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 infinitedimensional 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

A. Todeschini,
X. Miscouridou,
F. Caron,
Exchangeable Random Measures for Sparse and Modular Graphs with Overlapping Communities, Aug2017.
Project: bigbayes 
K. Palla,
D. Belgrave,
A BirthDeath Modelling Framework for Inferring Disease Causality within the Context of Allergy Development., in 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 2017.
Project: bigbayes 
K. Palla,
D. A. Knowles,
Z. Ghahramani,
A birthdeath process for feature allocation., in Proceedings of the 34th International Conference on Machine Learning, 2017.
Project: bigbayes 
Q. Zhang,
S. Filippi,
S. Flaxman,
D. Sejdinovic,
FeaturetoFeature Regression for a TwoStep Conditional Independence Test, in Uncertainty in Artificial Intelligence (UAI), 2017.
Project: bigbayes 
S. Flaxman,
Y. W. Teh,
D. Sejdinovic,
Poisson Intensity Estimation with Reproducing Kernels, in Artificial Intelligence and Statistics (AISTATS), 2017.
Project: bigbayes 
Q. Zhang,
S. Filippi,
A. Gretton,
D. Sejdinovic,
LargeScale Kernel Methods for Independence Testing, Statistics and Computing, to appear, 2017.
Project: bigbayes 
H. Kim,
Y. W. Teh,
Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes, arXiv preprint arXiv:1706.02524, 2017.
Project: bigbayes 
M. Lomeli,
S. Favaro,
Y. W. Teh,
A Marginal Sampler for σStable PoissonKingman Mixture Models, Journal of Computational and Graphical Statistics, 2017.
Project: bigbayes 
M. Battiston,
S. Favaro,
D. M. Roy,
Y. W. Teh,
A Characterization of ProductForm Exchangeable Feature Probability Functions, Annals of Applied Probability, vol. accepted, 2017.
Project: bigbayes
2016

C. Loeffler,
S. Flaxman,
Is Gun Violence Contagious?, 2016.
Project: bigbayes 
B. Goodman,
S. Flaxman,
European Union regulations on algorithmic decisionmaking and a “right to explanation,” Jun2016.
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 eprints, Nov2016.
Project: bigbayes 
K. Palla,
F. Caron,
Y. W. Teh,
A Bayesian nonparametric model for sparse dynamic networks, Jun2016.
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,
DRABC: Approximate Bayesian Computation with KernelBased 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 Nonparametric 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,
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 LargeScale 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 
K. Palla,
F. Caron,
Y. W. Teh,
Bayesian Nonparametrics for Sparse Dynamic Networks, 2016.
Project: bigbayes 
M. Battiston,
S. Favaro,
Y. W. Teh,
Multiarmed bandit for species discovery: A Bayesian nonparametric approach, Journal of the American Statistical Association, 2016.
Project: bigbayes
2015

A. G. Deshwar,
L. Boyles,
J. Wintersinger,
P. C. Boutros,
Y. W. Teh,
Q. Morris,
Abstract B259: PhyloSpan: using multimutation reads to resolve subclonal architectures from heterogeneous tumor samples, AACR Special Conference on Computational and Systems Biology of Cancer, vol. 75, 2015.
Project: bigbayes 
S. Favaro,
B. Nipoti,
Y. W. Teh,
Rediscovery of GoodTuring Estimators via Bayesian Nonparametrics, Biometrics, 2015.
Project: bigbayes 
P. G. Moreno,
A. ArtésRodríguez,
Y. W. Teh,
F. PerezCruz,
Bayesian Nonparametric Crowdsourcing, Journal of Machine Learning Research (JMLR), 2015.
Project: bigbayes 
M. Lomeli,
S. Favaro,
Y. W. Teh,
A hybrid sampler for PoissonKingman 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 Laguerretype exponentially tilted αstable distributions, Electronic Journal of Statistics, vol. 9, 1230–1242, 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.
2014

S. Favaro,
M. Lomeli,
Y. W. Teh,
On a Class of σstable PoissonKingman Models and an Effective Marginalized Sampler, Statistics and Computing, 2014.
Project: bigbayes 
S. Favaro,
M. Lomeli,
B. Nipoti,
Y. W. Teh,
On the StickBreaking Representation of σstable PoissonKingman 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 
A. Todeschini,
X. Miscouridou,
F. Caron,
SNetOC. 2017.
Project: bigbayes
2016

V. Perrone,
P. A. Jenkins,
D. Spano,
Y. W. Teh,
NIPS 19872015 dataset. 2016.
Project: bigbayes 
B. Lakshminarayanan,
D. M. Roy,
Y. W. Teh,
Mondrian Forest. 2016.
Project: bigbayes 
L. Elliott,
Y. W. Teh,
BNPPhase. 2016.
Project: bigbayes