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
2019

J. Lee
,
Y. Lee
,
J. Kim
,
A. Kosiorek
,
S. Choi
,
Y. W. Teh
,
Set Transformer: A Framework for Attentionbased PermutationInvariant Neural Networks, in International Conference on Machine Learning (ICML), 2019.
Project: bigbayes 
L. T. Elliott
,
M. De Iorio
,
S. Favaro
,
K. Adhikari
,
Y. W. Teh
,
Modeling Population Structure Under Hierarchical Dirichlet Processes, Bayesian Analysis, Jun. 2019.
Project: bigbayes 
S. Webb
,
T. Rainforth
,
Y. W. Teh
,
M. P. Kumar
,
A Statistical Approach to Assessing Neural Network Robustness, in International Conference on Learning Representations (ICLR), 2019.
Project: bigbayes 
J. Lee
,
L. James
,
S. Choi
,
F. Caron
,
A Bayesian model for sparse graphs with flexible degree distributionand overlapping community structure, in Artificial Intelligence and Statistics (AISTATS), 2019.
Project: bigbayes 
B. BloemReddy
,
Y. W. Teh
,
Probabilistic symmetry and invariant neural networks, Jan. 2019.
Project: bigbayes 
S. Flaxman
,
M. Chirico
,
P. Pereira
,
C. Loeffler
,
Scalable highresolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ “RealTime Crime Forecasting Challenge,” Revised and resubmit at Annals of Applied Statistics, 2019.
Project: bigbayes 
A. Raj
,
H. C. L. Law
,
D. Sejdinovic
,
M. Park
,
A Differentially Private Kernel TwoSample Test, in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), 2019, to appear.
Project: bigbayes 
E. Mathieu
,
T. Rainforth
,
N. Siddharth
,
Y. W. Teh
,
Disentangling Disentanglement in Variational Autoencoders, in Proceedings of the 36th International Conference on Machine Learning, Long Beach, California, USA, 2019, vol. 97, 4402–4412.
Project: bigbayes 
Y. Zhou
,
B. GramHansen
,
T. Kohn
,
T. Rainforth
,
H. Yang
,
F. Wood
,
A LowLevel Probabilistic Programming Language
for NonDifferentiable Models, International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
Project: bigbayes 
J. Ton
,
L. Chan
,
Y. W. Teh
,
D. Sejdinovic
,
Noise Contrastive MetaLearning for Conditional Density Estimation using Kernel Mean Embeddings, ArXiv eprints:1906.02236, 2019.
Project: bigbayes tencentlsml
2018

