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ORCIDFrank D. Wood
Frank Wood
Person information
- affiliation: University of British Columbia, Canada
- affiliation (former): University of Oxford, Department of Engineering Science, UK
- affiliation (former): Columbia University, Department of Statistics, New York, NY, USA
- affiliation (former): Brown University, Department of Computer Science, Providence, RI, USA
- affiliation (former): Cornell University, Ithaca, NY, USA
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2020 – today
- 2024
[c80]Manuel Glöckler, Michael Deistler, Christian Dietrich Weilbach, Frank Wood, Jakob H. Macke:
All-in-one simulation-based inference. ICML 2024- [c79]Saeid Naderiparizi, Xiaoxuan Liang, Setareh Cohan, Berend Zwartsenberg, Frank Wood:
Don't be so Negative! Score-based Generative Modeling with Oracle-assisted Guidance. ICML 2024 - [c78]Matthew Niedoba, Dylan Green, Saeid Naderiparizi, Vasileios Lioutas, Jonathan Wilder Lavington, Xiaoxuan Liang, Yunpeng Liu, Ke Zhang, Setareh Dabiri, Adam Scibior, Berend Zwartsenberg, Frank Wood:
Nearest Neighbour Score Estimators for Diffusion Generative Models. ICML 2024 - [c77]Jinsoo Yoo, Yunpeng Liu, Frank Wood, Geoff Pleiss:
Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning. ICML 2024 - [i81]Matthew Niedoba, Dylan Green, Saeid Naderiparizi, Vasileios Lioutas, Jonathan Wilder Lavington, Xiaoxuan Liang, Yunpeng Liu, Ke Zhang, Setareh Dabiri, Adam Scibior, Berend Zwartsenberg, Frank Wood:
Nearest Neighbour Score Estimators for Diffusion Generative Models. CoRR abs/2402.08018 (2024) - [i80]Jason Yoo, Yunpeng Liu, Frank Wood, Geoff Pleiss:
Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning. CoRR abs/2402.09542 (2024) - [i79]Laura Manduchi, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric T. Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E. Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin:
On the Challenges and Opportunities in Generative AI. CoRR abs/2403.00025 (2024) - [i78]Manuel Glöckler, Michael Deistler, Christian Weilbach, Frank Wood, Jakob H. Macke:
All-in-one simulation-based inference. CoRR abs/2404.09636 (2024) - [i77]Dylan Green, William Harvey, Saeid Naderiparizi, Matthew Niedoba, Yunpeng Liu, Xiaoxuan Liang, Jonathan Wilder Lavington, Ke Zhang, Vasileios Lioutas, Setareh Dabiri, Adam Scibior, Berend Zwartsenberg, Frank Wood:
Semantically Consistent Video Inpainting with Conditional Diffusion Models. CoRR abs/2405.00251 (2024) - [i76]Jonathan Wilder Lavington, Ke Zhang, Vasileios Lioutas, Matthew Niedoba, Yunpeng Liu, Dylan Green, Saeid Naderiparizi, Xiaoxuan Liang, Setareh Dabiri, Adam Scibior, Berend Zwartsenberg, Frank Wood:
TorchDriveEnv: A Reinforcement Learning Benchmark for Autonomous Driving with Reactive, Realistic, and Diverse Non-Playable Characters. CoRR abs/2405.04491 (2024) - [i75]Jason Yoo, Dylan Green, Geoff Pleiss, Frank Wood:
Online Continual Learning of Video Diffusion Models From a Single Video Stream. CoRR abs/2406.04814 (2024) - [i74]Ryan Fayyazi, Christian Weilbach, Frank Wood:
Prospective Messaging: Learning in Networks with Communication Delays. CoRR abs/2407.05494 (2024) - 2023
