Publications

Papers under review

1. Haoming Chen, Nicolas Zilberstein, and Santiago Segarra. Prior-informed flow matching for graph reconstruction. 

2. Victor M. Tenorio, Nicolas Zilberstein, Santiago Segarra, and Antonio G. Marques. Graph guided diffusion: Unified guidance for conditional graph generation. 

3. Ali Azizpour, Nicolas Zilberstein, and Santiago Segarra. Model-driven graph contrastive learning. arXiv preprint arXiv:2506.06212, 2025

Journals

1.  N. Zilberstein, A. Sabharwal, S. Segarra, Solving Linear Inverse Problems using Higher-Order Annealed Langevin Diffusion. IEEE Trans. Signal Process., 2024

2. F. Gama, N. Zilberstein, M. Sevilla, R. G. Baraniuk, S. Segarra, Unsupervised learning of sampling distributions for particle filters. IEEE Trans. Signal Process., 2023.

3.  N. Zilberstein, C. Dick, R. Doost-Mohammady, A. Sabharwal, S. Segarra, Annealed Langevin Dynamics for Massive MIMO Detection, IEEE Trans. Wireless Comm., 2022.

4.  N. Zilberstein, J. A. Maya, A. O. Altieri,  A BCS microwave imaging algorithm for object detection and shape reconstruction tested with experimental data., Electron. Lett., 2021. 

Conference Papers

1. N. Zilberstein, L. Chamon, and S. Segarra. Safe sampling with Langevin diffusion using navigation potentials. In Asilomar Conf. Signals, Syst. and Comp. IEEE, 2025

2. V. M. Tenorio, N. Zilberstein, S. Segarra, and A. G. Marques. Graph guided diffusion: Unified guidance for conditional graph generation. NeurIPS workshop NPGML, 2025

3. N. Zilberstein, M. Mardani, and S. Segarra. Repulsive latent score distillation for solving inverse problems, ICLR 2025

4. Ali Azizpour, Nicolas Zilberstein, and Santiago Segarra. Scalable implicit graphon learning, AISTATS 2025

5. N. Zilberstein, A. Malhotra, Y. Deenoo, and S. Hamidi-Rad. Topology preserving regularization for independent training of inter-operable models. NeurIPS workshop UniReps, 2024

6. N. Zilberstein, A. Swami, S. Segarra, Joint channel estimation and data detection in massive MIMO systems based on diffusion models. Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), 2024. 

7.  B. Cox, S. Perez-Vieites, N. Zilberstein, M. Sevilla, S. Segarra, V. Elvira. End-to- end learning of Gaussian mixture proposals using differentiable particle filters and neural networks, Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), 2024. 

8.  M. Sevilla*, N. Zilberstein*, B. Cox, S. Perez-Vieites, V. Elvira, S. Segarra. State and dynamics estimation with the Kalman–Langevin filter, 2023 57th Asilomar Conf. Signals, Systems, and Computers, IEEE, 2023

9. N. Zilberstein, C. Dick, R. Doost-Mohammady, A. Sabharwal, S. Segarra, Accelerated massive MIMO detector based on annealed underdamped Langevin dynamics, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), 2023. 

10. N. Zilberstein, C. Dick, R. Doost-Mohammady, A. Sabharwal, S. Segarra, Detection by Sampling: Massive MIMO Detector based on Langevin Dynamics, European Signal Process. Conf. (EUSIPCO), 2022.

11.  N. Zilberstein, C. Dick, R. Doost-Mohammady, A. Sabharwal, S. Segarra, Robust MIMO Detection using Hypernetworks with Learned Regularizers, European Signal Process. Conf. (EUSIPCO), 2022.

12.  F. Gama, N. Zilberstein, R. G. Baraniuk, S. Segarra, Unrolling Particles: Unsupervised Learning of Sampling Distributions, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), 2022. 

13.  N. Zilberstein, B. Cernuschi-Frias, Particle Filter with Unknown Noise Statistics and with Prior Knowledge, Argentine Conference on Automatic Control (AADECA), 2018.