Research Projects

Theory of Bayesian Nonparametrics

Sequential representations and error bounds for truncated random measures

[Paper]
pub_trunccrm
[Slides]

Paintboxes and probability functions for exchangeable trait allocations

[Paper]
pub_traitexch

Sparse exchangeable network models

[Paper]
pub_sparsegraphs
[Video]

Streaming, Distributed, Asynchronous Inference

Making big data smaller with Bayesian coresets

[Paper]
pub_coresets
[Code] [Video]

Merging Bayesian nonparametric posteriors with bipartite matching

[Paper]
pub_sdabnp
[Slides] [Poster]

Merging symmetric posteriors with combinatorial optimization

[Paper]
pub_sda
[Slides] [Poster] [MIT News Feature] [Popular Science]

Low-Variance Bayesian Nonparametrics

Tractable clustering for dynamic data

[Paper]
pub_ddp
[Slides] [Poster] [Code]

Dynamic von-Mises-Fisher mixtures for robotic perception

[Paper]
pub_sphereddp
[Slides] [Poster] [Code] [Video]

Optimization with Bayesian Nonparametrics

Efficient global point cloud alignment

[Paper]
pub_ptcldalign
[Code]

Tractable nonconvex uncertainty sets via the Dirichlet process mixture

[Paper]
pub_robustdp
[Slides]

Publications

Published

  • Coresets for scalable Bayesian logistic regression
    J. Huggins, T. Campbell, and T. Broderick
    Advances in Neural Information Processing Systems, 2016
    [NIPS] [arXiv]
    [BibTeX]
    @inproceedings{Huggins16_NIPS,
        author={Jonathan Huggins and Trevor Campbell and Tamara Broderick},
        title={Coresets for scalable {B}ayesian logistic regression},
        year={2016},
        booktitle={Advances in Neural Information Processing Systems}
    }
    
  • Edge-exchangeable graphs and sparsity
    D. Cai, T. Campbell, and T. Broderick
    Advances in Neural Information Processing Systems, 2016
    [NIPS]
    [BibTeX]
    @inproceedings{Cai16_NIPS,
        author={Diana Cai and Trevor Campbell and Tamara Broderick},
        title={Edge-exchangeable graphs and sparsity},
        year={2016},
        booktitle={Advances in Neural Information Processing Systems}
    }
    
  • Streaming, distributed variational inference for Bayesian nonparametrics
    T. Campbell, J. Straub, J.W. Fisher III, and J.P. How
    Advances in Neural Information Processing Systems, 2015
    [NIPS] [Supplement] [arXiv]
    [BibTeX]
    @inproceedings{Campbell15_NIPS,
        author={Trevor Campbell and Julian Straub and John W.~Fisher III, and Jonathan P.~How},
        title={Streaming, distributed variational inference for {B}ayesian nonparametrics},
        year={2015},
        booktitle={Advances in Neural Information Processing Systems}
    }
    
  • Small-variance nonparametric clustering on the hypersphere
    J. Straub, T. Campbell, J. P. How, and J. W. Fisher III
    IEEE Conference on Computer Vision and Pattern Recognition, 2015
    [CVPR] [Supplement]
    [BibTeX]
    @inproceedings{Straub15_CVPR,
        author = {Julian Straub and Trevor Campbell and Jonathan P.~How, and John W.~Fisher III},
        title = {Small-variance nonparametric clustering on the hypersphere},
        year = {2015},
        booktitle = {IEEE Conference on Computer Vision and Pattern Recognition}
    }
    
  • Bayesian nonparametric set construction for robust optimization
    T. Campbell and J. P. How
    American Control Conference, 2015
    [IEEE Explore]
    [BibTeX]
    @inproceedings{Campbell15_ACC,
        author = {Trevor Campbell and Jonathan P.~How},
        title = {Bayesian nonparametric set construction for robust optimization},
        year = {2015},
        booktitle = {American Controls Conference}
    }
    
  • Approximate decentralized Bayesian inference
    T. Campbell and J. P. How
    Uncertainty in Artificial Intelligence, 2014
    [UAI] [arXiv]
    [BibTeX]
    @inproceedings{Campbell14_UAI,
        author = {Trevor Campbell and Jonathan P.~How},
        title = {Approximate decentralized {B}ayesian inference},
        year = {2014},
        booktitle = {Uncertainty in Artificial Intelligence}
    }
    
  • Dynamic clustering via asymptotics of the dependent Dirichlet process mixture
    T. Campbell, M. Liu, B. Kulis, L. Carin, and J. P. How
    Advances in Neural Information Processing Systems, 2013
    [NIPS] [arXiv]
    [BibTeX]
    @inproceedings{Campbell13_NIPS,
        author = {Trevor Campbell and Miao Liu and Brian Kulis and Jonathan P.~How and Lawrence Carin},
        title = {Dynamic clustering via asymptotics of the dependent {D}irichlet process mixture},
        year = {2013},
        booktitle = {Advances in Neural Information Processing Systems}
    }
    
  • Multiagent allocation of Markov decision process tasks
    T. Campbell, L. Johnson, and J. P. How
    American Control Conference, 2013
    [IEEE Explore]
    [BibTeX]
    @inproceedings{Campbell13_ACC,
        author = {Trevor Campbell and Luke Johnson and Jonathan P.~How},
        title = {Multiagent allocation of {M}arkov decision process tasks},
        year = {2013},
        booktitle = {American Controls Conference}
    }
    
  • Simultaneous clustering on representation expansion for learning multimodel MDPs
    T. Campbell, R. H. Klein, A. Geramifard, and J. P. How
    Reinforcement Learning and Decision Making, 2013
    [PDF] [RLDM]
    [BibTeX]
    @inproceedings{Campbell13_RLDM,
        author = {Trevor Campbell and Robert Klein and Alborz Geramifard and Jonathan P.~How},
        title = {Simultaneous clustering on representation expansion for learning multimodel {MDPs}},
        year = {2013},
        booktitle = {Reinforcement Learning and Decision Making}
    }
    

Preprints

  • Exchangeable trait allocations
    T. Campbell, D. Cai, and T. Broderick
    [arXiv]
  • Truncated random measures
    T. Campbell*, J. Huggins*, J. P. How, and T. Broderick
    [arXiv]
  • Efficient global point cloud alignment using Bayesian nonparametric mixtures
    J. Straub*, T. Campbell*, J. P. How, and J. W. Fisher III
    [arXiv]
  • Dynamic clustering algorithms via small-variance analysis of the Markov dependent Dirichlet process
    T. Campbell, B. Kulis, and J. P. How

Theses

  • Truncated Bayesian nonparametrics
    T. Campbell
    Doctoral Thesis, Massachusetts Institute of Technology, 2016
    [Defense Slides]
  • Multiagent planning with Bayesian nonparametric asymptotics
    T. Campbell
    Master's Thesis, Massachusetts Institute of Technology, 2013