BigBayes

As datasets grow ever larger in scale, complexity and variety, there is an increasing need for powerful machine learning and statistical techniques that are capable of learning from such data. Bayesian nonparametrics is a promising approach to data analysis that is increasingly popular in machine learning and statistics. Bayesian nonparametric models are highly flexible models with infinite-dimensional parameter spaces that can be used to directly parameterise and learn about functions, densities, conditional distributions etc. This ERC funded project aims to develop Bayesian nonparametric techniques for learning rich representations from structured data in a computationally efficient and scalable manner.

Publications

2017

  • S. Flaxman, Y. W. Teh, D. Sejdinovic, Poisson Intensity Estimation with Reproducing Kernels, in Artificial Intelligence and Statistics (AISTATS), 2017, to appear.
    Project: bigbayes
  • Q. Zhang, S. Filippi, A. Gretton, D. Sejdinovic, Large-Scale Kernel Methods for Independence Testing, Statistics and Computing, to appear, 2017.
    Project: bigbayes
  • M. Battiston, S. Favaro, Y. W. Teh, Multi-armed bandit for species discovery: A Bayesian nonparametric approach, Journal of the American Statistical Association, to appear, 2017.
    Project: bigbayes

2016

  • C. Loeffler, S. Flaxman, Is Gun Violence Contagious?, 2016.
    Project: bigbayes
  • B. Goodman, S. Flaxman, European Union regulations on algorithmic decision-making and a “right to explanation,” Jun-2016.
    Project: bigbayes
  • S. Flaxman, D. Sutherland, Y. Wang, Y. W. Teh, Understanding the 2016 US Presidential Election using ecological inference and distribution regression with census microdata, Arxiv e-prints, Nov-2016.
    Project: bigbayes
  • K. Palla, F. Caron, Y. W. Teh, A Bayesian nonparametric model for sparse dynamic networks, Jun-2016.
    Project: bigbayes
  • K. Palla, D. Knowles, Z. Ghahramani, A birth-death process for feature allocation , 2016.
    Project: bigbayes
  • N. Heard, K. Palla, M. Skoularidou, Topic modelling of authentication events in an enterprise computer network, 2016.
    Project: bigbayes
  • J. Mitrovic, D. Sejdinovic, Y. W. Teh, DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression, in International Conference on Machine Learning (ICML), 2016, 1482–1491.
    Project: bigbayes
  • S. Flaxman, D. Sejdinovic, J. Cunningham, S. Filippi, Bayesian Learning of Kernel Embeddings, in Uncertainty in Artificial Intelligence (UAI), 2016, 182–191.
    Project: bigbayes
  • T. Fernandez, Y. W. Teh, Posterior Consistency for a Non-parametric Survival Model under a Gaussian Process Prior, 2016.
    Project: bigbayes
  • V. Perrone, P. A. Jenkins, D. Spano, Y. W. Teh, Poisson Random Fields for Dynamic Feature Models, 2016.
    Project: bigbayes
  • T. Fernandez, N. Rivera, Y. W. Teh, Gaussian Processes for Survival Analysis, in Advances in Neural Information Processing Systems (NIPS), 2016.
    Project: bigbayes
  • H. Kim, Y. W. Teh, Scalable Structure Discovery in Regression using Gaussian Processes, in Proceedings of the 2016 Workshop on Automatic Machine Learning, 2016.
    Project: bigbayes
  • L. T. Elliott, Y. W. Teh, A Nonparametric HMM for Genetic Imputation and Coalescent Inference, Electronic Journal of Statistics, 2016.
    Project: bigbayes
  • S. Favaro, A. Lijoi, C. Nava, B. Nipoti, I. Prüenster, Y. W. Teh, On the Stick-Breaking Representation for Homogeneous NRMIs, Bayesian Analysis, vol. 11, 697–724, 2016.
    Project: bigbayes
  • Y. W. Teh, Bayesian Nonparametric Modelling and the Ubiquitous Ewens Sampling Formula, Statistical Science, vol. 31, no. 1, 34–36, 2016.
    Project: bigbayes
  • M. Balog, B. Lakshminarayanan, Z. Ghahramani, D. M. Roy, Y. W. Teh, The Mondrian Kernel, in Uncertainty in Artificial Intelligence (UAI), 2016.
    Project: bigbayes
  • J. Arbel, S. Favaro, B. Nipoti, Y. W. Teh, Bayesian nonparametric inference for discovery probabilities: credible intervals and large sample asymptotics, Statistica Sinica, 2016.
    Project: bigbayes
  • B. Lakshminarayanan, D. M. Roy, Y. W. Teh, Mondrian Forests for Large-Scale Regression when Uncertainty Matters, in Artificial Intelligence and Statistics (AISTATS), 2016.
    Project: bigbayes
  • H. Kim, X. Lu, S. Flaxman, Y. W. Teh, Tucker Gaussian Process for Regression and Collaborative Filtering, 2016.
    Project: bigbayes
  • M. Battiston, S. Favaro, D. M. Roy, Y. W. Teh, A Characterization of Product-Form Exchangeable Feature Probability Functions, 2016.
    Project: bigbayes
  • K. Palla, F. Caron, Y. W. Teh, Bayesian Nonparametrics for Sparse Dynamic Networks, 2016.
    Project: bigbayes

