Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits. - Benjamin Shababo; Brooks Paige; Ari Pakman; Liam Paninski (NIPS, 2013)

A Compilation Target for Probabilistic Programming Languages. - Brooks Paige; Frank D. Wood (ICML, 2014)

Asynchronous Anytime Sequential Monte Carlo. - Brooks Paige; Frank D. Wood; Arnaud Doucet; Yee Whye Teh (NIPS, 2014)

Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs. - David Tolpin; Jan-Willem van de Meent; Brooks Paige; Frank D. Wood (ECML/PKDD (2), 2015)

Adaptive Scheduling in MCMC and Probabilistic Programming. - David Tolpin; Jan-Willem van de Meent; Brooks Paige; Frank D. Wood (CoRR, 2015)

Path Finding under Uncertainty through Probabilistic Inference. - David Tolpin; Brooks Paige; Frank D. Wood (CoRR, 2015)

Black-Box Policy Search with Probabilistic Programs. - Jan-Willem van de Meent; Brooks Paige; David Tolpin; Frank D. Wood (AISTATS, 2016)

Inference Networks for Sequential Monte Carlo in Graphical Models. - Brooks Paige; Frank D. Wood (ICML, 2016)

Interacting Particle Markov Chain Monte Carlo. - Tom Rainforth; Christian A. Naesseth; Fredrik Lindsten; Brooks Paige; Jan-Willem van de Meent; Arnaud Doucet; Frank D. Wood (ICML, 2016)

Super-Sampling with a Reservoir. - Brooks Paige; Dino Sejdinovic; Frank D. Wood (UAI, 2016)

Probabilistic structure discovery in time series data. - David Janz; Brooks Paige; Tom Rainforth; Jan-Willem van de Meent; Frank D. Wood (CoRR, 2016)

Inducing Interpretable Representations with Variational Autoencoders. - N. Siddharth 001; Brooks Paige; Alban Desmaison; Jan-Willem van de Meent; Frank D. Wood; Noah D. Goodman; Pushmeet Kohli; Philip H. S. Torr (CoRR, 2016)

Grammar Variational Autoencoder. - Matt J. Kusner; Brooks Paige; José Miguel Hernández-Lobato (ICML, 2017)

Learning Disentangled Representations with Semi-Supervised Deep Generative Models. - Siddharth Narayanaswamy; Brooks Paige; Jan-Willem van de Meent; Alban Desmaison; Noah D. Goodman; Pushmeet Kohli; Frank D. Wood; Philip H. S. Torr (NIPS, 2017)

Kernel Sequential Monte Carlo. - Ingmar Schuster; Heiko Strathmann; Brooks Paige; Dino Sejdinovic (ECML/PKDD (1), 2017)

Learning a Generative Model for Validity in Complex Discrete Structures. - David Janz; Jos van der Westhuizen; Brooks Paige; Matt J. Kusner; José Miguel Hernández-Lobato (ICLR (Poster), 2018)

Hierarchical Disentangled Representations. - Babak Esmaeili 001; Hao Wu 020; Sarthak Jain; N. Siddharth 001; Brooks Paige; Jan-Willem van de Meent (CoRR, 2018)

Predicting Electron Paths. - John Bradshaw; Matt J. Kusner; Brooks Paige; Marwin H. S. Segler; José Miguel Hernández-Lobato (CoRR, 2018)

An Introduction to Probabilistic Programming. - Jan-Willem van de Meent; Brooks Paige; Hongseok Yang; Frank Wood (CoRR, 2018)

Structured Disentangled Representations. - Babak Esmaeili 001; Hao Wu 020; Sarthak Jain; Alican Bozkurt; N. Siddharth 001; Brooks Paige; Dana H. Brooks; Jennifer G. Dy; Jan-Willem van de Meent (AISTATS, 2019)

A Generative Model For Electron Paths. - John Bradshaw; Matt J. Kusner; Brooks Paige; Marwin H. S. Segler; José Miguel Hernández-Lobato (ICLR (Poster), 2019)

