Peter Macgregor

Summary

I'm a Research Associate in the Laboratory for the Foundations of Computer Science (LFCS), School of Informatics at the University of Edinburgh. My research interests are in networks, data science, and algorithmic spectral graph theory.

I am currently visiting the Simons Institute for the Theory of Computing at UC Berkeley to participate in the fall program on Data Structures and Optimisation for Fast Algorithms.

I am developing the STAG open-source library of efficient spectral graph algorithms along with He Sun.

Publications

NeurIPS 2023 Spotlight

Fast Approximation of Similarity Graphs with Kernel Density Estimation

Peter Macgregor and He Sun

Links coming soon...

NeurIPS 2023

Fast and Simple Spectral Clustering in Theory and Practice

Peter Macgregor

Links coming soon...

ISAAC 2023

Is the Algorithmic Kadison-Singer Problem Hard?

Ben Jourdan, Peter Macgregor, and He Sun

arXiv version

@inproceedings{jourdanAlgorithmicKadisonSinger2022,
    author     = {Ben Jourdan and Peter Macgregor and He Sun},
    title      = {Is the Algorithmic Kadison-Singer Problem Hard?},
    booktitle  = {34th International Symposium on Algorithms and Computation (ISAAC'23)},
    year       = {2023}
}

Tech. Report

Spectral Toolkit of Algorithms for Graphs: Technical Report (1)

Peter Macgregor and He Sun

arXiv version PDF code

@misc{macgregor2023spectral,
  title={Spectral Toolkit of Algorithms for Graphs: Technical Report (1)}, 
  author={Peter Macgregor and He Sun},
  year={2023},
  eprint={2304.03170},
  archivePrefix={arXiv},
  primaryClass={cs.SI}
}

ICML 2022

A Tighter Analysis of Spectral Clustering, and Beyond

Peter Macgregor and He Sun

arXiv version conference version talk code

@inproceedings{macgregorTighterAnalysisSpectral2022,
    title = {A Tighter Analysis of Spectral Clustering, and Beyond},
    booktitle = {39th {{International Conference}} on {{Machine Learning}} ({{ICML}}'22)},
    author = {Macgregor, Peter and Sun, He},
    year = {2022},
    pages = {14717--14742}
}

NeurIPS 2021

Finding Bipartite Components in Hypergraphs

Peter Macgregor and He Sun

arXiv version conference version talk code

@inproceedings{macgregorFindingBipartiteComponents2021,
    title = {Finding Bipartite Components in Hypergraphs},
    booktitle = {34th {{Advances}} in {{Neural Information Processing Systems}} ({{NeurIPS}}'21)},
    author = {Macgregor, Peter and Sun, He},
    year = {2021},
    pages = {7912--7923}
}

ICML 2021 Oral

Local Algorithms for Finding Densely Connected Clusters

Peter Macgregor and He Sun

arXiv version conference version talk code demo

@inproceedings{macgregorLocalAlgorithmsFinding2021,
  title = {Local Algorithms for Finding Densely Connected Clusters},
  booktitle = {38th {{International Conference}} on {{Machine Learning}} ({{ICML}}'21)},
  author = {Macgregor, Peter and Sun, He},
  year = {2021},
  pages = {7268--7278}
}

Thesis

arXiv version

On Learning the Structure of Clusters in Graphs

Peter Macgregor, PhD Thesis, University of Edinburgh, 2022

@phdthesis{macgregorLearningStructureClusters2022,
    title = {On Learning the Structure of Clusters in Graphs},
    author = {Macgregor, Peter},
    year = {2022},
    school = {University of Edinburgh}
}

Summary

Publications

NeurIPS 2023 Spotlight

Fast Approximation of Similarity Graphs with Kernel Density Estimation

NeurIPS 2023

Fast and Simple Spectral Clustering in Theory and Practice

ISAAC 2023

Is the Algorithmic Kadison-Singer Problem Hard?

Tech. Report

Spectral Toolkit of Algorithms for Graphs: Technical Report (1)

ICML 2022

A Tighter Analysis of Spectral Clustering, and Beyond

NeurIPS 2021

Finding Bipartite Components in Hypergraphs

ICML 2021 Oral

Local Algorithms for Finding Densely Connected Clusters

Thesis

arXiv version

On Learning the Structure of Clusters in Graphs

Talks

Edinburgh LFCS 2022

MathWorks 2022

Beyond Symmetric Clusters: Finding Inter-Cluster Structure in Graphs

Edinburgh LFCS 2023

How to Cluster a Million Data Points While the Kettle Boils