X. Miscouridou
,
F. Caron
,
Y. W. Teh
,
Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data, in Advances in Neural Information Processing Systems (NeurIPS), 2018.
Project: bigbayes 
A. Golinski
,
Y. W. Teh
,
F. Wood
,
T. Rainforth
,
Amortized Monte Carlo Integration, in Symposium on Advances in Approximate Bayesian Inference, 2018.
Project: bigbayes 
J. Mitrovic
,
D. Sejdinovic
,
Y. Teh
,
Causal Inference via Kernel Deviance Measures, in Advances in Neural Information Processing Systems (NeurIPS), 2018.
Project: bigbayes 
J. Chen
,
J. Zhu
,
Y. W. Teh
,
T. Zhang
,
Stochastic Expectation Maximization with Variance Reduction, in Advances in Neural Information Processing Systems (NeurIPS), 2018, 7978–7988.
Project: bigbayes tencentlsml 
B. BloemReddy
,
A. Foster
,
E. Mathieu
,
Y. W. Teh
,
Sampling and Inference for Beta NeutraltotheLeft Models of Sparse Networks, in Conference on Uncertainty in Artificial Intelligence, 2018.
Project: bigbayes 
B. BloemReddy
,
P. Orbanz
,
RandomWalk Models of Network Formation and Sequential Monte Carlo Methods for Graphs, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 80, no. 5, 871–898, Aug. 2018.
Project: bigbayes 
T. Rainforth
,
A. R. Kosiorek
,
T. A. Le
,
C. J. Maddison
,
M. Igl
,
F. Wood
,
Y. W. Teh
,
Tighter Variational Bounds are Not Necessarily Better, in International Conference on Machine Learning (ICML), 2018.
Project: bigbayes 
X. Miscouridou
,
A. Perotte
,
N. Elhadad
,
R. Ranganath
,
Deep Survival Analysis: Nonparametrics and Missingness, in pmlr, 2018.
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. 28, Jun. 2018.
Project: bigbayes 
H. Kim
,
Y. W. Teh
,
Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes, in Artificial Intelligence and Statistics (AISTATS), 2018.
Project: bigbayes 
Q. Zhang
,
S. Filippi
,
A. Gretton
,
D. Sejdinovic
,
LargeScale Kernel Methods for Independence Testing, Statistics and Computing, vol. 28, no. 1, 113–130, Jan. 2018.
Project: bigbayes 
F. Ayed
,
M. Battiston
,
F. Camerlenghi
,
S. Favaro
,
Consistent estimation of the missing mass for feature models, 2018.
Project: bigbayes 
M. Battiston
,
S. Favaro
,
Y. W. Teh
,
Bayesian nonparametric approaches to samplesize estimation for finding unseen species, 2018.
Project: bigbayes 
F. Ayed
,
M. Battiston
,
F. Camerlenghi
,
S. Favaro
,
On the consistent estimation of the missing mass, 2018.
Project: bigbayes 
F. Ayed
,
M. Battiston
,
F. Camerlenghi
,
S. Favaro
,
On the GoodTuring estimator for feature allocation models, 2018.
Project: bigbayes 
B. BloemReddy
,
Y. W. Teh
,
Neural network models of exchangeable sequences, NeurIPS Workshop on Bayesian Deep Learning, 2018.
Project: bigbayes 
C. Loeffler
,
S. Flaxman
,
Is gun violence contagious? A spatiotemporal test, Journal of Quantitative Criminology, vol. 34, no. 4, 999–1017, 2018.
Project: bigbayes 
A. Foster
,
M. Jankowiak
,
E. Bingham
,
Y. W. Teh
,
T. Rainforth
,
N. Goodman
,
Variational Optimal Experiment Design: Efficient Automation of Adaptive Experiments, NeurIPS Workshop on Bayesian Deep Learning, 2018.
Project: bigbayes 
S. Webb
,
A. Golinski
,
R. Zinkov
,
N. Siddharth
,
T. Rainforth
,
Y. W. Teh
,
F. Wood
,
Faithful Inversion of Generative Models for Effective Amortized Inference, in Advances in Neural Information Processing Systems (NeurIPS), 2018.
Project: bigbayes 
A. R. Kosiorek
,
H. Kim
,
Y. W. Teh
,
I. Posner
,
Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects, in Advances in Neural Information Processing Systems (NeurIPS), 2018.
Project: bigbayes 
H. Kim
,
A. Mnih
,
Disentangling by Factorising, in International Conference on Machine Learning (ICML), 2018.
Project: bigbayes 
H. Law
,
D. Sejdinovic
,
E. Cameron
,
T. Lucas
,
S. Flaxman
,
K. Battle
,
K. Fukumizu
,
Variational Learning on Aggregate Outputs with Gaussian Processes, in Advances in Neural Information Processing Systems (NeurIPS), 2018, to appear.
Project: bigbayes 
J. Heo
,
H. Lee
,
S. Kim
,
J. Lee
,
K. Kim
,
E. Yang
,
S. Hwang
,
Uncertaintyaware attention for reliable interpretation and prediction, in Advances in Neural Information Processing Systems (NeurIPS), 2018.
Project: bigbayes 
H. Lee
,
J. Lee
,
S. Kim
,
E. Yang
,
S. Hwang
,
DropMax: adaptive variational softmax, in Advances in Neural Information Processing Systems (NeurIPS), 2018.
Project: bigbayes 
T. Rainforth
,
Y. Zhou
,
X. Lu
,
Y. W. Teh
,
F. Wood
,
H. Yang
,
J. Meent
,
Inference Trees: Adaptive Inference with Exploration, arXiv preprint arXiv:1806.09550, 2018.
Project: bigbayes 
X. Lu
,
T. Rainforth
,
Y. Zhou
,
J. Meent
,
Y. W. Teh
,
On Exploration, Exploitation and Learning in Adaptive Importance Sampling, arXiv preprint arXiv:1810.13296, 2018.
Project: bigbayes 
T. Rainforth
,
Nesting Probabilistic Programs, Conference on Uncertainty in Artificial Intelligence (UAI), 2018.
Project: bigbayes 
T. Rainforth
,
R. Cornish
,
H. Yang
,
A. Warrington
,
F. Wood
,
On Nesting Monte Carlo Estimators, International Conference on Machine Learning (ICML), 2018.
Project: bigbayes 
J. Ton
,
S. Flaxman
,
D. Sejdinovic
,
S. Bhatt
,
Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features, Spatial Statistics, vol. 28, 59–78, 2018.
Project: bigbayes 
H. Law
,
D. Sutherland
,
D. Sejdinovic
,
S. Flaxman
,
Bayesian Approaches to Distribution Regression, in Artificial Intelligence and Statistics (AISTATS), 2018.
Project: bigbayes
2017

