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2020 – today
- 2024
[c28]Mathieu Alain, So Takao, Brooks Paige, Marc Peter Deisenroth:
Gaussian Processes on Cellular Complexes. ICML 2024- [c27]Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber:
Diffusive Gibbs Sampling. ICML 2024 - [i34]Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber:
Diffusive Gibbs Sampling. CoRR abs/2402.03008 (2024) - [i33]Martin Marek, Brooks Paige, Pavel Izmailov:
Can a Confident Prior Replace a Cold Posterior? CoRR abs/2403.01272 (2024) - [i32]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 abs/2405.01616 (2024) - [i31]Daniel Tan, David Chanin, Aengus Lynch, Dimitrios Kanoulas, Brooks Paige, Adrià Garriga-Alonso, Robert Kirk:
Analyzing the Generalization and Reliability of Steering Vectors. CoRR abs/2407.12404 (2024) - [i30]Chunan Liu, Lilian Denzler, Yihong Chen, Andrew Martin, Brooks Paige:
AsEP: Benchmarking Deep Learning Methods for Antibody-specific Epitope Prediction. CoRR abs/2407.18184 (2024) - [i29]Seongho Son, William Bankes, Sayak Ray Chowdhury, Brooks Paige, Ilija Bogunovic:
Right Now, Wrong Then: Non-Stationary Direct Preference Optimization under Preference Drift. CoRR abs/2407.18676 (2024) - 2023
- [j3]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): 2575-2596 (2023) - [c26]Mingtian Zhang, Alex Hawkins-Hooker, Brooks Paige, David Barber:
Moment Matching Denoising Gibbs Sampling. NeurIPS 2023 - [i28]Mingtian Zhang, Alex Hawkins-Hooker, Brooks Paige, David Barber:
Moment Matching Denoising Gibbs Sampling. CoRR abs/2305.11650 (2023) - [i27]Mathieu Alain, So Takao, Brooks Paige, Marc Peter Deisenroth:
Gaussian Processes on Cellular Complexes. CoRR abs/2311.01198 (2023) - 2022
- [c25]Hugh Dance, Brooks Paige:
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes. AISTATS 2022: 7976-8002 - [i26]Mingtian Zhang, Tim Z. Xiao, Brooks Paige, David Barber:
Improving VAE-based Representation Learning. CoRR abs/2205.14539 (2022) - [i25]Mingtian Zhang, Oscar Key, Peter Hayes, David Barber, Brooks Paige, François-Xavier Briol:
Towards Healing the Blindness of Score Matching. CoRR abs/2209.07396 (2022) - 2021
- [j2]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): 435-451 (2021) - [c24]Alexander Camuto, Matthew Willetts, Chris C. Holmes, Brooks Paige, Stephen J. Roberts:
Learning Bijective Feature Maps for Linear ICA. AISTATS 2021: 3655-3663 - [c23]Yuge Shi, Brooks Paige, Philip H. S. Torr, N. Siddharth:
Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models. ICLR 2021 - [i24]Matthew Willetts, Brooks Paige:
I Don't Need u: Identifiable Non-Linear ICA Without Side Information. CoRR abs/2106.05238 (2021) - [i23]Hugh Dance, Brooks Paige:
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes. CoRR abs/2111.04558 (2021) - [i22]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:
Simulation Intelligence: Towards a New Generation of Scientific Methods. CoRR abs/2112.03235 (2021) - 2020
- [c22]Judith Clymo, Adrià Gascón, Brooks Paige, Nathanaël Fijalkow, Haik Manukian:
Data Generation for Neural Programming by Example. AISTATS 2020: 3450-3459 - [c21]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 - [c20]Amina Mollaysa, Brooks Paige, Alexandros Kalousis:
Goal-directed Generation of Discrete Structures with Conditional Generative Models. NeurIPS 2020 - [c19]Brooks Paige, James Bell, Aurélien Bellet, Adrià Gascón, Daphne Ezer:
Reconstructing Genotypes in Private Genomic Databases from Genetic Risk Scores. RECOMB 2020: 266-268 - [i21]Alexander Camuto, Matthew Willetts, Brooks Paige, Chris C. Holmes, Stephen J. Roberts:
Learning Bijective Feature Maps for Linear ICA. CoRR abs/2002.07766 (2020) - [i20]Yuge Shi, Brooks Paige, Philip H. S. Torr, N. Siddharth:
Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models. CoRR abs/2007.01179 (2020) - [i19]Amina Mollaysa, Brooks Paige, Alexandros Kalousis:
Goal-directed Generation of Discrete Structures with Conditional Generative Models. CoRR abs/2010.02311 (2020) - [i18]Aneesh Pappu, Brooks Paige:
Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning. CoRR abs/2011.12203 (2020) - [i17]George Lamb, Brooks Paige:
Bayesian Graph Neural Networks for Molecular Property Prediction. CoRR abs/2012.02089 (2020) - [i16]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 abs/2012.11522 (2020)
2010 – 2019
- 2019
- [j1]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): 54:1-54:19 (2019) - [c18]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: 2525-2534 - [c17]John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato:
