Uncertainty principles, minimum uncertainty samplings and translations. - Daniel Lantzberg; Florian Lieb; Hans-Georg Stark; Ron Levie; Nir A. Sochen (EUSIPCO, 2012)

Adjoint translation, adjoint observable and uncertainty principles. - Ron Levie; Hans-Georg Stark; Florian Lieb; Nir A. Sochen (Adv. Comput. Math., 2014)

Uncertainty principles and optimally sparse wavelet transforms. - Ron Levie; Nir A. Sochen (CoRR, 2017)

A wavelet Plancherel theory with application to sparse continuous wavelet transform. - Ron Levie; Nir A. Sochen (CoRR, 2017)

On the Transferability of Spectral Graph Filters. - Ron Levie; Elvin Isufi; Gitta Kutyniok (CoRR, 2019)

Transferability of Spectral Graph Convolutional Neural Networks. - Ron Levie; Michael M. Bronstein; Gitta Kutyniok (CoRR, 2019)

CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters. - Ron Levie; Federico Monti; Xavier Bresson; Michael M. Bronstein (IEEE Trans. Signal Process., 2019)

Pathloss Prediction using Deep Learning with Applications to Cellular Optimization and Efficient D2D Link Scheduling. - Ron Levie; Çagkan Yapar; Gitta Kutyniok; Giuseppe Caire (ICASSP, 2020)

A Rate-Distortion Framework for Explaining Black-Box Model Decisions. - Stefan Kolek; Duc Anh Nguyen; Ron Levie; Joan Bruna; Gitta Kutyniok (xxAI@ICML, 2020)

Real-time Localization Using Radio Maps. - Çagkan Yapar; Ron Levie; Gitta Kutyniok; Giuseppe Caire (CoRR, 2020)

In-Distribution Interpretability for Challenging Modalities. - Cosmas Heiß; Ron Levie; Cinjon Resnick; Gitta Kutyniok; Joan Bruna (CoRR, 2020)

Quasi Monte Carlo Time-Frequency Analysis. - Ron Levie; Haim Avron; Gitta Kutyniok (CoRR, 2020)

Wavelet Design with Optimally Localized Ambiguity Function: a Variational Approach. - Ron Levie; Efrat Krimer Avraham; Nir A. Sochen (CoRR, 2021)

Existence of Uncertainty Minimizers for the Continuous Wavelet Transform. - Simon Halvdansson; Jan-Fredrik Olsen; Nir A. Sochen; Ron Levie (CoRR, 2021)

Transferability of Graph Neural Networks: an Extended Graphon Approach. - Sohir Maskey; Ron Levie; Gitta Kutyniok (CoRR, 2021)

Transferability of Spectral Graph Convolutional Neural Networks. - Ron Levie; Wei Huang; Lorenzo Bucci; Michael M. Bronstein; Gitta Kutyniok (J. Mach. Learn. Res., 2021)

RadioUNet: Fast Radio Map Estimation With Convolutional Neural Networks. - Ron Levie; Çagkan Yapar; Gitta Kutyniok; Giuseppe Caire (IEEE Trans. Wirel. Commun., 2021)

Cartoon Explanations of Image Classifiers. - Stefan Kolek; Duc Anh Nguyen; Ron Levie; Joan Bruna; Gitta Kutyniok (ECCV (12), 2022)

LocUNet: Fast Urban Positioning Using Radio Maps and Deep Learning. - Çagkan Yapar; Ron Levie; Gitta Kutyniok; Giuseppe Caire (ICASSP, 2022)

Generalization Analysis of Message Passing Neural Networks on Large Random Graphs. - Sohir Maskey; Ron Levie; Yunseok Lee; Gitta Kutyniok (NeurIPS, 2022)

Randomized continuous frames in time-frequency analysis. - Ron Levie; Haim Avron (Adv. Comput. Math., 2022)

On the Effective Usage of Priors in RSS-based Localization. - Çagkan Yapar; Fabian Jaensch; Ron Levie; Giuseppe Caire (CoRR, 2022)

Dataset of Pathloss and ToA Radio Maps With Localization Application. - Çagkan Yapar; Ron Levie; Gitta Kutyniok; Giuseppe Caire (CoRR, 2022)

