Brooks Paige

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 55

Top Topics

Generative Model

Sequential Monte Carlo

Gaussian Process

Top Venues

Publications

Seongho Son, William Bankes, Sayak Ray Chowdhury, Brooks Paige, Ilija Bogunovic
Right Now, Wrong Then: Non-Stationary Direct Preference Optimization under Preference Drift. CoRR (2024)
Daniel Tan, David Chanin, Aengus Lynch, Dimitrios Kanoulas, Brooks Paige, Adrià Garriga-Alonso, Robert Kirk
Analyzing the Generalization and Reliability of Steering Vectors. CoRR (2024)
Chunan Liu, Lilian Denzler, Yihong Chen, Andrew Martin, Brooks Paige
AsEP: Benchmarking Deep Learning Methods for Antibody-specific Epitope Prediction. CoRR (2024)
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, Jarrid Rector-Brooks, Simon Blackburn, Leo Feng, Hadi Nekoei, Sai Krishna Gottipati, Priyesh Vijayan, Prateek Gupta, Ladislav Rampásek, Sasikanth Avancha, Pierre-Luc Bacon, William L. Hamilton, Brooks Paige, Sanchit Misra, Stanislaw Kamil Jastrzebski, Bharat Kaul, Doina Precup, José Miguel Hernández-Lobato, Marwin H. S. Segler, Michael M. Bronstein, Anne Marinier, Mike Tyers, Yoshua Bengio
Generative Active Learning for the Search of Small-molecule Protein Binders. CoRR (2024)
Martin Marek, Brooks Paige, Pavel Izmailov
Can a Confident Prior Replace a Cold Posterior? CoRR (2024)
Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber
Diffusive Gibbs Sampling. CoRR (2024)
Mingtian Zhang, Alex Hawkins-Hooker, Brooks Paige, David Barber
Moment Matching Denoising Gibbs Sampling. CoRR (2023)
Stephen Law, Rikuo Hasegawa, Brooks Paige, Chris Russell, Andrew Elliott
Explaining holistic image regressors and classifiers in urban analytics with plausible counterfactuals. Int. J. Geogr. Inf. Sci. 37 (12) (2023)
Mathieu Alain, So Takao, Brooks Paige, Marc Peter Deisenroth
Gaussian Processes on Cellular Complexes. CoRR (2023)
Mingtian Zhang, Alex Hawkins-Hooker, Brooks Paige, David Barber
Moment Matching Denoising Gibbs Sampling. NeurIPS (2023)
Mingtian Zhang, Oscar Key, Peter Hayes, David Barber, Brooks Paige, François-Xavier Briol
Towards Healing the Blindness of Score Matching. CoRR (2022)
Mingtian Zhang, Tim Z. Xiao, Brooks Paige, David Barber
Improving VAE-based Representation Learning. CoRR (2022)
Hugh Dance, Brooks Paige
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes. AISTATS (2022)
Alexander Camuto, Matthew Willetts, Chris C. Holmes, Brooks Paige, Stephen J. Roberts
Learning Bijective Feature Maps for Linear ICA. AISTATS (2021)
Matthew Willetts, Brooks Paige
I Don't Need u: Identifiable Non-Linear ICA Without Side Information. CoRR (2021)
Brooks Paige, James Bell, Aurélien Bellet, Adrià Gascón, Daphne Ezer
Reconstructing Genotypes in Private Genomic Databases from Genetic Risk Scores. J. Comput. Biol. 28 (5) (2021)
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 Macke, Kyle Cranmer, Jiaxin Zhang, Haruko M. Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfeffer
Simulation Intelligence: Towards a New Generation of Scientific Methods. CoRR (2021)
Yuge Shi, Brooks Paige, Philip H. S. Torr, N. Siddharth
Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models. ICLR (2021)
Hugh Dance, Brooks Paige
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes. CoRR (2021)
Brooks Paige, James Bell, Aurélien Bellet, Adrià Gascón, Daphne Ezer
Reconstructing Genotypes in Private Genomic Databases from Genetic Risk Scores. RECOMB (2020)
John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato
Barking up the right tree: an approach to search over molecule synthesis DAGs. CoRR (2020)
Judith Clymo, Adrià Gascón, Brooks Paige, Nathanaël Fijalkow, Haik Manukian
Data Generation for Neural Programming by Example. AISTATS (2020)
Amina Mollaysa, Brooks Paige, Alexandros Kalousis
Goal-directed Generation of Discrete Structures with Conditional Generative Models. NeurIPS (2020)
George Lamb, Brooks Paige
Bayesian Graph Neural Networks for Molecular Property Prediction. CoRR (2020)
John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato
Barking up the right tree: an approach to search over molecule synthesis DAGs. NeurIPS (2020)
Alexander Camuto, Matthew Willetts, Brooks Paige, Chris C. Holmes, Stephen J. Roberts
Learning Bijective Feature Maps for Linear ICA. CoRR (2020)
Aneesh Pappu, Brooks Paige
Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning. CoRR (2020)
Yuge Shi, Brooks Paige, Philip H. S. Torr, N. Siddharth
Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models. CoRR (2020)
Amina Mollaysa, Brooks Paige, Alexandros Kalousis
Goal-directed Generation of Discrete Structures with Conditional Generative Models. CoRR (2020)
