| Accepted Machine Learning | | | Intrinsic Gaussian Vector Fields on Manifolds D. Robert-Nicoud, A. Krause, V. Borovitskiy Accepted by International Conference on Artificial Intelligence and Statistics (AISTATS) 2024 Oral |
| Accepted Machine Learning | | | Hodge-Compositional Edge Gaussian Processes M. Yang, V. Borovitskiy, E. Isufi Accepted by International Conference on Artificial Intelligence and Statistics (AISTATS) 2024 |
| Published Machine Learning Statistics | | | Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds P. Rosa, V. Borovitskiy, A. Terenin, J. Rousseau In Neural Information Processing Systems (NeurIPS), 2023 Spotlight |
| Published Machine Learning | | | Implicit Manifold Gaussian Process Regression B. Fichera, V. Borovitskiy, A. Krause, A. Billard In Neural Information Processing Systems (NeurIPS), 2023 |
| Preprint Machine Learning Statistics | | | Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spaces I. Azangulov In review for Journal of Machine Learning Research (JMLR) |
| Published Machine Learning | | | Isotropic Gaussian Processes on Finite Spaces of Graphs V. Borovitskiy In International Conference on Artificial Intelligence and Statistics (AISTATS), 2023 |
| Short note Machine Learning | | | On power sum kernels on symmetric groups I. Azangulov, V. Borovitskiy, A. Smolensky |
| Preprint Machine Learning Statistics | | | Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact case I. Azangulov In review for Journal of Machine Learning Research (JMLR) |
| Preprint Robotics Machine Learning | | | Bringing motion taxonomies to continuous domains via GPLVM on hyperbolic manifolds N. Jaquier, L. Rozo, M. González-Duque, V. Borovitskiy, T. Asfour |
| Published Bioinformatics Optimization Machine Learning | | | Bayesian optimization for demographic inference E. Noskova, V. Borovitskiy In G3: Genes, Genomes, Genetics, 2023 |
| Published Harmonic Analysis | | | Littlewood–Paley–Rubio de Francia inequality for multi-parameter Vilenkin systems V. Borovitskiy In Mathematische Nachrichten, 2023 |
| Published Machine Learning | | | Quadric hypersurface intersection for manifold learning in feature space F. Pavutnitskiy In International Conference on Artificial Intelligence and Statistics (AISTATS), 2022 |
| Published Machine Learning | | | Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels M. Hutchinson In Neural Information Processing Systems (NeurIPS), 2021 |
| Published Robotics Optimization Machine Learning | | | Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels N. Jaquier In Conference on Robot Learning (CoRL), 2021 |
| Published Harmonic Analysis | | | Burkholder meets Gundy: Bellman function method for general operators on martingales V. Borovitskiy In Advances in Mathematics, 2022 |
| Published Harmonic Analysis | | | Interpolation of abstract Hardy-type spaces V. Borovitskiy In Journal of Mathematical Sciences, 2022 |
| Published Machine Learning | | | Matérn Gaussian Processes on Graphs V. Borovitskiy In International Conference on Artificial Intelligence and Statistics (AISTATS), 2021 Best Student Paper Award |
| Published Machine Learning | | | Pathwise Conditioning of Gaussian Processes J. T. Wilson In Journal of Machine Learning Research (JMLR), 2021 |
| Published Harmonic Analysis | | | Littlewood–Paley–Rubio De Francia Inequality for the Two-Parameter Walsh System V. Borovitskiy In Journal of Mathematical Sciences, 2022 |
| Published Harmonic Analysis | | | Weighted Littlewood–Paley inequality for arbitrary rectangles in \(\mathbb{R}^2\) V. Borovitskiy In St. Petersburg Mathematical Journal, 2021 |
| Published Machine Learning | | | Matérn Gaussian processes on Riemannian manifolds V. Borovitskiy In Neural Information Processing Systems (NeurIPS), 2020 |
| Published Machine Learning | | | Efficiently sampling functions from Gaussian process posteriors J. T. Wilson In International Conference on Machine Learning (ICML), 2020 Outstanding Paper Honorable Mention |
| Published Geostatistics | | | Boolean Spectral Analysis in Categorical Reservoir Modeling N. Ismagilov, V. Borovitskiy, M. Lifshits, M. Platonova In Mathematical Geosciences, 2021 |
| Non peer-reviewed Geostatistics | | | Bayesian Inference of Covariance Parameters in Spectral Approach to Geostatistical Simulation N. Ismagilov, I. Azangulov, V. Borovitskiy, M. Lifshits, P. Mostowsky In European Conference on the Mathematics of Oil Recovery (ECMOR), 2020 |
| Non peer-reviewed Machine Learning | | | Reproducibility Project: DeepSite T. Malygina, V. Borovitskiy, Y. Porozov In Transylvanian Machine Learning Summer School, 2018 |
| Published Harmonic Analysis | | | K-closedness for weighted Hardy spaces on the torus \(\mathbb{T}^2\). V. Borovitskiy In Journal of Mathematical Sciences, 2018 |