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

@inproceedings{macgregorKDESimilarityGraph2023,
    title = {Fast Approximation of Similarity Graphs with Kernel Density Estimation},
    booktitle = {36th {{Advances}} in {{Neural Information Processing Systems}} ({{NeurIPS}}'23)},
    author = {Peter Macgregor and He Sun},
    year = {2023}
}

arXiv conference code

NeurIPS 2023

Fast and Simple Spectral Clustering in Theory and Practice

Peter Macgregor

@inproceedings{macgregorFastSpectralClustering2023,
    title = {Fast and Simple Spectral Clustering in Theory and Practice},
    booktitle = {36th {{Advances}} in {{Neural Information Processing Systems}} ({{NeurIPS}}'23)},
    author = {Macgregor, Peter},
    year = {2023}
}

arXiv conference code

ISAAC 2023

Is the Algorithmic Kadison-Singer Problem Hard?

Ben Jourdan, Peter Macgregor, and He Sun

@InProceedings{jourdan_et_al:LIPIcs.ISAAC.2023.43,
  author =	{Jourdan, Ben and Macgregor, Peter and Sun, He},
  title =	{{Is the Algorithmic Kadison-Singer Problem Hard?}},
  booktitle =	{34th International Symposium on Algorithms and Computation (ISAAC 2023)},
  pages =	{43:1--43:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-289-1},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{283},
  editor =	{Iwata, Satoru and Kakimura, Naonori},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ISAAC.2023.43},
  URN =		{urn:nbn:de:0030-drops-193457},
  doi =		{10.4230/LIPIcs.ISAAC.2023.43},
  annote =	{Keywords: Kadison-Singer problem, spectral sparsification}
}

arXiv conference

Tech. Report

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

Peter Macgregor and He Sun

@article{stag,
    author       = {Peter Macgregor and He Sun},
    title        = {Spectral Toolkit of Algorithms for Graphs: Technical Report {(1)}},
    journal      = {CoRR},
    volume       = {abs/2304.03170},
    year         = {2023},
    doi          = {10.48550/arXiv.2304.03170},
    eprinttype    = {arXiv},
    eprint       = {2304.03170},
}

arXiv PDF code

ICML 2022

A Tighter Analysis of Spectral Clustering, and Beyond

Peter Macgregor and He Sun

@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}
}

arXiv conference talk code

NeurIPS 2021

Finding Bipartite Components in Hypergraphs

Peter Macgregor and He Sun

@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}
}

arXiv conference talk code

ICML 2021 Oral

Local Algorithms for Finding Densely Connected Clusters

Peter Macgregor and He Sun

@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}
}

arXiv conference talk code demo

PhD Thesis

On Learning the Structure of Clusters in Graphs

Peter Macgregor, 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

arXiv conference code bibtex

NeurIPS 2023

Fast and Simple Spectral Clustering in Theory and Practice

arXiv conference code bibtex

ISAAC 2023

Is the Algorithmic Kadison-Singer Problem Hard?

arXiv conference bibtex

Tech. Report

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

arXiv PDF code bibtex

ICML 2022

A Tighter Analysis of Spectral Clustering, and Beyond

arXiv conference talk code bibtex

NeurIPS 2021

Finding Bipartite Components in Hypergraphs

arXiv conference talk code bibtex

ICML 2021 Oral

Local Algorithms for Finding Densely Connected Clusters

arXiv conference talk code demo bibtex

PhD Thesis

On Learning the Structure of Clusters in Graphs

arXiv University version bibtex

arXiv conference code

arXiv conference code

arXiv conference

arXiv PDF code

arXiv conference talk code

arXiv conference talk code

arXiv conference talk code demo

arXiv University version