Babak Esmaeili, Hao Wu, Sarthak Jain, Alican Bozkurt, N. Siddharth, Brooks Paige, Dana H. Brooks, Jennifer G. Dy, Jan-Willem van de Meent
Structured Disentangled Representations. AISTATS (2019)
Yuge Shi, N. Siddharth, Brooks Paige, Philip H. S. Torr
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models. CoRR (2019)
John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato
Generating Molecules via Chemical Reactions. DGS@ICLR (2019)
Judith Clymo, Haik Manukian, Nathanaël Fijalkow, Adrià Gascón, Brooks Paige
Data Generation for Neural Programming by Example. CoRR (2019)
John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato
A Model to Search for Synthesizable Molecules. CoRR (2019)
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
Usability of Probabilistic Programming Languages. PPIG (2019)
Yuge Shi, Siddharth Narayanaswamy, Brooks Paige, Philip H. S. Torr
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models. NeurIPS (2019)
Stephen Law, Brooks Paige, Chris Russell
Take a Look Around: Using Street View and Satellite Images to Estimate House Prices. ACM Trans. Intell. Syst. Technol. 10 (5) (2019)
John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato
A Model to Search for Synthesizable Molecules. NeurIPS (2019)
John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato
A Generative Model For Electron Paths. ICLR (Poster) (2019)
Jan-Willem van de Meent, Brooks Paige, Hongseok Yang, Frank Wood
An Introduction to Probabilistic Programming. CoRR (2018)
Babak Esmaeili, Hao Wu, Sarthak Jain, N. Siddharth, Brooks Paige, Jan-Willem van de Meent
Hierarchical Disentangled Representations. CoRR (2018)
John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato
Predicting Electron Paths. CoRR (2018)
Stephen Law, Brooks Paige, Chris Russell
Take a Look Around: Using Street View and Satellite Images to Estimate House Prices. CoRR (2018)
David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel Hernández-Lobato
Learning a Generative Model for Validity in Complex Discrete Structures. ICLR (Poster) (2018)
Matt J. Kusner, Brooks Paige, José Miguel Hernández-Lobato
Grammar Variational Autoencoder. ICML (2017)
David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel Hernández-Lobato
Learning a Generative Model for Validity in Complex Discrete Structures. CoRR (2017)
N. Siddharth, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Frank D. Wood, Noah D. Goodman, Pushmeet Kohli, Philip H. S. Torr
Learning Disentangled Representations with Semi-Supervised Deep Generative Models. CoRR (2017)
Ingmar Schuster, Heiko Strathmann, Brooks Paige, Dino Sejdinovic
Kernel Sequential Monte Carlo. ECML/PKDD (1) (2017)
Siddharth Narayanaswamy, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Noah D. Goodman, Pushmeet Kohli, Frank D. Wood, Philip H. S. Torr
Learning Disentangled Representations with Semi-Supervised Deep Generative Models. NIPS (2017)
Jan-Willem van de Meent, Brooks Paige, David Tolpin, Frank D. Wood
Black-Box Policy Search with Probabilistic Programs. AISTATS (2016)
David Janz, Brooks Paige, Tom Rainforth, Jan-Willem van de Meent, Frank D. Wood
Probabilistic structure discovery in time series data. CoRR (2016)
Brooks Paige, Dino Sejdinovic, Frank D. Wood
Super-Sampling with a Reservoir. UAI (2016)
Tom Rainforth, Christian A. Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem van de Meent, Arnaud Doucet, Frank D. Wood
Interacting Particle Markov Chain Monte Carlo. ICML (2016)
Brooks Paige, Frank D. Wood
Inference Networks for Sequential Monte Carlo in Graphical Models. ICML (2016)
N. Siddharth, Brooks Paige, Alban Desmaison, Jan-Willem van de Meent, Frank D. Wood, Noah D. Goodman, Pushmeet Kohli, Philip H. S. Torr
Inducing Interpretable Representations with Variational Autoencoders. CoRR (2016)
David Tolpin, Jan-Willem van de Meent, Brooks Paige, Frank D. Wood
Adaptive Scheduling in MCMC and Probabilistic Programming. CoRR (2015)
David Tolpin, Brooks Paige, Frank D. Wood
Path Finding under Uncertainty through Probabilistic Inference. CoRR (2015)
David Tolpin, Jan-Willem van de Meent, Brooks Paige, Frank D. Wood
Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs. ECML/PKDD (2) (2015)
Jan-Willem van de Meent, David Tolpin, Brooks Paige, Frank D. Wood
Black-Box Policy Search with Probabilistic Programs. CoRR (2015)
Brooks Paige, Frank D. Wood, Arnaud Doucet, Yee Whye Teh
Asynchronous Anytime Sequential Monte Carlo. NIPS (2014)
Brooks Paige, Frank D. Wood
A Compilation Target for Probabilistic Programming Languages. ICML (2014)
Brooks Paige, Frank D. Wood
A Compilation Target for Probabilistic Programming Languages. CoRR (2014)
Benjamin Shababo, Brooks Paige, Ari Pakman, Liam Paninski
Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits. NIPS (2013)