| Efficiently Sampling Functions from Gaussian Process Posteriors JT Wilson, V Borovitskiy, A Terenin, P Mostowsky, MP Deisenroth International Conference on Machine Learning, 10292-10302, 2020 | 162 | 2020 |
| Matérn Gaussian Processes on Riemannian manifolds V Borovitskiy, A Terenin, P Mostowsky, MP Deisenroth Advances in Neural Information Processing Systems, 2020 | 127 | 2020 |
| Matérn Gaussian Processes on Graphs V Borovitskiy, I Azangulov, A Terenin, P Mostowsky, MP Deisenroth, ... Artificial Intelligence and Statistics, 2021 | 91 | 2021 |
| Variational Integrator Networks for Physically Structured Embeddings S Saemundsson, A Terenin, K Hofmann, MP Deisenroth Artificial Intelligence and Statistics, 3078-3087, 2020 | 79 | 2020 |
| Asynchronous Gibbs sampling A Terenin, D Simpson, D Draper Artificial Intelligence and Statistics, 144-154, 2020 | 67* | 2020 |
| Pathwise Conditioning of Gaussian Processes JT Wilson, V Borovitskiy, A Terenin, P Mostowsky, MP Deisenroth Journal of Machine Learning Research, 2021 | 53 | 2021 |
| GPU-accelerated Gibbs sampling: a case study of the Horseshoe Probit model A Terenin, S Dong, D Draper Statistics and computing 29, 301-310, 2019 | 52* | 2019 |
| 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 | 37 | 2021 |
| 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 | 27 | 2021 |
| Learning Contact Dynamics using Physically Structured Neural Networks A Hochlehnert, A Terenin, S Sćmundsson, MP Deisenroth Artificial Intelligence and Statistics, 2021 | 26 | 2021 |
| Pólya Urn Latent Dirichlet Allocation: a doubly sparse massively parallel sampler A Terenin, M Magnusson, L Jonsson, D Draper IEEE Transactions on Pattern Analysis and Machine Intelligence 41 (7), 1709-1719, 2019 | 21 | 2019 |
| Stationary kernels and Gaussian processes on Lie groups and their homogeneous spaces I: the compact case I Azangulov, A Smolensky, A Terenin, V Borovitskiy Journal of Machine Learning Research, 2024 | 19 | 2024 |
| Aligning Time Series on Incomparable Spaces S Cohen, G Luise, A Terenin, B Amos, MP Deisenroth Artificial Intelligence and Statistics, 2021 | 17 | 2021 |
| 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 | 14 | 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 Journal of Machine Learning Research, 2024 | 13 | 2024 |
| 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 | 11 | 2021 |
| A noninformative prior on a space of distribution functions A Terenin, D Draper Entropy 19 (8), 391, 2017 | 11 | 2017 |
| The Cambridge Law Corpus: A Dataset for Legal AI Research A Östling, H Sargeant, H Xie, L Bull, A Terenin, L Jonsson, M Magnusson, ... Advances in Neural Information Processing Systems, 2024 | 8* | 2024 |
| 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 | 5 | 2023 |
| 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 | 4 | 2024 |
Viacheslav BorovitskiyPostdoctoral Fellow at ETH ZürichVerified email at inf.ethz.ch
