Survey on Generalization Theory for Graph Neural Networks.
CoRR, March, 2025
Covered Forest: Fine-grained generalization analysis of graph neural networks.
CoRR, 2024
Generalization, Expressivity, and Universality of Graph Neural Networks on Attributed Graphs.
CoRR, 2024
PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities.
CoRR, 2024
Generalization Bounds for Message Passing Networks on Mixture of Graphons.
CoRR, 2024
Future Directions in Foundations of Graph Machine Learning.
CoRR, 2024
Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024
Learning on Large Graphs using Intersecting Communities.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024
Position: Future Directions in the Theory of Graph Machine Learning.
Proceedings of the Forty-first International Conference on Machine Learning, 2024
Real-Time Outdoor Localization Using Radio Maps: A Deep Learning Approach.
IEEE Trans. Wirel. Commun., December, 2023
A graphon-signal analysis of graph neural networks.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Approximately Equivariant Graph Networks.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Fine-grained Expressivity of Graph Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Overview of the Urban Wireless Localization Competition.
Proceedings of the 33rd IEEE International Workshop on Machine Learning for Signal Processing, 2023
Unveiling the sampling density in non-uniform geometric graphs.
Proceedings of the Eleventh International Conference on Learning Representations, 2023
Memorization-Dilation: Modeling Neural Collapse Under Noise.
Proceedings of the Eleventh International Conference on Learning Representations, 2023
The First Pathloss Radio Map Prediction Challenge.
Proceedings of the IEEE International Conference on Acoustics, 2023
Explaining Image Classifiers with Multiscale Directional Image Representation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
Dataset of Pathloss and ToA Radio Maps with Localization Application.
Dataset, December, 2022
Dataset of Pathloss and ToA Radio Maps With Localization Application.
CoRR, 2022
On the Effective Usage of Priors in RSS-based Localization.
CoRR, 2022
Randomized continuous frames in time-frequency analysis.
Adv. Comput. Math., 2022
Generalization Analysis of Message Passing Neural Networks on Large Random Graphs.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
LocUNet: Fast Urban Positioning Using Radio Maps and Deep Learning.
Proceedings of the IEEE International Conference on Acoustics, 2022
Cartoon Explanations of Image Classifiers.
Proceedings of the Computer Vision - ECCV 2022, 2022
RadioUNet: Fast Radio Map Estimation With Convolutional Neural Networks.
IEEE Trans. Wirel. Commun., 2021
Transferability of Spectral Graph Convolutional Neural Networks.
J. Mach. Learn. Res., 2021
Transferability of Graph Neural Networks: an Extended Graphon Approach.
CoRR, 2021
Existence of Uncertainty Minimizers for the Continuous Wavelet Transform.
CoRR, 2021
Wavelet Design with Optimally Localized Ambiguity Function: a Variational Approach.
CoRR, 2021
Quasi Monte Carlo Time-Frequency Analysis.
CoRR, 2020
In-Distribution Interpretability for Challenging Modalities.
CoRR, 2020
Real-time Localization Using Radio Maps.
CoRR, 2020
A Rate-Distortion Framework for Explaining Black-Box Model Decisions.
Proceedings of the xxAI - Beyond Explainable AI, 2020
Pathloss Prediction using Deep Learning with Applications to Cellular Optimization and Efficient D2D Link Scheduling.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020
CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters.
IEEE Trans. Signal Process., 2019
Transferability of Spectral Graph Convolutional Neural Networks.
CoRR, 2019
On the Transferability of Spectral Graph Filters.
CoRR, 2019
A wavelet Plancherel theory with application to sparse continuous wavelet transform.
CoRR, 2017
Uncertainty principles and optimally sparse wavelet transforms.
CoRR, 2017
Adjoint translation, adjoint observable and uncertainty principles.
Adv. Comput. Math., 2014
Uncertainty principles, minimum uncertainty samplings and translations.
Proceedings of the 20th European Signal Processing Conference, 2012