Tamara Broderick

Selected Papers


Links to code are next to the relevant papers.

Coming Soon

  • Campbell, T, Cai, D, Broderick, T.
    Exchangeable trait allocations. Preprint on arXiv:1609.09147 [math.ST] [arXiv]
  • Campbell, T*, Huggins, JH*, How, J, and Broderick, T.
    Truncated random measures. Preprint on arXiv:1603.00861 [math.ST] [arXiv]
  • Broderick, T, Wilson, AC, and Jordan, MI.
    Posteriors, conjugacy, and exponential families for completely random measures. Bernoulli, to appear. [link to forthcoming papers] [arXiv]

Refereed

  • Stephenson, W and Broderick, T.
    Understanding Covariance Estimates in Expectation Propagation. NIPS 2016 Workshop on Advances in Approximate Bayesian Inference. 2016. [link pdf] [workshop link]
  • Guo, F, Wang, X, Fan, K, Broderick, T, and Dunson, D.
    Boosting Variational Inference. NIPS 2016 Workshop on Advances in Approximate Bayesian Inference. 2016. [link pdf] [workshop link] [arXiv]
  • Giordano, R, Broderick, T, and Jordan, MI.
    Fast Measurements of Robustness to Changing Priors in Variational Bayes. NIPS 2016 Workshop on Advances in Approximate Bayesian Inference. 2016. [link pdf] [workshop link] [arXiv]
  • Cai, D, Campbell, T, and Broderick, T.
    Paintboxes and probability functions for edge-exchangeable graphs. NIPS 2016 Workshop on Adaptive and Scalable Nonparametric Methods in Machine Learning. 2016. [link pdf] [workshop link]
  • Campbell, T, Cai, D, and Broderick, T.
    A Paintbox Representation of Exchangeable Trait Allocations. NIPS 2016 Workshop on Practical Bayesian Nonparametrics. 2016. [link pdf] [workshop link]
  • Huggins, JH, Campbell, T, and Broderick, T.
    Coresets for scalable Bayesian logistic regression. Neural Information Processing Systems. 2016. [link] [arXiv] [spotlight video and more info]
  • Cai, D, Campbell, T, and Broderick, T.
    Edge-exchangeable graphs and sparsity. Neural Information Processing Systems. 2016. [link] [spotlight video and more info]
    Earlier versions:
    • Cai, D, Broderick, T.
      Completely random measures for modeling power laws in sparse graphs. NIPS 2015 Workshop on Networks in the Social and Information Sciences. 2015. [link pdf] [workshop link] [arXiv]
    • Broderick, T, and Cai, D.
      Edge-exchangeable graphs and sparsity. NIPS 2015 Workshop on Networks in the Social and Information Sciences. 2015. [link pdf] [workshop link] [arXiv]
    • Broderick, T, and Cai, D.
      Edge-exchangeable graphs, sparsity, and power laws. NIPS 2015 Workshop: Bayesian Nonparametrics: The Next Generation. 2015. [link pdf] [workshop link]
  • Giordano, R, Broderick, T, Meager, R, Huggins, JH, and Jordan, MI.
    Fast robustness quantification with variational Bayes. ICML 2016 Workshop on #Data4Good: Machine Learning in Social Good Applications. 2016. [workshop link] [arXiv]
  • Giordano, R, Broderick, T, and Jordan, MI.
    Robust inference with variational Bayes. NIPS 2015 Workshop on Advances in Approximate Bayesian Inference. 2015. [link pdf] [workshop link] [arXiv]
  • Giordano, R, Broderick, T, and Jordan, MI.
    Linear response methods for accurate covariance estimates from mean field variational Bayes. Neural Information Processing Systems. 2015. [link] [workshop link] [arXiv] [github code]
    Earlier versions:
    • Giordano, R and Broderick, T.
      Covariance matrices for mean field variational Bayes. NIPS 2014 Workshop on Advances in Variational Inference. 2014. [link pdf] [workshop link] [arXiv]
  • Broderick, T, Mackey, L, Paisley, J, and Jordan, MI.
    Combinatorial clustering and the beta negative binomial process. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2015 (2011 arXiv). [link] [arXiv] [code: zip]
  • Broderick, T and Steorts, RC.
    Variational Bayes for merging noisy databases. NIPS 2014 Workshop on Advances in Variational Inference. 2014. [link] [workshop link] [arXiv]
  • Luts, J, Broderick, T, and Wand, MP.
    Real-time semiparametric regression. Journal of Computational and Graphical Statistics. 2014. [link] [arXiv]
  • Broderick, T, Pitman, J, and Jordan, MI.
    Feature allocations, probability functions, and paintboxes. Bayesian Analysis. 2013. [linked pdf] [arXiv]
  • Broderick, T, Boyd, N, Wibisono, A, Wilson, AC, and Jordan, MI.
    Streaming variational Bayes. Neural Information Processing Systems. 2013. [link] [arXiv] [github code]
  • Pan, X, Gonzalez, JE, Jegelka, S, Broderick, T, and Jordan, MI.
    Optimistic concurrency control for distributed unsupervised learning. Neural Information Processing Systems. 2013. [link] [arXiv]
  • Broderick, T, Jordan, MI, and Pitman, J.
    Cluster and feature modeling from combinatorial stochastic processes. Statistical Science. 2013. [link] [arXiv]
  • Broderick, T, Kulis, B, and Jordan, MI.
    MAD-Bayes: MAP-based asymptotic derivations from Bayes. Proceedings of the 30th International Conference on Machine Learning. 2013. [link] [arXiv] [github code]
  • Broderick, T, Jordan, MI, and Pitman, J.
    Beta processes, stick-breaking, and power laws. Bayesian Analysis. 2012. [linked pdf] [arXiv]
  • Morgan, AN, Long, J, Richards, JW, Broderick, T, Butler, NR, Bloom, JS.
    Rapid, machine-learned resource allocation: application to high-redshift GRB follow-up. Astrophysical Journal. 2012. [link] [arXiv]
  • Broderick, T and Gramacy, RB.
    Classification and categorical inputs with treed Gaussian process models. Journal of Classification. 2011. [link] [arXiv]
  • Broderick, T, Wong-Lin, KF, and Holmes, P.
    Closed-form approximations of first-passage distributions for a stochastic decision making model. Applied Mathematics Research Express. 2010. [link]
  • Kapicioglu, B, Schapire, RE, Wikelski, M, and Broderick, T.
    Combining spatial and telemetric features for learning animal movement models. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence. 2010. [linked pdf]
  • Broderick, T and MacKay, DJC.
    Fast and flexible selection with a single switch. PLoS ONE 4(10), e7481. 2009. [link] [arXiv] [further resources and code link]
  • Broderick, T and Gramacy, RB.
    Treed Gaussian process models for classification. Proceedings of the 11th IFCS Biennial Conference. 2009. [Springer]
  • Mandelbaum, R, Hirata, CM, Broderick, T, Seljak, U, and Brinkmann, J.
    Ellipticity of dark matter halos with galaxy-galaxy weak lensing. Monthly Notices of the Royal Astronomical Society 370, 1008-1024. 2006. [arXiv]
  • Huterer, D, Kim, A, Krauss, LM, and Broderick, T.
    Redshift accuracy requirements for future supernova and number count surveys. Astrophysical Journal 615, 595-602. 2004. [arXiv]

