Brooks Paige

According to our database, Brooks Paige authored at least 44 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Can a Confident Prior Replace a Cold Posterior?

Martin Marek, Brooks Paige, Pavel Izmailov

CoRR, 2024

Diffusive Gibbs Sampling.

Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber

CoRR, 2024

2023

Explaining holistic image regressors and classifiers in urban analytics with plausible counterfactuals.

Stephen Law, Rikuo Hasegawa, Brooks Paige, Chris Russell, Andrew Elliott

Int. J. Geogr. Inf. Sci., December, 2023

Gaussian Processes on Cellular Complexes.

Mathieu Alain, So Takao, Brooks Paige, Marc Peter Deisenroth

CoRR, 2023

Moment Matching Denoising Gibbs Sampling.

Mingtian Zhang, Alex Hawkins-Hooker, Brooks Paige, David Barber

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Towards Healing the Blindness of Score Matching.

Mingtian Zhang, Oscar Key, Peter Hayes, David Barber, Brooks Paige, François-Xavier Briol

CoRR, 2022

Improving VAE-based Representation Learning.

Mingtian Zhang, Tim Z. Xiao, Brooks Paige, David Barber

CoRR, 2022

Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes.

Hugh Dance, Brooks Paige

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

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, Erik Peterson, Peter L. McMahon, Jakob H. Macke, Kyle Cranmer, Jiaxin Zhang, Haruko M. Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfeffer

CoRR, 2021

I Don't Need u: Identifiable Non-Linear ICA Without Side Information.

Matthew Willetts, Brooks Paige

CoRR, 2021

Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models.

Yuge Shi, Brooks Paige, Philip H. S. Torr, N. Siddharth

Proceedings of the 9th International Conference on Learning Representations, 2021

Learning Bijective Feature Maps for Linear ICA.

Alexander Camuto, Matthew Willetts, Chris C. Holmes, Brooks Paige, Stephen J. Roberts

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Bayesian Graph Neural Networks for Molecular Property Prediction.

George Lamb, Brooks Paige

CoRR, 2020

Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning.

Aneesh Pappu, Brooks Paige

CoRR, 2020

Reconstructing Genotypes in Private Genomic Databases from Genetic Risk Scores.

Brooks Paige, James Bell, Aurélien Bellet, Adrià Gascón, Daphne Ezer

Proceedings of the Research in Computational Molecular Biology, 2020

Goal-directed Generation of Discrete Structures with Conditional Generative Models.

Amina Mollaysa, Brooks Paige, Alexandros Kalousis

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 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

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Data Generation for Neural Programming by Example.

Judith Clymo, Adrià Gascón, Brooks Paige, Nathanaël Fijalkow, Haik Manukian

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Take a Look Around: Using Street View and Satellite Images to Estimate House Prices.

Stephen Law, Brooks Paige, Chris Russell

ACM Trans. Intell. Syst. Technol., 2019

Usability of Probabilistic Programming Languages.

Alan F. Blackwell, Luke Church, Martin Erwig, James Geddes, Andy Gordon, Maria I. Gorinova, Atilim Gunes Baydin, Bradley Gram-Hansen, Tobias Kohn, Neil Lawrence, Vikash Mansinghka, Brooks Paige, Tomas Petricek, Diana Robinson, Advait Sarkar, Oliver Strickson

Proceedings of the 30th Annual Workshop of the Psychology of Programming Interest Group, 2019

Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models.

Yuge Shi, Siddharth Narayanaswamy, Brooks Paige, Philip H. S. Torr

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

A Model to Search for Synthesizable Molecules.

John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Generating Molecules via Chemical Reactions.

John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato

Proceedings of the Deep Generative Models for Highly Structured Data, 2019

A Generative Model For Electron Paths.

John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato

Proceedings of the 7th International Conference on Learning Representations, 2019

Structured Disentangled Representations.

Babak Esmaeili, Hao Wu, Sarthak Jain, Alican Bozkurt, N. Siddharth, Brooks Paige, Dana H. Brooks, Jennifer G. Dy, Jan-Willem van de Meent

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

An Introduction to Probabilistic Programming.

Jan-Willem van de Meent, Brooks Paige, Hongseok Yang, Frank Wood

CoRR, 2018

Predicting Electron Paths.

John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato

CoRR, 2018

Hierarchical Disentangled Representations.

Babak Esmaeili, Hao Wu, Sarthak Jain, N. Siddharth, Brooks Paige, Jan-Willem van de Meent

CoRR, 2018

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

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Kernel Sequential Monte Carlo.

Ingmar Schuster, Heiko Strathmann, Brooks Paige, Dino Sejdinovic

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 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

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Grammar Variational Autoencoder.

Matt J. Kusner, Brooks Paige, José Miguel Hernández-Lobato

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Inducing Interpretable Representations with Variational Autoencoders.

N. Siddharth, Brooks Paige, Alban Desmaison, Jan-Willem van de Meent, Frank D. Wood, Noah D. Goodman, Pushmeet Kohli, Philip H. S. Torr

CoRR, 2016

Probabilistic structure discovery in time series data.

David Janz, Brooks Paige, Tom Rainforth, Jan-Willem van de Meent, Frank D. Wood

CoRR, 2016

Super-Sampling with a Reservoir.

Brooks Paige, Dino Sejdinovic, Frank D. Wood

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 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

Proceedings of the 33nd International Conference on Machine Learning, 2016

Inference Networks for Sequential Monte Carlo in Graphical Models.

Brooks Paige, Frank D. Wood

Proceedings of the 33nd International Conference on Machine Learning, 2016

Black-Box Policy Search with Probabilistic Programs.

Jan-Willem van de Meent, Brooks Paige, David Tolpin, Frank D. Wood

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Path Finding under Uncertainty through Probabilistic Inference.

David Tolpin, Brooks Paige, Frank D. Wood

CoRR, 2015

Adaptive Scheduling in MCMC and Probabilistic Programming.

David Tolpin, Jan-Willem van de Meent, Brooks Paige, Frank D. Wood

CoRR, 2015

Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs.

David Tolpin, Jan-Willem van de Meent, Brooks Paige, Frank D. Wood

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

2014

Asynchronous Anytime Sequential Monte Carlo.

Brooks Paige, Frank D. Wood, Arnaud Doucet, Yee Whye Teh

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

A Compilation Target for Probabilistic Programming Languages.

Brooks Paige, Frank D. Wood

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits.

Benjamin Shababo, Brooks Paige, Ari Pakman, Liam Paninski

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013