem Artemev; Khurram Ghani; Alexander Goodall; Andrei Paleyes; Sattar Vakili; Sergio Pascual-Diaz; et al. (CoRR, 2023)

Recommendations for Baselines and Benchmarking Approximate Gaussian Processes. - Sebastian W. Ober; Artem Artemev; Marcel Wagenländer; Rudolfs Grobins; Mark van der Wilk (CoRR, 2024)

Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees. - Alexander Terenin; David R. Burt; Artem Artemev; Seth R. Flaxman; Mark van der Wilk; Carl Edward Rasmussen; Hong Ge (J. Mach. Learn. Res., 2024)