Dataset of Pathloss and ToA Radio Maps with Localization Application. - Çagkan Yapar; Ron Levie; Gitta Kutyniok; Giuseppe Caire (IEEE DataPort, 2022)

Explaining Image Classifiers with Multiscale Directional Image Representation. - Stefan Kolek; Robert Windesheim; Héctor Andrade-Loarca; Gitta Kutyniok; Ron Levie (CVPR, 2023)

The First Pathloss Radio Map Prediction Challenge. - Çagkan Yapar; Fabian Jaensch; Ron Levie; Gitta Kutyniok; Giuseppe Caire (ICASSP, 2023)

Memorization-Dilation: Modeling Neural Collapse Under Noise. - Duc Anh Nguyen; Ron Levie; Julian Lienen; Eyke Hüllermeier; Gitta Kutyniok (ICLR, 2023)

Unveiling the sampling density in non-uniform geometric graphs. - Raffaele Paolino; Aleksandar Bojchevski; Stephan Günnemann; Gitta Kutyniok; Ron Levie (ICLR, 2023)

Overview of the Urban Wireless Localization Competition. - Çagkan Yapar; Fabian Jaensch; Ron Levie; Gitta Kutyniok; Giuseppe Caire (MLSP, 2023)

Fine-grained Expressivity of Graph Neural Networks. - Jan Böker; Ron Levie; Ningyuan Huang; Soledad Villar; Christopher Morris 001 (NeurIPS, 2023)

Approximately Equivariant Graph Networks. - Ningyuan Huang; Ron Levie; Soledad Villar (NeurIPS, 2023)

A graphon-signal analysis of graph neural networks. - Ron Levie (NeurIPS, 2023)

Real-Time Outdoor Localization Using Radio Maps: A Deep Learning Approach. - Çagkan Yapar; Ron Levie; Gitta Kutyniok; Giuseppe Caire (IEEE Trans. Wirel. Commun., 2023)

Position: Future Directions in the Theory of Graph Machine Learning. - Christopher Morris 001; Fabrizio Frasca; Nadav Dym; Haggai Maron; Ismail Ilkan Ceylan; Ron Levie; Derek Lim; Michael M. Bronstein; Martin Grohe; Stefanie Jegelka (ICML, 2024)

Learning on Large Graphs using Intersecting Communities. - Ben Finkelshtein; Ismail Ilkan Ceylan; Michael M. Bronstein; Ron Levie (NeurIPS, 2024)

Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters. - Ya-Wei Eileen Lin; Ronen Talmon; Ron Levie (NeurIPS, 2024)

Future Directions in Foundations of Graph Machine Learning. - Christopher Morris 001; Nadav Dym; Haggai Maron; Ismail Ilkan Ceylan; Fabrizio Frasca; Ron Levie; Derek Lim; Michael M. Bronstein; Martin Grohe; Stefanie Jegelka (CoRR, 2024)

Generalization Bounds for Message Passing Networks on Mixture of Graphons. - Sohir Maskey; Gitta Kutyniok; Ron Levie (CoRR, 2024)

PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities. - Daniel Zilberg; Ron Levie (CoRR, 2024)

Covered Forest: Fine-grained generalization analysis of graph neural networks. - Antonis Vasileiou; Ben Finkelshtein; Floris Geerts; Ron Levie; Christopher Morris 001 (CoRR, 2024)

Generalization, Expressivity, and Universality of Graph Neural Networks on Attributed Graphs. - Levi Rauchwerger; Stefanie Jegelka; Ron Levie (ICLR, 2025)

Survey on Generalization Theory for Graph Neural Networks. - Antonis Vasileiou; Stefanie Jegelka; Ron Levie; Christopher Morris 001 (CoRR, 2025)

Efficient Learning on Large Graphs using a Densifying Regularity Lemma. - Jonathan Kouchly; Ben Finkelshtein; Michael M. Bronstein; Ron Levie (CoRR, 2025)

Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models. - Ben Finkelshtein; Ismail Ilkan Ceylan; Michael M. Bronstein; Ron Levie (CoRR, 2025)