Paige, Brooks

37 publications

NeurIPS 2025 Effects of Dropout on Performance in Long-Range Graph Learning Tasks Jasraj Singh, Keyue Jiang, Brooks Paige, Laura Toni

ICML 2025 Right Now, Wrong Then: Non-Stationary Direct Preference Optimization Under Preference Drift Seongho Son, William Bankes, Sayak Ray Chowdhury, Brooks Paige, Ilija Bogunovic

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

ICMLW 2024 Analyzing the Generalization and Reliability of Steering Vectors Daniel Chee Hian Tan, David Chanin, Aengus Lynch, Adrià Garriga-Alonso, Dimitrios Kanoulas, Brooks Paige, Robert Kirk

NeurIPS 2024 AsEP: Benchmarking Deep Learning Methods for Antibody-Specific Epitope Prediction Chu'nan Liu, Lilian Denzler, Yihong Chen, Andrew Martin, Brooks Paige

ICMLW 2024 AsEP: Benchmarking Deep Learning Methods for Antibody-Specific Epitope Prediction ChuNan Liu, Lilian Denzler, Yihong Chen, Brooks Paige, Andrew CR Martin

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

ICML 2024 Gaussian Processes on Cellular Complexes Mathieu Alain, So Takao, Brooks Paige, Marc Peter Deisenroth

ICMLW 2024 Improving Fragment-Based Deep Molecular Generative Models Panukorn Taleongpong, Brooks Paige

ICMLW 2024 MSA Pairing Transfomer: Protein Interaction Partner Prediction with Few-Shot Contrastive Learning Alex Hawkins-Hooker, Daniel Burkhardt Cerigo, Umberto Lupo, David Jones, Brooks Paige

ICMLW 2024 SWUS: Active Learning with Structure Weighted Uncertainty Score Andrea Karlova, Brooks Paige

ICMLW 2024 Variational Inference with Censored Gaussian Process Regressors Andrea Karlova, Rishabh Kabra, Daniel Augusto de Souza, Brooks Paige

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

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

NeurIPSW 2022 Towards Healing the Blindness of Score Matching Mingtian Zhang, Oscar Key, Peter Hayes, David Barber, Brooks Paige, Francois-Xavier Briol

AISTATS 2021 Learning Bijective Feature Maps for Linear ICA Alexander Camuto, Matthew Willetts, Chris Holmes, Brooks Paige, Stephen Roberts

ICLR 2021 Relating by Contrasting: A Data-Efficient Framework for Multimodal Generative Models Yuge Shi, Brooks Paige, Philip Torr, Siddharth N

NeurIPS 2020 Barking up the Right Tree: An Approach to Search over Molecule Synthesis DAGs John Bradshaw, Brooks Paige, Matt J Kusner, Marwin Segler, José Miguel Hernández-Lobato

AISTATS 2020 Data Generation for Neural Programming by Example Judith Clymo, Haik Manukian, Nathanael Fijalkow, Adria Gascon, Brooks Paige

NeurIPS 2020 Goal-Directed Generation of Discrete Structures with Conditional Generative Models Amina Mollaysa, Brooks Paige, Alexandros Kalousis

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

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

ICLRW 2019 Generating Molecules via Chemical Reactions John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato

AISTATS 2019 Structured Disentangled Representations Babak Esmaeili, Hao Wu, Sarthak Jain, Alican Bozkurt, N Siddharth, Brooks Paige, Dana H. Brooks, Jennifer Dy, Jan-Willem Meent

NeurIPS 2019 Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models Yuge Shi, Siddharth N, Brooks Paige, Philip Torr

ICLR 2018 Learning a Generative Model for Validity in Complex Discrete Structures Dave Janz, Jos van der Westhuizen, Brooks Paige, Matt Kusner, José Miguel Hernández-Lobato

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

ECML-PKDD 2017 Kernel Sequential Monte Carlo Ingmar Schuster, Heiko Strathmann, Brooks Paige, Dino Sejdinovic

NeurIPS 2017 Learning Disentangled Representations with Semi-Supervised Deep Generative Models Siddharth N, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Noah Goodman, Pushmeet Kohli, Frank Wood, Philip Torr

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

ICML 2016 Inference Networks for Sequential Monte Carlo in Graphical Models Brooks Paige, Frank Wood

ICML 2016 Interacting Particle Markov Chain Monte Carlo Tom Rainforth, Christian Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem Vandemeent, Arnaud Doucet, Frank Wood

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

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

ICML 2014 A Compilation Target for Probabilistic Programming Languages Brooks Paige, Frank Wood

NeurIPS 2014 Asynchronous Anytime Sequential Monte Carlo Brooks Paige, Frank Wood, Arnaud Doucet, Yee Whye Teh

NeurIPS 2013 Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits Ben Shababo, Brooks Paige, Ari Pakman, Liam Paninski