A Gaussian Process perspective on Convolutional Neural Networks. - Anastasia Borovykh (CoRR, 2018)

Efficient Computation of Various Valuation Adjustments Under Local Lévy Models. - Anastasia Borovykh; Andrea Pascucci; Cornelis W. Oosterlee (SIAM J. Financial Math., 2018)

Generalisation in fully-connected neural networks for time series forecasting. - Anastasia Borovykh; Cornelis W. Oosterlee; Sander M. Bohté (CoRR, 2019)

A neural network-based framework for financial model calibration. - Shuaiqiang Liu; Anastasia Borovykh; Lech A. Grzelak; Cornelis W. Oosterlee (CoRR, 2019)

Analytic expressions for the output evolution of a deep neural network. - Anastasia Borovykh (CoRR, 2019)

Generalization in fully-connected neural networks for time series forecasting. - Anastasia Borovykh; Cornelis W. Oosterlee; Sander M. Bohté (J. Comput. Sci., 2019)

On Calibration Neural Networks for extracting implied information from American options. - Shuaiqiang Liu; Álvaro Leitao; Anastasia Borovykh; Cornelis W. Oosterlee (CoRR, 2020)

Layer-wise Characterization of Latent Information Leakage in Federated Learning. - Fan Mo 004; Anastasia Borovykh; Mohammad Malekzadeh; Hamed Haddadi; Soteris Demetriou (CoRR, 2020)

Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs. - Mohammad Malekzadeh; Anastasia Borovykh; Deniz Gündüz (CCS, 2021)

Honest-but-Curious Nets: Sensitive Attributes of Private Inputs can be Secretly Coded into the Entropy of Classifiers' Outputs. - Mohammad Malekzadeh; Anastasia Borovykh; Deniz Gündüz (CoRR, 2021)

Quantifying Information Leakage from Gradients. - Fan Mo 004; Anastasia Borovykh; Mohammad Malekzadeh; Hamed Haddadi; Soteris Demetriou (CoRR, 2021)

Optimally weighted loss functions for solving PDEs with Neural Networks. - Remco van der Meer; Cornelis W. Oosterlee; Anastasia Borovykh (J. Comput. Appl. Math., 2022)

Privacy Risk for anisotropic Langevin dynamics using relative entropy bounds. - Anastasia Borovykh; Nikolas Kantas; Panos Parpas; Greg Pavliotis (CoRR, 2023)

On original and latent space connectivity in deep neural networks. - Boyang Gu; Anastasia Borovykh (CoRR, 2023)

Username Squatting on Online Social Networks: A Study on X. - Anastasios Lepipas; Anastasia Borovykh; Soteris Demetriou (AsiaCCS, 2024)

Leave-one-out Distinguishability in Machine Learning. - Jiayuan Ye 001; Anastasia Borovykh; Soufiane Hayou; Reza Shokri (ICLR, 2024)

P4: Towards private, personalized, and Peer-to-Peer learning. - Mohammad Mahdi Maheri; Sandra Deepthy Siby; Sina Abdollahi; Anastasia Borovykh; Hamed Haddadi (CoRR, 2024)

Deep Unlearn: Benchmarking Machine Unlearning. - Xavier F. Cadet; Anastasia Borovykh; Mohammad Malekzadeh; Sara Ahmadi-Abhari; Hamed Haddadi (CoRR, 2024)

Stochastic Mirror Descent for Convex Optimization with Consensus Constraints. - Anastasia Borovykh; Nikolas Kantas; Panos Parpas; Grigorios A. Pavliotis (SIAM J. Appl. Dyn. Syst., 2024)

Deep Unlearn: Benchmarking Machine Unlearning for Image Classification. - Xavier F. Cadet; Anastasia Borovykh; Mohammad Malekzadeh; Sara Ahmadi-Abhari; Hamed Haddadi (EuroS&P, 2025)

Concept Reachability in Diffusion Models: Beyond Dataset Constraints. - Marta Aparicio Rodriguez; Xenia Miscouridou; Anastasia Borovykh (CoRR, 2025)