2015

  • A. G. Deshwar, L. Boyles, J. Wintersinger, P. C. Boutros, Y. W. Teh, Q. Morris, Abstract B2-59: PhyloSpan: using multimutation reads to resolve subclonal architectures from heterogeneous tumor samples, Cancer Research, vol. 75, 2015.
    Project: bigbayes
  • S. Favaro, B. Nipoti, Y. W. Teh, Rediscovery of Good-Turing Estimators via Bayesian Nonparametrics, Biometrics, 2015.
    Project: bigbayes
  • P. G. Moreno, A. Artés-Rodríguez, Y. W. Teh, F. Perez-Cruz, Bayesian Nonparametric Crowdsourcing, Journal of Machine Learning Research (JMLR), 2015.
    Project: bigbayes
  • M. Lomeli, S. Favaro, Y. W. Teh, A hybrid sampler for Poisson-Kingman mixture models, in Advances in Neural Information Processing Systems (NIPS), 2015.
    Project: bigbayes
  • M. De Iorio, S. Favaro, Y. W. Teh, Bayesian Inference on Population Structure: From Parametric to Nonparametric Modeling, in Nonparametric Bayesian Inference in Biostatistics, Springer, 2015.
    Project: bigbayes
  • S. Favaro, B. Nipoti, Y. W. Teh, Random variate generation for Laguerre-type exponentially tilted α-stable distributions, Electronic Journal of Statistics, vol. 9, 1230–1242, 2015.
    Project: bigbayes
  • M. Lomeli, S. Favaro, Y. W. Teh, A Marginal Sampler for σ-Stable Poisson-Kingman Mixture Models, jcgs, 2015.
    Project: bigbayes
  • M. Balog, Y. W. Teh, The Mondrian Process for Machine Learning, 2015.
    Project: bigbayes
  • P. Orbanz, L. James, Y. W. Teh, Scaled subordinators and generalizations of the Indian buffet process, 2015.
    Project: bigbayes
  • M. De Iorio, L. Elliott, S. Favaro, Y. W. Teh, Bayesian Nonparametric Inference of Population Admixtures, 2015.
    Project: bigbayes
  • B. Lakshminarayanan, D. M. Roy, Y. W. Teh, Particle Gibbs for Bayesian Additive Regression Trees, in Proceedings of the International Conference on Artificial Intelligence and Statistics, 2015.
    Project: bigbayes sgmcmc

2014

  • S. Favaro, M. Lomeli, Y. W. Teh, On a Class of σ-stable Poisson-Kingman Models and an Effective Marginalized Sampler, Statistics and Computing, 2014.
    Project: bigbayes
  • S. Favaro, M. Lomeli, B. Nipoti, Y. W. Teh, On the Stick-Breaking Representation of σ-stable Poisson-Kingman Models, Electronic Journal of Statistics, vol. 8, 1063–1085, 2014.
    Project: bigbayes
  • B. Lakshminarayanan, D. Roy, Y. W. Teh, Mondrian Forests: Efficient Online Random Forests, in Advances in Neural Information Processing Systems (NIPS), 2014.
    Project: bigbayes

Software

2017

  • S. Flaxman, Y. W. Teh, D. Sejdinovic, Kernel Poisson. 2017.
    Project: bigbayes

2016

  • B. Lakshminarayanan, D. M. Roy, Y. W. Teh, Mondrian Forest. 2016.
    Project: bigbayes
  • L. Elliott, Y. W. Teh, BNPPhase. 2016.
    Project: bigbayes

2015

  • B. Lakshminarayanan, D. M. Roy, Y. W. Teh, PGBart. 2015.
    Project: bigbayes
  • M. De Iorio, L. T. Elliott, S. Favaro, Y. W. Teh, HDPStructure. 2015.
    Project: bigbayes
  • L. Boyles, Y. W. Teh, CPABS: Cancer Phylogenetic Reconstruction with Aldous’ Beta Splitting. 2015.
    Project: bigbayes