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Nicolas Zilberstein
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2020 – today
- 2026
[i8]Harvey Chen, Nicolas Zilberstein, Santiago Segarra:
Prior-Informed Flow Matching for Graph Reconstruction. CoRR abs/2601.22107 (2026)- 2025
[c9]Ali Azizpour, Nicolas Zilberstein, Santiago Segarra:
Scalable Implicit Graphon Learning. AISTATS 2025: 3952-3960
[c8]Nicolas Zilberstein, Morteza Mardani, Santiago Segarra:
Repulsive Latent Score Distillation for Solving Inverse Problems. ICLR 2025
[i7]Victor M. Tenorio
, Nicolas Zilberstein, Santiago Segarra
, Antonio G. Marques:
Graph Guided Diffusion: Unified Guidance for Conditional Graph Generation. CoRR abs/2505.19685 (2025)
[i6]Ali Azizpour, Nicolas Zilberstein, Santiago Segarra
:
Model-Driven Graph Contrastive Learning. CoRR abs/2506.06212 (2025)
[i5]Nicolas Zilberstein, Santiago Segarra, Luiz Chamon:
Sampling with Shielded Langevin Monte Carlo Using Navigation Potentials. CoRR abs/2512.22153 (2025)- 2024
[j3]Nicolas Zilberstein
, Ashutosh Sabharwal
, Santiago Segarra
:
Solving Linear Inverse Problems Using Higher-Order Annealed Langevin Diffusion. IEEE Trans. Signal Process. 72: 492-505 (2024)
[c7]Benjamin Cox, Sara Pérez-Vieites, Nicolas Zilberstein, Martin Sevilla, Santiago Segarra
, Víctor Elvira
:
End-to-End Learning of Gaussian Mixture Proposals Using Differentiable Particle Filters and Neural Networks. ICASSP 2024: 9701-9705
[c6]Nicolas Zilberstein, Ananthram Swami, Santiago Segarra
:
Joint Channel Estimation and Data Detection in Massive Mimo Systems Based on Diffusion Models. ICASSP 2024: 13291-13295
[i4]Nicolas Zilberstein, Morteza Mardani, Santiago Segarra
:
Repulsive Score Distillation for Diverse Sampling of Diffusion Models. CoRR abs/2406.16683 (2024)
[i3]Ali Azizpour, Nicolas Zilberstein, Santiago Segarra
:
Scalable Implicit Graphon Learning. CoRR abs/2410.17464 (2024)- 2023
[j2]Fernando Gama
, Nicolas Zilberstein
, Martin Sevilla
, Richard G. Baraniuk
, Santiago Segarra
:
Unsupervised Learning of Sampling Distributions for Particle Filters. IEEE Trans. Signal Process. 71: 3852-3866 (2023)
[j1]Nicolas Zilberstein
, Chris Dick, Rahman Doost-Mohammady, Ashutosh Sabharwal
, Santiago Segarra
:
Annealed Langevin Dynamics for Massive MIMO Detection. IEEE Trans. Wirel. Commun. 22(6): 3762-3776 (2023)
[c5]Martin Sevilla, Nicolas Zilberstein, Benjamin Cox, Sara Pérez-Vieites, Víctor Elvira
, Santiago Segarra
:
State and Dynamics Estimation with the Kalman-Langevin filter. ACSSC 2023: 1372-1376
[c4]Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady, Ashutosh Sabharwal, Santiago Segarra
:
Accelerated Massive MIMO Detector Based on Annealed Underdamped Langevin Dynamics. ICASSP 2023: 1-5- 2022
[c3]Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady, Ashutosh Sabharwal, Santiago Segarra:
Robust MIMO Detection using Hypernetworks with Learned Regularizers. EUSIPCO 2022: 1626-1630
[c2]Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady, Ashutosh Sabharwal, Santiago Segarra:
Detection by Sampling: Massive MIMO Detector based on Langevin Dynamics. EUSIPCO 2022: 1651-1655
[c1]Fernando Gama, Nicolas Zilberstein, Richard G. Baraniuk, Santiago Segarra
:
Unrolling Particles: Unsupervised Learning of Sampling Distributions. ICASSP 2022: 5498-5502- 2021
[i2]Fernando Gama, Nicolas Zilberstein, Richard G. Baraniuk, Santiago Segarra:
Unrolling Particles: Unsupervised Learning of Sampling Distributions. CoRR abs/2110.02915 (2021)
[i1]Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady, Ashutosh Sabharwal, Santiago Segarra:
Robust MIMO Detection using Hypernetworks with Learned Regularizers. CoRR abs/2110.07053 (2021)
Coauthor Index

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