| Stochastic Gradient Descent for Gaussian Processes Done Right JA Lin, S Padhy, J Antorán, A Tripp, A Terenin, C Szepesvári, ... International Conference for Learning Representations, 2024 | | 2024 |
| A Unifying Variational Framework for Gaussian Process Motion Planning L Cosier, R Iordan, S Zwane, G Franzese, JT Wilson, MP Deisenroth, ... Artificial Intelligence and Statistics, 2024 | | 2024 |
| The Cambridge Law Corpus: A Dataset for Legal AI Research A Östling, H Sargeant, H Xie, L Bull, A Terenin, L Jonsson, M Magnusson, ... University of Cambridge Faculty of Law Research Paper, 2024 | 2* | 2024 |
| Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees A Terenin, DR Burt, A Artemev, S Flaxman, M van der Wilk, ... Journal of Machine Learning Research, 2024 | 2 | 2024 |
| Multi-objective Bayesian optimisation for design of Pareto-optimal current drive profiles in STEP T Brown, S Marsden, V Gopakumar, A Terenin, H Ge, F Casson IEEE Transactions on Plasma Physics, 2023 | 1 | 2023 |
| Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds P Rosa, V Borovitskiy, A Terenin, J Rousseau Advances in Neural Information Processing Systems, 2023 | 1 | 2023 |
| Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent JA Lin, J Antorán, S Padhy, D Janz, JM Hernández-Lobato, A Terenin Advances in Neural Information Processing Systems, 2023 | 9 | 2023 |
| Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spaces I Azangulov, A Smolensky, A Terenin, V Borovitskiy arXiv preprint arXiv:2301.13088, 2023 | 9 | 2023 |
| Stationary kernels and Gaussian processes on Lie groups and their homogeneous spaces I: the compact case I Azangulov, A Smolensky, A Terenin, V Borovitskiy arXiv preprint arXiv:2208.14960, 2022 | 13 | 2022 |
| Gaussian Processes and Statistical Decision-making in Non-Euclidean Spaces A Terenin PhD Thesis, Imperial College London, 2022 | 2 | 2022 |
| Vector-valued Gaussian processes on Riemannian manifolds via gauge independent projected kernels M Hutchinson, A Terenin, V Borovitskiy, S Takao, Y Teh, M Deisenroth Advances in Neural Information Processing Systems 34, 17160-17169, 2021 | 21 | 2021 |
| Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels N Jaquier, V Borovitskiy, A Smolensky, A Terenin, T Asfour, L Rozo Conference on Robot Learning, 2021 | 28 | 2021 |
| Learning Contact Dynamics using Physically Structured Neural Networks A Hochlehnert, A Terenin, S Sćmundsson, MP Deisenroth Artificial Intelligence and Statistics, 2021 | 23 | 2021 |
| Sliced Multi-Marginal Optimal Transport S Cohen, A Terenin, Y Pitcan, B Amos, MP Deisenroth, KSS Kumar NeurIPS Workshop on Optimal Transport and Machine Learning, 2021 | 9 | 2021 |
| Pathwise Conditioning of Gaussian Processes JT Wilson, V Borovitskiy, A Terenin, P Mostowsky, MP Deisenroth Journal of Machine Learning Research, 2021 | 44 | 2021 |
| Matérn Gaussian Processes on Graphs V Borovitskiy, I Azangulov, A Terenin, P Mostowsky, MP Deisenroth, ... Artificial Intelligence and Statistics, 2021 | 75 | 2021 |
| Aligning Time Series on Incomparable Spaces S Cohen, G Luise, A Terenin, B Amos, MP Deisenroth Artificial Intelligence and Statistics, 2021 | 18 | 2021 |
| Matérn Gaussian Processes on Riemannian manifolds V Borovitskiy, A Terenin, P Mostowsky, MP Deisenroth Advances in Neural Information Processing Systems, 2020 | 104 | 2020 |
| Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models A Terenin, M Magnusson, L Jonsson Conference on Empirical Methods in Natural Language Processing, 2020 | 1 | 2020 |
| Asynchronous Gibbs sampling A Terenin, D Simpson, D Draper Artificial Intelligence and Statistics, 144-154, 2020 | 66* | 2020 |
Viacheslav BorovitskiyPostdoctoral Fellow at ETH ZürichVerified email at inf.ethz.ch