Theses

  • Broderick, T.
    Clusters and features from combinatorial stochastic processes. PhD Dissertation. Department of Statistics, UC Berkeley. 2014.
  • Broderick, T.
    Nomon: Efficient communication with a single switch. Technical Report (extension to Master's Thesis). Cavendish Laboratory, University of Cambridge. 2009. [pdf]
  • Broderick, T.
    Treed models and Gaussian processes for classification. Master of Advanced Study in Mathematics (Part III) Essay. Department of Pure Mathematics and Mathematical Statistics, University of Cambridge. 2008.
  • Broderick, T.
    Construction of a pairwise Ising distribution over a large state space with sparse data. Senior Thesis. Mathematics Department, Program in Applications of Computing, Program in Applied and Computational Mathematics, Princeton University. 2007.

Technical Reports and Notes

  • Giordano, R and Broderick, T.
    Covariance matrices and influence scores for mean field variational Bayes. Preprint on arXiv:1502.07685 [stat.ML] [arXiv]
  • MacKay, DJC and Broderick, T.
    Probabilities over trees: generalizations of the Dirichlet Diffusion Tree and the Kingman Coalescent. 2007. [link]
  • Broderick, T, Dudik, M, Tkacik, G, Schapire, RE, and Bialek, W.
    Faster solutions of the inverse pairwise Ising problem. Preprint on arXiv:0712.2437 [q-bio.QM]. 2007. [arXiv]

Translations

  • Alvarez-Melis, D and Broderick, T.
    A translation of "The characteristic function of a random phenomenon" by Bruno de Finetti. On arXiv:1512.01229 [math.ST] [arXiv]