Email me: b.paige@ucl.ac.uk

I am an associate professor in machine learning at the University College London AI Centre.

I’m also a Turing fellow at the Alan Turing Institute, and a statistical ambassador for the Royal Statistical Society.

I’m interested in developing interpretable machine learning models which complement human expertise, rather than attempt to replace it. For a high-level overview on some recent work, here’s a video of a talk I gave at the PROBPROG conference in Boston in September, 2018.

Previously I was a Turing research fellow based at the Alan Turing Institute; before that I was a DPhil student at University of Oxford, working with Frank Wood. My thesis focused on sequential Monte Carlo methods, and their application to general-purpose inference in probabilistic programs. A cliff-notes version is available as this short talk from back when I had long hair.

The Molecule Chef, presented at NeurIPS 2019, introduces a new deep generative model for molecules, which produces novel molecules by selecting combinations of easily available precursors which will react together when run through a reaction predictor. This is follow-up work to our earlier papers on molecular search using discrete deep generative models, and modeling chemical reactions while respecting chemical constraints.

An introduction to probabilistic programming, co-written with Jan-Willem, Hongseok, and Frank, is a textbook-length tutorial on developing probabilistic programming systems and that can perform automatic Bayesian inference on a large class of models.

*This list tends to be slightly outdated;
please also refer to my google scholar profile.*

Camuto, A., Willetts, M., **Paige, B.**, Holmes, C., & Roberts, S. (2020).
Learning Bijective Feature Maps for Linear ICA.
[preprint].

**Paige, B.**, Bell, J., Bellet, A., Gascón, A., & Ezer, D. (2020).
Reconstructing Genotypes in Private Genomic Databases from Genetic Risk Scores.
[preprint]

van de Meent, J.-W., **Paige, B.**, Yang, H., & Wood, F. (2019).
An Introduction to Probabilistic Programming.
In revision, *Foundations and Trends in Machine Learning (FTML)*.
[preprint]

Clymo, J., Manukian, H., Fijalkow, N., Gascon, A., & **Paige, B.** (2020).
Data Generation for Neural Programming by Example.
Accepted at *AISTATS*, to appear.
[preprint]

Bradshaw, J., **Paige, B.**, Kusner, M., Segler, M.H.S., & Hernández-Lobato, J.M. (2019).
A Model to Search for Synthesizable Molecules.
In *Advances in Neural Information Processing Systems (NeurIPS)*, 32:7935–7947.
[link]

Shi, Y., Siddharth, N., **Paige, B.**, & Torr, P.H.S. (2019).
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models.
In *Advances in Neural Information Processing Systems (NeurIPS)*, 32:15692–15703.
[link]

Law, S., **Paige, B.**, & Russell, C. (2019).
Take a Look Around: Using Street View and Satellite Images to Estimate House Prices.
In *ACM Transactions on Intelligent Systems and Technology (TIST)*, 10, 5, Article 54.
[link]
[arXiv]

Bradshaw, J., Kusner, M., **Paige, B.**, Segler, M.H.S., & Hernández-Lobato, J.M. (2019).
A Generative Model for Electron Paths.
In *International Conference on Learning Representations (ICLR)*.
[preprint]

Esmaeili, B., Wu, H., Jain, S., Bozkurt, A., Siddharth, N., **Paige, B.**, Brooks, D.H., Dy, J., van de Meent, J.-W. (2019).
Structured Disentangled Representations.
In *Proceedings of Machine Learning Research (AISTATS)*, PMLR 89:2525-2534.
[link]

Janz, D., van der Westhuizen, J., **Paige, B.**, Kusner, M., & Hernández-Lobato, J.M. (2018).
Learning a Generative Model for Validity in Complex Discrete Structures.
In *International Conference on Learning Representations (ICLR)*. [link]

Siddharth, N.*, **Paige, B.***, van de Meent, J.-W.*, Desmaison, A., Wood, F., Goodman, N.D., Kohli, P., & Torr, P.H.S. (2017).
Learning Disentangled Representations with Semi-Supervised Deep Generative Models.
In *Advances in Neural Information Processing Systems (NIPS)*, 30:5925–5935.
[link]

Kusner, M.J.*, **Paige, B.***, & Hernández-Lobato, J.M. (2017).
Grammar Variational Autoencoder.
In *Proceedings of the 34th International Conference on Machine Learning*,
PMLR 70: 1945–1954.
[link]
[video]

Schuster, I., Strathmann, H., **Paige, B.**, & Sejdinovic, D. (2017).
Kernel Sequential Monte Carlo.
In *European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)*, LNCS 10534: 390–409.
[preprint]