A Generative Model For Electron Paths. ICLR (Poster) 2019 - [c16]John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato:
Generating Molecules via Chemical Reactions. DGS@ICLR 2019 - [c15]John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato:
A Model to Search for Synthesizable Molecules. NeurIPS 2019: 7935-7947 - [c14]Yuge Shi, Siddharth Narayanaswamy, Brooks Paige, Philip H. S. Torr:
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models. NeurIPS 2019: 15692-15703 - [c13]Alan F. Blackwell, Luke Church, Martin Erwig, James Geddes, Andy Gordon, Maria I. Gorinova, Atilim Gunes Baydin, Bradley Gram-Hansen, Tobias Kohn, Neil D. Lawrence, Vikash Mansinghka, Brooks Paige, Tomas Petricek, Diana Robinson, Advait Sarkar, Oliver Strickson:
Usability of Probabilistic Programming Languages. PPIG 2019 - [i15]John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato:
A Model to Search for Synthesizable Molecules. CoRR abs/1906.05221 (2019) - [i14]Judith Clymo, Haik Manukian, Nathanaël Fijalkow, Adrià Gascón, Brooks Paige:
Data Generation for Neural Programming by Example. CoRR abs/1911.02624 (2019) - [i13]Yuge Shi, N. Siddharth, Brooks Paige, Philip H. S. Torr:
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models. CoRR abs/1911.03393 (2019) - 2018
- [c12]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 - [i12]Babak Esmaeili, Hao Wu, Sarthak Jain, N. Siddharth, Brooks Paige, Jan-Willem van de Meent:
Hierarchical Disentangled Representations. CoRR abs/1804.02086 (2018) - [i11]John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato:
Predicting Electron Paths. CoRR abs/1805.10970 (2018) - [i10]Stephen Law, Brooks Paige, Chris Russell:
Take a Look Around: Using Street View and Satellite Images to Estimate House Prices. CoRR abs/1807.07155 (2018) - [i9]Jan-Willem van de Meent, Brooks Paige, Hongseok Yang, Frank Wood:
An Introduction to Probabilistic Programming. CoRR abs/1809.10756 (2018) - 2017
- [c11]Matt J. Kusner, Brooks Paige, José Miguel Hernández-Lobato:
Grammar Variational Autoencoder. ICML 2017: 1945-1954 - [c10]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: 5925-5935 - [c9]Ingmar Schuster, Heiko Strathmann, Brooks Paige, Dino Sejdinovic:
Kernel Sequential Monte Carlo. ECML/PKDD (1) 2017: 390-409 - [i8]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 abs/1706.00400 (2017) - [i7]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 abs/1712.01664 (2017) - 2016
- [c8]Jan-Willem van de Meent, Brooks Paige, David Tolpin, Frank D. Wood:
Black-Box Policy Search with Probabilistic Programs. AISTATS 2016: 1195-1204 - [c7]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: 2616-2625 - [c6]Brooks Paige, Frank D. Wood:
Inference Networks for Sequential Monte Carlo in Graphical Models. ICML 2016: 3040-3049 - [c5]Brooks Paige, Dino Sejdinovic, Frank D. Wood:
Super-Sampling with a Reservoir. UAI 2016 - [i6]David Janz, Brooks Paige, Tom Rainforth, Jan-Willem van de Meent, Frank D. Wood:
Probabilistic structure discovery in time series data. CoRR abs/1611.06863 (2016) - [i5]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 abs/1611.07492 (2016) - 2015
- [c4]David Tolpin, Jan-Willem van de Meent, Brooks Paige, Frank D. Wood:
Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs. ECML/PKDD (2) 2015: 311-326 - [i4]David Tolpin, Jan-Willem van de Meent, Brooks Paige, Frank D. Wood:
Adaptive Scheduling in MCMC and Probabilistic Programming. CoRR abs/1501.05677 (2015) - [i3]David Tolpin, Brooks Paige, Frank D. Wood:
Path Finding under Uncertainty through Probabilistic Inference. CoRR abs/1502.07314 (2015) - [i2]Jan-Willem van de Meent, David Tolpin, Brooks Paige, Frank D. Wood:
Black-Box Policy Search with Probabilistic Programs. CoRR abs/1507.04635 (2015) - 2014
- [c3]Brooks Paige, Frank D. Wood:
A Compilation Target for Probabilistic Programming Languages. ICML 2014: 1935-1943 - [c2]Brooks Paige, Frank D. Wood, Arnaud Doucet, Yee Whye Teh:
Asynchronous Anytime Sequential Monte Carlo. NIPS 2014: 3410-3418 - [i1]Brooks Paige, Frank D. Wood:
A Compilation Target for Probabilistic Programming Languages. CoRR abs/1403.0504 (2014) - 2013
- [c1]Benjamin Shababo, Brooks Paige, Ari Pakman, Liam Paninski:
Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits. NIPS 2013: 1304-1312
Coauthor Index
aka: Frank Wood
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last updated on 2024-10-07 21:23 CEST by the dblp team
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