| Conditional time series forecasting with convolutional neural networks A Borovykh, S Bohte, CW Oosterlee Journal of Computational Finance 22 (4), 2017 | 517* | 2017 |
| A neural network-based framework for financial model calibration S Liu, A Borovykh, LA Grzelak, CW Oosterlee Journal of Mathematics in Industry 9, 1-28, 2019 | 70 | 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 | 52 | 2020 |
| Generalization in fully-connected neural networks for time series forecasting A Borovykh, CW Oosterlee, SM Bohté Journal of Computational Science 36, 101020, 2019 | 26 | 2019 |
| 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 |
| 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 | 15 | 2021 |
| 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 | 15* | 2021 |
| A Gaussian Process perspective on Convolutional Neural Networks A Borovykh arXiv preprint arXiv:1810.10798, 2018 | 15 | 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 | 15 | 2018 |
| 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 | 12 | 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 | 9 | 2018 |
| 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 | 5* | 2021 |
| Stochastic Mirror Descent for Convex Optimization with Consensus Constraints A Borovykh, N Kantas, P Parpas, GA Pavliotis arXiv preprint arXiv:2201.08642, 2022 | 2* | 2022 |
| The effects of optimization on generalization in infinitely wide neural networks A Borovykh International Conference on Machine Learning (ICML) Workshop on …, 2019 | 2 | 2019 |
| Optimizing interacting Langevin dynamics using spectral gaps A Borovykh, N Kantas, P Parpas, G Pavliotis International Conference on Machine Learning (ICML) Workshop on “Beyond …, 2021 | 1 | 2021 |
| Implications of collateral agreements for derivative pricing A Borovykh, MJ Boes Working paper, 2014 | 1 | 2014 |
| Efficient regression with deep neural networks: how many datapoints do we need? D Lengyel, A Borovykh Has it Trained Yet? NeurIPS 2022 Workshop, 2022 | | 2022 |
| 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 | | 2022 |
| Accurate river level predictions using a Wavenet-like model S Doyle, A Borovykh NeurIPS Workshop on Climate Change AI, 2020 | | 2020 |
| Analytic expressions for the output evolution of a deep neural network A Borovykh arXiv preprint arXiv:1912.08526, 2019 | | 2019 |
