Embedded Files
Publications
Publications
2025
A. Vasileiou, B. Finkelshtein, F. Geerts, R. Levie, C Morris. Covered Forest: Fine-grained generalization analysis of graph neural networks. ICML. 2025.
D. Zilberg, R. Levie. PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities. ICML. 2025.
J. Kouchly, B. Finkelshtein, M. Bronstein, R. Levie. Efficient Learning on Large Graphs using a Densifying Regularity Lemma. Preprint. 2025.
A .Vasileiou, S. Jegelka, R. Levie, C. Morris. Survey on Generalization Theory for Graph Neural Networks. Preprint. 2025.
L. Rauchwerger, S. Jegelka, R. Levie. Generalization, Expressivity, and Universality of Graph Neural Networks on Attributed Graphs. ICLR. 2025.
S. Maskey, G. Kutyniok, R. Levie. Generalization Bounds for Message Passing Networks on Mixture of Graphons. To be published, SIAM Journal on Mathematics of Data Science (SIMODS). 2025.
2024
Ç. Yapar, F. Jaensch, R. Levie, G. Kutyniok, G. Caire. Overview of the First Pathloss Radio Map Prediction Challenge. IEEE Open Journal of Signal Processing. 2024.
YWE. Lin, R. Talmon, R. Levie. Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters. NeurIPS. 2024.
B. Finkelshtein, İ. Ceylan, M. Bronstein, R. Levie. Learning on Large Graphs using Intersecting Communities. NeurIPS. 2024.
C. Morris, N. Dym, H. Maron, İ. Ceylan, F. Frasca, R. Levie, D. Lim, M. Bronstein, M. Grohe, S. Jegelka. Position: Future Directions in Foundations of Graph Machine Learning. ICML. 2024.
R. Levie. A graphon-signal analysis of graph neural networks. Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS). 2024.
J. Böker, R. Levie, N. Huang, S. Villar, C. Morris. Fine-grained Expressivity of Graph Neural Networks. Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS). 2024.
N. Huang, R. Levie, S. Villar. Approximately Equivariant Graph Networks. Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS). 2024.
2023
Ç. Yapar, R. Levie, G. Kutyniok, G. Caire. Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach. IEEE Transactions on Wireless Communications, 2023.
S. Kolek, R. Windesheim, H. A. Loarca, G. Kutyniok, R. Levie. Explaining Image Classifiers with Multiscale Directional Image Representation. IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023.
D. A. Nguyen, R. Levie*, J. Lienen, G. Kutyniok, E. Hüllermeier. Memorization-Dilation: Modeling Neural Collapse Under Noise. Eleventh International Conference on Learning Representations (ICLR). 2023.
R. Paolino, A. Bojchevski, S. Günnemann, G. Kutyniok, R. Levie. Unveiling the Sampling Density in Non-Uniform Geometric Graphs. Eleventh International Conference on Learning Representations (ICLR). 2023.
S. Maskey, R. Levie, G. Kutyniok. Transferability of Graph Neural Networks: an Extended Graphon Approach. Applied and Computational Harmonic Analysis (ACHA). 2023.
.R. Levie, H. Avron, G. Kutyniok. Quasi Monte Carlo Time-Frequency Analysis. Journal of Mathematical Analysis and Applications. 2023.
S. Halvdansson, J.-F. Olsen, N. Sochen, R. Levie. Existence of Uncertainty Minimizers for the Continuous Wavelet Transform. Mathematische Nachrichten. 2023.
2022
S. Kolek, D. Nguyen, R. Levie, J. Bruna, G. Kutyniok. Cartoon Explanations of Image Classifiers. Proceedings of the European Conference on Computer Vision (ECCV) 2022. (Oral presentation).
S. Maskey, R. Levie*, Y. Lee, G. Kutyniok. Generalization Analysis of Message Passing Neural Networks on Large Random Graphs. Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS). 2022.
R. Levie and N. Sochen. A Wavelet Plancherel Theory with Application to Multipliers and Sparse Approximations. Numerical Functional Analysis and Optimization. 2022.
Ç. Yapar, R. Levie, G. Kutyniok, & G. Caire. LocUNet: Fast Urban Positioning Using Radio Maps and Deep Learning. ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4063-4067). IEEE.
R. Levie and H. Avron. Randomized Continuous Frames in Time-Frequency Analysis. Advances in Computational Mathematics. vol. 48, no. 25. 2022.
V. Tiep Do, R. Levie, G. Kutyniok. Analysis of simultaneous inpainting and geometric separation based on sparse decomposition. Analysis and Applications, vol. 20, no. 02, pp. 303-352. 2022.
R. Levie, and H. Avron. Randomized Signal Processing with Continuous Frames. Journal of Fourier Analysis and Applications, vol. 28, no. 5. 2022.
2021
R. Levie, W. Huang, L. Bucci, M. M. Bronstein, G. Kutyniok. Transferability of Spectral Graph Convolutional Neural Networks. Journal of Machine Learning Research, vol. 22, no. 272, pp. 1-59. 2021.
R. Levie, Ç. Yapar, G. Kutyniok, G. Caire. RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks. IEEE Transactions on Wireless Communications, vol. 20, no. 6, pp. 4001-4015. 2021.
2020
C. Heiß, R. Levie, C. Resnick, G. Kutyniok, J. Bruna. In-Distribution Interpretability for Challenging Modalities. ICML, Interpretability for Scientific Discovery. 2020.
Ç. Yapar, R. Levie, G. Kutyniok, G. Caire. Real-time Localization Using Radio Maps. 2020. (Preprint).
R. Levie, and N.Sochen, Uncertainty principles and optimally sparse wavelet transforms. Applied and Computational Harmonic Analysis, vol. 48, no. 3, pp. 811-867. 2020.
2019
R. Levie, F. Monti, X. Bresson, and M. Bronstein. CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters. IEEE Transactions on Signal Processing, vol. 67, no. 1, pp. 97-109. 2019.
2014
R. Levie, H.G. Stark, F.Lieb, and N.Sochen. Adjoint translation, adjoint observable and uncertainty principles. Advances in Computational Mathematics, vol. 40, no. 3, pp. 609 – 627. 2014.
Invited Books Chapters
Invited Books Chapters
S. Kolek, D. Nguyen, R. Levie, J. Bruna, G. Kutyniok. A Rate-Distortion Framework for Explaining Black-box Model Decisions. Springer LNAI xxAI – Beyond explainable Artificial Intelligence. accepted 2021.
R. Levie, C. Yapar, G. Caire, G. Kutyniok. Fast Radio Propagation Prediction with Deep Learning. Springer ANHA Compressed Sensing in Information Processing. accepted 2021.
Ph.D Thesis
Ph.D Thesis
A Group Representation Approach to Localization in Signal Processing. Adviser: Prof. Nir Sochen. Tel Aviv University, School of Mathematical Sciences. 2018.
M.Sc Thesis
M.Sc Thesis
Line cross-section models: hybrid volume-surface models of 3D objects based on 1D cross-sections. Advisers: Prof. Nira Dyn & Dr. Elza Farkhi. Tel Aviv University, School of Mathematical Sciences. 2014.
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