V. Perrone
,
P. A. Jenkins
,
D. Spano
,
Y. W. Teh
,
Poisson Random Fields for Dynamic Feature Models, Journal of Machine Learning Research (JMLR), Dec. 2017.
Project: bigbayes 
G. Di Benedetto
,
F. Caron
,
Y. W. Teh
,
Nonexchangeable random partition models for microclustering, Nov2017.
Project: bigbayes 
B. BloemReddy
,
P. Orbanz
,
Preferential Attachment and Vertex Arrival Times, Oct. 2017.
Project: bigbayes 
A. Todeschini
,
X. Miscouridou
,
F. Caron
,
Exchangeable Random Measures for Sparse and Modular Graphs with Overlapping Communities, Aug2017.
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, Apr. 2017.
Project: bigbayes 
X. Lu
,
V. Perrone
,
L. Hasenclever
,
Y. W. Teh
,
S. J. Vollmer
,
Relativistic Monte Carlo, in Artificial Intelligence and Statistics (AISTATS), 2017.
Project: bigbayes 
M. Battiston
,
S. Favaro
,
Discussion of F. Caron and E. B. Fox, "Sparse graphs using exchangeable random measures.", Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 79, no. 5, 2017.
Project: bigbayes 
S. Bacallado
,
M. Battiston
,
S. Favaro
,
L. Trippa
,
Sufficientness postulates for Gibbstype priors and hierarchial generalizations, Statistical Sciences, vol. 32, 487–500, 2017.
Project: bigbayes 
B. Goodman
,
S. Flaxman
,
European Union Regulations on Algorithmic Decision Making and a “Right to Explanation,” AI Magazine, vol. 38, no. 3, 50–58, 2017.
Project: bigbayes 
L. Hasenclever
,
S. Webb
,
T. Lienart
,
S. Vollmer
,
B. Lakshminarayanan
,
C. Blundell
,
Y. W. Teh
,
Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server, Journal of Machine Learning Research, vol. 18, no. 106, 1–37, 2017.

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 
B. BloemReddy
,
E. Mathieu
,
A. Foster
,
T. Rainforth
,
H. Ge
,
M. Lomelí
,
Z. Ghahramani
,
Y. W. Teh
,
Sampling and inference for discrete random probability measures in probabilistic programs, NIPS Workshop on Advances in Approximate Bayesian Inference, 2017.
Project: bigbayes 
S. Flaxman
,
Y. Teh
,
D. Sejdinovic
,
Poisson Intensity Estimation with Reproducing Kernels, Electronic Journal of Statistics, vol. 11, no. 2, 5081–5104, 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 
J. Mitrovic
,
D. Sejdinovic
,
Y. W. Teh
,
Deep Kernel Machines via the Kernel Reparametrization Trick, in International Conference on Learning Representations (ICLR) Workshop Track, 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 
M. Lomeli
,
S. Favaro
,
Y. W. Teh
,
A Marginal Sampler for σStable PoissonKingman Mixture Models, Journal of Computational and Graphical Statistics, 2017.
Project: bigbayes
2016

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 
T. Fernandez
,
N. Rivera
,
Y. W. Teh
,
Gaussian Processes for Survival Analysis, in Advances in Neural Information Processing Systems (NeurIPS), 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 
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 
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 (NeurIPS), 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 (NeurIPS), 2014.
Project: bigbayes
Software
2019

Y. Zhou
,
B. GramHansen
,
T. Kohn
,
T. Rainforth
,
H. Yang
,
F. Wood
,
A LowLevel Probabilistic Programming Language for NonDifferentiable Models, International Conference on Artificial Intelligence and Statistics (AISTATS). 2019.
Project: bigbayes
2017

S. Flaxman
,
M. Chirico
,
P. Pereira
,
C. Loeffler
,
Forecasting Crime in Portland. 2017.
Project: bigbayes 
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