These pages contain selected publications which serve as examples of the contributions by the researchers at Oxford and the ATI.

Publications by Oxford researchers funded by the Alan Turing Institute are included.

Consistency and fluctuations for stochastic gradient Langevin dynamics

Date: 2016

Categories:

Reference

Teh, Y.W., Thiery, A.H. and Vollmer, S.J. (2016) ‘Consistency and fluctuations for stochastic gradient Langevin dynamics’, Journal of Machine Learning Research, 17, pp. 1–33.

Full text available at www.jmlr.org

Author

Prof. Yee Whye Teh

Bayesian Learning of Kernel Embeddings

Date: 2016

Categories:

Reference

S. Flaxman, D. Sejdinovic, J. P. Cunningham, and S. Filippi, Bayesian Learning of Kernel Embeddings, in Uncertainty in Artificial Intelligence (UAI), 2016.

Full text available at www.auai.org

Authors

Prof. Dino Sejdinovic
Dr. Sarah Filippi

Global and local information in clustering labelled block models

Date: 2016

Categories:

Reference

Kanade, V., Mossel, E. and Schramm, T. (2016) ‘Global and local information in clustering labelled block models’, 18th International Workshop on Randomization and Computation (RANDOM 2014), . doi: 10.1109/TIT.2016.2516564.

No publisher link available

Author

Dr. Varun Kanade

Evaluating the Paper-to-Screen Translation of Participant-Aided Sociograms with High-Risk Participants

Date: 2016

Categories:

Reference

Hogan, B., Melville, J., Phillips II, G., Janulis, P., Contractor, N., Mustanski, B., & Birkett, M. (2016) ‘Evaluating the Paper-to-Screen Translation of Participant-Aided Sociograms with High-Risk Participants’, 2016 Conference on Human Factors in Computing.

Full text available at dl.acm.org

Author

Dr. Bernie Hogan

DR-ABC: Approximate Bayesian computation with kernel-based distribution regression

Date: 2016

Categories:

Reference

Mitrovic, J., Sejdinovic, D. and Teh, Y.W. (2016) ‘DR-ABC: Approximate Bayesian computation with kernel-based distribution regression’, International Conference on Machine Learning (ICML).

Full text available at arxiv.org

Authors

Prof. Dino Sejdinovic
Prof. Yee Whye Teh

First-Generation Students and College: The Role of Facebook Networks as Information Sources

Date: 2016

Categories:

Reference

Jeon, G. Y., Ellison, N. B., Hogan, B., & Greenhow, C. (2016) ‘First-Generation Students and College: The Role of Facebook Networks as Information Sources’, 2016 ACM Conference on Computer-Supported Cooperative Work and Social Computing.

Full text available at dl.acm.org

Author

Dr. Bernie Hogan

Learning Linear Regression Models over Factorized Joins

Date: 2016

Categories:

Reference

Schleich, M., Olteanu, D. and Ciucanu, R. (2016) ‘Learning Linear Regression Models over Factorized Joins’, ACM SIGMOD.

Full text available at dl.acm.org

Author

Prof. Dan Olteanu

Generalized Pólya urn for time-varying Pitman-Yor processes

Date: 2016

Categories:

Reference

Caron, F., Neiswanger, W., Wood, F., Doucet, A. and Davy, M. (2016) ‘Generalized Pólya urn for time-varying Pitman-Yor processes’,Journal of Machine Learning Research.

Full text available at www.stats.ox.ac.uk

Author

Prof. Frank Wood

Inference networks for Sequential Monte Carlo in graphical models

Date: 2016

Categories:

Reference

Paige, B. and Wood, F. (2016) ‘Inference networks for Sequential Monte Carlo in graphical models’.

Full text available at arxiv.org

Author

Prof. Frank Wood

Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator

Date: 2015

Categories:

Reference

Doucet, A., Pitt, M.K., Deligiannidis, G. and John, R. (2015) ‘Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator’, Biometrika, 102(2), pp. 295–313.

Full text available at biomet.oxfordjournals.org

Authors

Prof. Arnaud Doucet
Dr. Georgios Deligiannidis