| Conditional time series forecasting with convolutional neural networks A Borovykh, S Bohte, CW Oosterlee Journal of Computational Finance 22 (4), 2017 | 1058* | 2017 |
| A neural network-based framework for financial model calibration S Liu, A Borovykh, LA Grzelak, CW Oosterlee Journal of Mathematics in Industry 9 (1), 9, 2019 | 187 | 2019 |
| Optimally weighted loss functions for solving pdes with neural networks R van der Meer, C Oosterlee, A Borovykh Journal of Computational and Applied Mathematics, 2020 | 186 | 2020 |
| Honest-but-curious nets: Sensitive attributes of private inputs can be secretly coded into the classifiers' outputs M Malekzadeh, A Borovykh, D Gündüz Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications …, 2021 | 59 | 2021 |
| Generalization in fully-connected neural networks for time series forecasting A Borovykh, CW Oosterlee, SM Bohté Journal of Computational Science 36, 101020, 2019 | 53 | 2019 |
| Layer-wise characterization of latent information leakage in federated learning F Mo, A Borovykh, M Malekzadeh, H Haddadi, S Demetriou Distributed and Private Machine Learning (DPML) Workshop ICLR, 2020 | 40 | 2020 |
| A Gaussian Process perspective on Convolutional Neural Networks A Borovykh arXiv preprint arXiv:1810.10798, 2018 | 24 | 2018 |
| Efficient computation of various valuation adjustments under local Lévy models A Borovykh, A Pascucci, CW Oosterlee SIAM Journal on Financial Mathematics 9 (1), 251-273, 2018 | 24 | 2018 |
| Quantifying and Localizing Usable Information Leakage from Neural Network Gradients F Mo, A Borovykh, M Malekzadeh, S Demetriou, D Gündüz, H Haddadi arXiv preprint arXiv:2105.13929, 2022 | 21* | 2022 |
| On a neural network to extract implied information from American options S Liu, Á Leitao, A Borovykh, CW Oosterlee Applied Mathematical Finance 28 (5), 449-475, 2021 | 21* | 2021 |
| Leave-one-out distinguishability in machine learning J Ye, A Borovykh, S Hayou, R Shokri International Conference on Learning Representations (ICLR) 2024, 2024 | 20 | 2024 |
| On stochastic mirror descent with interacting particles: convergence properties and variance reduction A Borovykh, N Kantas, P Parpas, GA Pavliotis Physica D: Nonlinear Phenomena 418, 132844, 2021 | 17 | 2021 |
| Pricing Bermudan options under local Lévy models with default A Borovykh, A Pascucci, CW Oosterlee Journal of Mathematical Analysis and Applications 450 (2), 929-953, 2017 | 17 | 2017 |
| Systemic risk in a mean-field model of interbank lending with self-exciting shocks A Borovykh, A Pascucci, S La Rovere IISE Transactions 50 (9), 806-819, 2018 | 12 | 2018 |
| Deep Unlearn: Benchmarking Machine Unlearning for Image Classification XF Cadet, A Borovykh, M Malekzadeh, S Ahmadi-Abhari, H Haddadi 2025 IEEE 10th European Symposium on Security and Privacy (EuroS&P), 939-962, 2025 | 10* | 2025 |
| Reinforcement learning of chaotic systems control in partially observable environments M Weissenbacher, A Borovykh, G Rigas Flow, Turbulence and Combustion, 1-22, 2025 | 10 | 2025 |
| Data-driven initialization of deep learning solvers for Hamilton-Jacobi-Bellman PDEs A Borovykh, D Kalise, A Laignelet, P Parpas 25th International Symposium on Mathematical Theory of Networks and Systems …, 2022 | 9 | 2022 |
| To interact or not? The convergence properties of interacting stochastic mirror descent A Borovykh, N Kantas, P Parpas, GA Pavliotis International Conference on Machine Learning (ICML) Workshop on ‘Beyond …, 2020 | 7 | 2020 |
| Stochastic Mirror Descent for Convex Optimization with Consensus Constraints A Borovykh, N Kantas, P Parpas, GA Pavliotis SIAM Journal on Applied Dynamical Systems, 2024 | 6* | 2024 |
| CHAROT: Robustly controlling chaotic PDEs with partial observations M Weissenbacher, A Borovykh, G Rigas ICLR 2024 Workshop on AI4DifferentialEquations In Science, 2024 | 4 | 2024 |