**Paige, B.** (2016).
Automatic Inference for Higher-Order Probabilistic Programs.
DPhil thesis.
[link]

**Paige, B.**, Sejdinovic, D., & Wood, F. (2016). Super-sampling with a Reservoir.
In *Proceedings of the 32nd Annual Conference on Uncertainty in Artificial Intelligence*, UAI 32: 567–576.
[link]

**Paige, B.**, & Wood, F. (2016). Inference Networks for Sequential Monte Carlo in Graphical Models.
In *Proceedings of the 33rd International Conference on Machine Learning*,
JMLR W&CP 48: 3040-3049.
[link]

Rainforth, T., Naesseth, C., Lindsten, F., **Paige, B.**, van de Meent, J.-W., Doucet, A., & Wood, F. (2016). Interacting Particle Markov Chain Monte Carlo.
In *Proceedings of the 33rd International Conference on Machine Learning*,
JMLR W&CP 48: 2616-2625.
[link]

van de Meent, J.-W., **Paige, B.**, Tolpin, D., & Wood, F. (2016).
Black-box policy search with probabilistic programs.
In *Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS)*,
JMLR: W&CP 51: 1195-1204.
[link]

Tolpin, D., van de Meent, J.-W., **Paige, B.**, & Wood, F. (2015).
Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs.
In *European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)*, LNCS 9285: 311-326.
[preprint]

**Paige, B.**, Wood, F., Doucet, A., & Teh, Y.W. (2014). Asynchronous Anytime Sequential Monte Carlo.
In *Advances in Neural Information Processing Systems (NIPS)* 27: 3410-3418. [link]
[video]

**Paige, B.**, & Wood, F. (2014). A Compilation Target for Probabilistic Programming Languages. In *Proceedings of the 31st International Conference on Machine Learning*, JMLR W&CP 32(1): 1935-1943.
[link]
[video]

Shababo, B.*, **Paige, B.***, Pakman, A. & Paninski, L. (2013). Bayesian inference and online experimental design for mapping neural microcircuits.
In *Advances in Neural Information Processing Systems (NIPS)* 26: 1304-1312. [link]

Miklós, I., **Paige, B.**, & Ligeti, P. (2006). Efficient Sampling of transpositions and inverted transpositions for Bayesian MCMC. In *Proceedings of WABI 2006*, *LNBI* 4175: 174-185.

Marasoiu, M., Islam, S., Church, L., Lucero, M., **Paige, B.**, Petricek, T. (2018).
Stories of storytelling about UK’s EU funding.
In *Proceedings of the 2nd European Data and Computational Journalism Conference*.
[proceedings]

León-Villagrá, P., Islam, S., Lucero, M., **Paige, B.**, Petricek, T. (2018).
You guessed it! Reflecting on preconceptions and exploring data without statistics.
In *Proceedings of the 2nd European Data and Computational Journalism Conference*.
[proceedings]

Siddharth, N., **Paige, B.**, Desmaison, A., van de Meent, J.-W., Wood, F., Goodman, N.D., Kohli, P., Torr, P.H.S. (2016).
Inducing Interpretable Representations with Variational Autoencoders.
Presented at *Interpretable Machine Learning in Complex Systems, NIPS 2016 Workshop*.
[arXiv]

Janz, D., **Paige, B.**, Rainforth, T., van de Meent, J.-W., Wood, F. (2016).
Probabilistic structure discovery in time series data.
Presented at *Towards an Artificial Intelligence for Data Science, NIPS 2016 Workshop*.
[arXiv]

**Paige, B.**, Wood, F. (2015).
Inference networks for graphical models.
Presented at *Advances in Approximate Bayesian Inference, NIPS 2015 Workshop*.
[link]

Tolpin, D., **Paige, B.**, van de Meent, J.-W., Wood, F. (2015).
Path finding under uncertainty through probabilistic inference.
Presented at *5th Workshop on Planning and Learning, ICAPS 2015*.
[arXiv]

van de Meent, J.-W., **Paige, B.**, & Wood, F. (2014). Tempering by Subsampling. *ArXiv e-prints*, 1401.7145. [preprint]

**Paige, B.**, Zhang, X., Forde, J., & Wood, F. (2012). Perspective Inference for Eye-to-Eye Videoconferencing: Empirical Evaluation Tools and Data.
Presented at *7th Annual Machine Learning Workshop, New York Academy of Sciences*.

* denotes joint first authorship.