Anastasia Borovykh
Anastasia Borovykh
Researcher Imperial College
Verified email at - Homepage
Cited by
Cited by
Conditional time series forecasting with convolutional neural networks
A Borovykh, S Bohte, CW Oosterlee
Journal of Computational Finance 22 (4), 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), 1-28, 2019
Optimally weighted loss functions for solving pdes with neural networks
R van der Meer, C Oosterlee, A Borovykh
arXiv preprint arXiv:2002.06269, 2020
Generalization in fully-connected neural networks for time series forecasting
A Borovykh, CW Oosterlee, SM BohtÚ
Journal of Computational Science 36, 101020, 2019
Pricing Bermudan options under local LÚvy models with default
A Borovykh, A Pascucci, CW Oosterlee
Journal of Mathematical Analysis and Applications 450 (2), 929-953, 2017
A Gaussian Process perspective on Convolutional Neural Networks
A Borovykh
arXiv preprint arXiv:1810.10798, 2018
Efficient computation of various valuation adjustments under local LÚvy models
A Borovykh, A Pascucci, CW Oosterlee
SIAM Journal on Financial Mathematics 9 (1), 251-273, 2018
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
Layer-wise Characterization of Latent Information Leakage in Federated Learning
F Mo, A Borovykh, M Malekzadeh, H Haddadi, S Demetriou
ICLR Workshop on Distributed and Private Machine Learning, 2020
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
On Calibration Neural Networks for extracting implied information from American options
S Liu, ┴ Leitao, A Borovykh, CW Oosterlee
arXiv preprint arXiv:2001.11786, 2020
Stochastic mirror descent for fast distributed optimization and federated learning
A Borovykh, N Kantas, P Parpas, G Pavliotis
NeurIPS Workshop on Optimization for Machine Learning, 2020
The effects of optimization on generalization in infinitely wide neural networks
A Borovykh
ICML Workshop on Understanding and Improving Generalization in Deep Learning, 2019
Implications of collateral agreements for derivative pricing
A Borovykh, MJ Boes
Working paper, 2014
Quantifying Information Leakage from Gradients
F Mo, A Borovykh, M Malekzadeh, H Haddadi, S Demetriou
arXiv preprint arXiv:2105.13929, 2021
Honest-but-Curious Nets: Sensitive Attributes of Private Inputs can be Secretly Coded into the Entropy of Classifiers' Outputs
M Malekzadeh, A Borovykh, D GŘndŘz
arXiv preprint arXiv:2105.12049, 2021
Accurate river level predictions using a Wavenet-like model
S Doyle, A Borovykh
NeurIPS Workshop on Climate Change AI, 2020
Machine Learning to Compute Implied Volatility from European/American Options Considering Dividend Yield
S Liu, ┴ Leitao, A Borovykh, CW Oosterlee
Multidisciplinary Digital Publishing Institute Proceedings 54 (1), 61, 2020
Analytic expressions for the output evolution of a deep neural network
A Borovykh
arXiv preprint arXiv:1912.08526, 2019
Bermudan option valuation under state-dependent models
A Borovykh, A Pascucci, CW Oosterlee
International Congress on Actuarial Science and Quantitative Finance, 127-138, 2016
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