T. Mitchell Roddenberry

According to our database, T. Mitchell Roddenberry authored at least 23 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Enhanced Graph-Learning Schemes Driven by Similar Distributions of Motifs.

Samuel Rey, T. Mitchell Roddenberry, Santiago Segarra, Antonio G. Marques

IEEE Trans. Signal Process., 2023

Limits of Dense Simplicial Complexes.

T. Mitchell Roddenberry, Santiago Segarra

J. Mach. Learn. Res., 2023

Implicit Neural Representations and the Algebra of Complex Wavelets.

T. Mitchell Roddenberry, Vishwanath Saragadam, Maarten V. de Hoop, Richard G. Baraniuk

CoRR, 2023

Windowed Fourier Analysis for Signal Processing on Graph Bundles.

T. Mitchell Roddenberry, Santiago Segarra

Proceedings of the IEEE International Conference on Acoustics, 2023

Signal Processing On Product Spaces.

T. Mitchell Roddenberry, Vincent P. Grande, Florian Frantzen, Michael T. Schaub, Santiago Segarra

Proceedings of the IEEE International Conference on Acoustics, 2023

2022

On Local Distributions in Graph Signal Processing.

T. Mitchell Roddenberry, Fernando Gama, Richard G. Baraniuk, Santiago Segarra

IEEE Trans. Signal Process., 2022

Signal Processing On Cell Complexes.

T. Mitchell Roddenberry, Michael T. Schaub, Mustafa Hajij

Proceedings of the IEEE International Conference on Acoustics, 2022

Hodgelets: Localized Spectral Representations of Flows On Simplicial Complexes.

T. Mitchell Roddenberry, Florian Frantzen, Michael T. Schaub, Santiago Segarra

Proceedings of the IEEE International Conference on Acoustics, 2022

2021

Blind Inference of Eigenvector Centrality Rankings.

T. Mitchell Roddenberry, Santiago Segarra

IEEE Trans. Signal Process., 2021

Signal processing on higher-order networks: Livin' on the edge... and beyond.

Michael T. Schaub, Yu Zhu, Jean-Baptiste Seby, T. Mitchell Roddenberry, Santiago Segarra

Signal Process., 2021

Signal processing on simplicial complexes.

Michael T. Schaub, Jean-Baptiste Seby, Florian Frantzen, T. Mitchell Roddenberry, Yu Zhu, Santiago Segarra

CoRR, 2021

An Impossibility Theorem for Node Embedding.

T. Mitchell Roddenberry, Yu Zhu, Santiago Segarra

CoRR, 2021

Sparse Partial Least Squares for Coarse Noisy Graph Alignment.

Michael Weylandt, George Michailidis, T. Mitchell Roddenberry

Proceedings of the IEEE Statistical Signal Processing Workshop, 2021

Principled Simplicial Neural Networks for Trajectory Prediction.

T. Mitchell Roddenberry, Nicholas Glaze, Santiago Segarra

Proceedings of the 38th International Conference on Machine Learning, 2021

Network Topology Inference with Graphon Spectral Penalties.

T. Mitchell Roddenberry, Madeline Navarro, Santiago Segarra

Proceedings of the IEEE International Conference on Acoustics, 2021

Network Topology Change-Point Detection from Graph Signals with Prior Spectral Signatures.

Chiraag Kaushik, T. Mitchell Roddenberry, Santiago Segarra

Proceedings of the IEEE International Conference on Acoustics, 2021

2020

Exact Blind Community Detection From Signals on Multiple Graphs.

T. Mitchell Roddenberry, Michael T. Schaub, Hoi-To Wai, Santiago Segarra

IEEE Trans. Signal Process., 2020

Rank-One Measurements of Low-Rank PSD Matrices Have Small Feasible Sets.

T. Mitchell Roddenberry, Santiago Segarra, Anastasios Kyrillidis

CoRR, 2020

Simultaneous Grouping and Denoising via Sparse Convex Wavelet Clustering.

Michael Weylandt, T. Mitchell Roddenberry, Genevera I. Allen

CoRR, 2020

Metric Representations of Networks: A Uniqueness Result.

Santiago Segarra, T. Mitchell Roddenberry, Facundo Mémoli, Alejandro Ribeiro

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Blind Inference of Centrality Rankings from Graph Signals.

T. Mitchell Roddenberry, Santiago Segarra

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Blind Estimation of Eigenvector Centrality from Graph Signals: Beyond Low-pass Filtering.

T. Mitchell Roddenberry, Santiago Segarra

Proceedings of the 54th Asilomar Conference on Signals, Systems, and Computers, 2020

2019

HodgeNet: Graph Neural Networks for Edge Data.

T. Mitchell Roddenberry, Santiago Segarra

Proceedings of the 53rd Asilomar Conference on Signals, Systems, and Computers, 2019