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2020 – today
- 2024
[j1]Alexander Terenin, David R. Burt, Artem Artemev, Seth R. Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge:
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees. J. Mach. Learn. Res. 25: 26:1-26:36 (2024)- [i13]Sebastian W. Ober, Artem Artemev, Marcel Wagenländer, Rudolfs Grobins, Mark van der Wilk:
Recommendations for Baselines and Benchmarking Approximate Gaussian Processes. CoRR abs/2402.09849 (2024) - 2023
- [i12]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, Stratis Markou, Jixiang Qing, Nasrulloh R. B. S. Loka, Ivo Couckuyt:
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow. CoRR abs/2302.08436 (2023) - 2022
- [c7]Artem Artemev, Yuze An, Tilman Roeder, Mark van der Wilk:
Memory safe computations with XLA compiler. NeurIPS 2022 - [i11]Artem Artemev, Tilman Roeder, Mark van der Wilk:
Memory Safe Computations with XLA Compiler. CoRR abs/2206.14148 (2022) - [i10]Alexander Terenin, David R. Burt, Artem Artemev, Seth R. Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge
:
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees. CoRR abs/2210.07893 (2022) - 2021
- [c6]Artem Artemev, David R. Burt, Mark van der Wilk:
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients. ICML 2021: 362-372 - [c5]Sattar Vakili, Henry B. Moss, Artem Artemev, Vincent Dutordoir, Victor Picheny:
Scalable Thompson Sampling using Sparse Gaussian Process Models. NeurIPS 2021: 5631-5643 - [i9]Artem Artemev, David R. Burt, Mark van der Wilk:
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients. CoRR abs/2102.08314 (2021) - [i8]Vincent Dutordoir, Hugh Salimbeni, Eric Hambro, John McLeod, Felix Leibfried, Artem Artemev, Mark van der Wilk, James Hensman, Marc Peter Deisenroth, S. T. John:
GPflux: A Library for Deep Gaussian Processes. CoRR abs/2104.05674 (2021) - [i7]David R. Burt, Artem Artemev, Mark van der Wilk:
Barely Biased Learning for Gaussian Process Regression. CoRR abs/2109.09417 (2021) - 2020
- [c4]Vincent Dutordoir, Mark van der Wilk, Artem Artemev, James Hensman:
Bayesian Image Classification with Deep Convolutional Gaussian Processes. AISTATS 2020: 1529-1539 - [c3]Vincent Adam, Stefanos Eleftheriadis, Artem Artemev, Nicolas Durrande, James Hensman:
Doubly Sparse Variational Gaussian Processes. AISTATS 2020: 2874-2884 - [c2]Victor Picheny, Vincent Dutordoir, Artem Artemev, Nicolas Durrande:
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation. ECML/PKDD (3) 2020: 431-446 - [i6]Vincent Adam, Stefanos Eleftheriadis, Nicolas Durrande, Artem Artemev, James Hensman:
Doubly Sparse Variational Gaussian Processes. CoRR abs/2001.05363 (2020) - [i5]Mark van der Wilk, Vincent Dutordoir, S. T. John, Artem Artemev, Vincent Adam, James Hensman:
A Framework for Interdomain and Multioutput Gaussian Processes. CoRR abs/2003.01115 (2020) - [i4]Sattar Vakili, Victor Picheny, Artem Artemev:
Scalable Thompson Sampling using Sparse Gaussian Process Models. CoRR abs/2006.05356 (2020) - [i3]Victor Picheny, Vincent Dutordoir, Artem Artemev, Nicolas Durrande:
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation. CoRR abs/2006.14376 (2020)
2010 – 2019
- 2019
- [c1]Mark van der Wilk, S. T. John, Artem Artemev, James Hensman:
Variational Gaussian Process Models without Matrix Inverses. AABI 2019: 1-9 - [i2]Vincent Dutordoir, Mark van der Wilk, Artem Artemev, Marcin Tomczak, James Hensman:
Translation Insensitivity for Deep Convolutional Gaussian Processes. CoRR abs/1902.05888 (2019) - [i1]Victor Picheny, Sattar Vakili, Artem Artemev:
Ordinal Bayesian Optimisation. CoRR abs/1912.02493 (2019)
Coauthor Index
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