Generating Molecules via Chemical Reactions. - John Bradshaw; Matt J. Kusner; Brooks Paige; Marwin H. S. Segler; José Miguel Hernández-Lobato (DGS@ICLR, 2019)

A Model to Search for Synthesizable Molecules. - John Bradshaw; Brooks Paige; Matt J. Kusner; Marwin H. S. Segler; José Miguel Hernández-Lobato (NeurIPS, 2019)

Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models. - Yuge Shi; Siddharth Narayanaswamy; Brooks Paige; Philip H. S. Torr (NeurIPS, 2019)

Usability of Probabilistic Programming Languages. - Alan F. Blackwell; Luke Church; Martin Erwig; James Geddes; Andy Gordon 001; Maria I. Gorinova 001; Atilim Gunes Baydin; Bradley Gram-Hansen; Tobias Kohn; Neil D. Lawrence; Vikash Mansinghka 001; Brooks Paige; et al. (PPIG, 2019)

Take a Look Around: Using Street View and Satellite Images to Estimate House Prices. - Stephen Law; Brooks Paige; Chris Russell 001 (ACM Trans. Intell. Syst. Technol., 2019)

Data Generation for Neural Programming by Example. - Judith Clymo; Adrià Gascón; Brooks Paige; Nathanaël Fijalkow; Haik Manukian (AISTATS, 2020)

Barking up the right tree: an approach to search over molecule synthesis DAGs. - John Bradshaw; Brooks Paige; Matt J. Kusner; Marwin H. S. Segler; José Miguel Hernández-Lobato (NeurIPS, 2020)

Goal-directed Generation of Discrete Structures with Conditional Generative Models. - Amina Mollaysa; Brooks Paige; Alexandros Kalousis (NeurIPS, 2020)

Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning. - Aneesh Pappu; Brooks Paige (CoRR, 2020)

Bayesian Graph Neural Networks for Molecular Property Prediction. - George Lamb; Brooks Paige (CoRR, 2020)

Learning Bijective Feature Maps for Linear ICA. - Alexander Camuto; Matthew Willetts; Chris C. Holmes; Brooks Paige; Stephen J. Roberts (AISTATS, 2021)

Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models. - Yuge Shi; Brooks Paige; Philip H. S. Torr; N. Siddharth 001 (ICLR, 2021)

I Don't Need u: Identifiable Non-Linear ICA Without Side Information. - Matthew Willetts; Brooks Paige (CoRR, 2021)

Simulation Intelligence: Towards a New Generation of Scientific Methods. - Alexander Lavin; Hector Zenil; Brooks Paige; David Krakauer; Justin Gottschlich; Tim Mattson; Anima Anandkumar; Sanjay Choudry; Kamil Rocki; Atilim Günes Baydin; Carina Prunkl; Olexandr Isayev; et al. (CoRR, 2021)

Reconstructing Genotypes in Private Genomic Databases from Genetic Risk Scores. - Brooks Paige; James Bell 001; Aurélien Bellet; Adrià Gascón; Daphne Ezer (J. Comput. Biol., 2021)

Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes. - Hugh Dance; Brooks Paige (AISTATS, 2022)

Improving VAE-based Representation Learning. - Mingtian Zhang; Tim Z. Xiao; Brooks Paige; David Barber (CoRR, 2022)

Towards Healing the Blindness of Score Matching. - Mingtian Zhang; Oscar Key; Peter Hayes; David Barber; Brooks Paige; François-Xavier Briol (CoRR, 2022)

Moment Matching Denoising Gibbs Sampling. - Mingtian Zhang; Alex Hawkins-Hooker; Brooks Paige; David Barber (NeurIPS, 2023)

Explaining holistic image regressors and classifiers in urban analytics with plausible counterfactuals. - Stephen Law; Rikuo Hasegawa; Brooks Paige; Chris Russell 001; Andrew Elliott (Int. J. Geogr. Inf. Sci., 2023)

Balancing Calibration and Performance: Stochastic Depth in Segmentation BNNs. - Linghong Yao; Denis Hadjivelichkov; Andromachi Maria Delfaki; Yuanchang Liu; Brooks Paige; Dimitrios Kanoulas (BMVC, 2024)

Gaussian Processes on Cellular Complexes. - Mathieu Alain; So Takao; Brooks Paige; Marc Peter Deisenroth (ICML, 2024)

Diffusive Gibbs Sampling. - Wenlin Chen; Mingtian Zhang; Brooks Paige; José Miguel Hernández-Lobato; David Barber (ICML, 2024)

AsEP: Benchmarking Deep Learning Methods for Antibody-specific Epitope Prediction. - Chunan Liu; Lilian Denzler; Yihong Chen; Andrew C. R. Martin; Brooks Paige (NeurIPS, 2024)

Analysing the Generalisation and Reliability of Steering Vectors. - Daniel Tan; David Chanin; Aengus Lynch; Brooks Paige; Dimitrios Kanoulas; Adrià Garriga-Alonso; Robert Kirk (NeurIPS, 2024)

Can a Confident Prior Replace a Cold Posterior? - Martin Marek; Brooks Paige; Pavel Izmailov (CoRR, 2024)

Generative Active Learning for the Search of Small-molecule Protein Binders. - Maksym Korablyov; Cheng-Hao Liu; Moksh Jain; Almer M. van der Sloot; Eric Jolicoeur; Edward Ruediger; Andrei Cristian Nica; Emmanuel Bengio; Kostiantyn Lapchevskyi; Daniel St-Cyr; Doris Alexandra Schuetz; Victor Ion Butoi; et al. (CoRR, 2024)

Analyzing the Generalization and Reliability of Steering Vectors. - Daniel Tan; David Chanin; Aengus Lynch; Dimitrios Kanoulas; Brooks Paige; Adrià Garriga-Alonso; Robert Kirk (CoRR, 2024)

Right Now, Wrong Then: Non-Stationary Direct Preference Optimization under Preference Drift. - Seongho Son; William Bankes; Sayak Ray Chowdhury; Brooks Paige; Ilija Bogunovic (CoRR, 2024)

CACHE Challenge #2: Targeting the RNA Site of the SARS-CoV-2 Helicase Nsp13. - Oleksandra Herasymenko; Madhushika Silva; Abd Al-Aziz A. Abu-Saleh; Ayaz Ahmad; Jesus Alvarado-Huayhuaz; Oscar Emilio Arrúa Arce; Roly J. Armstrong; Cheryl H. Arrowsmith; Kelly E. Bachta; Hartmut Beck; Dénes Berta; Mateusz K. Bieniek; et al. (J. Chem. Inf. Model., 2025)

Effects of Random Edge-Dropping on Over-Squashing in Graph Neural Networks. - Jasraj Singh; Keyue Jiang; Brooks Paige; Laura Toni (CoRR, 2025)

Time-varying Factor Augmented Vector Autoregression with Grouped Sparse Autoencoder. - Yiyong Luo; Brooks Paige; Jim Griffin (CoRR, 2025)

AbRank: A Benchmark Dataset and Metric-Learning Framework for Antibody-Antigen Affinity Ranking. - Chunan Liu; Aurélien Pélissier; Yanjun Shao; Lilian Denzler; Andrew C. R. Martin; Brooks Paige; Mariia Rodriguez Martinez (CoRR, 2025)

Bayesian Optimization for Molecules Should Be Pareto-Aware. - Anabel Yong; Austin Tripp; Layla Hosseini-Gerami; Brooks Paige (CoRR, 2025)

SmilesT5: Domain-specific pretraining for molecular language models. - Philip Spence; Brooks Paige; Anne Osbourn (CoRR, 2025)