- [j13]Berend Zwartsenberg, Adam Scibior, Matthew Niedoba, Vasileios Lioutas, Justice Sefas, Yunpeng Liu, Setareh Dabiri, Jonathan Wilder Lavington, Trevor Campbell, Frank Wood:
Conditional Permutation Invariant Flows. Trans. Mach. Learn. Res. 2023 (2023) - [c76]Vasileios Lioutas, Jonathan Wilder Lavington, Justice Sefas, Matthew Niedoba, Yunpeng Liu, Berend Zwartsenberg, Setareh Dabiri, Frank Wood, Adam Scibior:
Critic Sequential Monte Carlo. ICLR 2023 - [c75]Andreas Munk, Alexander Mead, Frank Wood:
Uncertain Evidence in Probabilistic Models and Stochastic Simulators. ICML 2023: 25486-25500 - [c74]Christian Dietrich Weilbach, William Harvey, Frank Wood:
Graphically Structured Diffusion Models. ICML 2023: 36887-36909 - [c73]Yunpeng Liu, Vasileios Lioutas, Jonathan Wilder Lavington, Matthew Niedoba, Justice Sefas, Setareh Dabiri, Dylan Green, Xiaoxuan Liang, Berend Zwartsenberg, Adam Scibior, Frank Wood:
Video Killed the HD-Map: Predicting Multi-Agent Behavior Directly From Aerial Images. ITSC 2023: 3261-3267 - [c72]Matthew Niedoba, Jonathan Wilder Lavington, Yunpeng Liu, Vasileios Lioutas, Justice Sefas, Xiaoxuan Liang, Dylan Green, Setareh Dabiri, Berend Zwartsenberg, Adam Scibior, Frank Wood:
A Diffusion-Model of Joint Interactive Navigation. NeurIPS 2023 - [i73]William Harvey, Frank Wood:
Visual Chain-of-Thought Diffusion Models. CoRR abs/2303.16187 (2023) - [i72]Yunpeng Liu, Vasileios Lioutas, Jonathan Wilder Lavington, Matthew Niedoba, Justice Sefas, Setareh Dabiri, Dylan Green, Xiaoxuan Liang, Berend Zwartsenberg, Adam Scibior, Frank Wood:
Video Killed the HD-Map: Predicting Driving Behavior Directly From Drone Images. CoRR abs/2305.11856 (2023) - [i71]Setareh Dabiri, Vasileios Lioutas, Berend Zwartsenberg, Yunpeng Liu, Matthew Niedoba, Xiaoxuan Liang, Dylan Green, Justice Sefas, Jonathan Wilder Lavington, Frank Wood, Adam Scibior:
Realistically distributing object placements in synthetic training data improves the performance of vision-based object detection models. CoRR abs/2305.14621 (2023) - [i70]Saeid Naderiparizi, Xiaoxuan Liang, Berend Zwartsenberg, Frank Wood:
Don't be so negative! Score-based Generative Modeling with Oracle-assisted Guidance. CoRR abs/2307.16463 (2023) - [i69]Matthew Niedoba, Jonathan Wilder Lavington, Yunpeng Liu, Vasileios Lioutas, Justice Sefas, Xiaoxuan Liang, Dylan Green, Setareh Dabiri, Berend Zwartsenberg, Adam Scibior, Frank Wood:
A Diffusion-Model of Joint Interactive Navigation. CoRR abs/2309.12508 (2023) - 2022
- [j12]Vasileios Lioutas, Adam Scibior, Frank Wood:
TITRATED: Learned Human Driving Behavior without Infractions via Amortized Inference. Trans. Mach. Learn. Res. 2022 (2022) - [c71]Saeid Naderiparizi, Adam Scibior, Andreas Munk, Mehrdad Ghadiri, Atilim Gunes Baydin, Bradley J. Gram-Hansen, Christian A. Schröder de Witt, Robert Zinkov, Philip H. S. Torr, Tom Rainforth, Yee Whye Teh, Frank Wood:
Amortized Rejection Sampling in Universal Probabilistic Programming. AISTATS 2022: 8392-8412 - [c70]William Harvey, Saeid Naderiparizi, Frank Wood:
Conditional Image Generation by Conditioning Variational Auto-Encoders. ICLR 2022 - [c69]William Harvey, Michael Teng, Frank Wood:
Near-Optimal Glimpse Sequences for Improved Hard Attention Neural Network Training. IJCNN 2022: 1-8 - [c68]Yunpeng Liu, Jonathan Wilder Lavington, Adam Scibior, Frank Wood:
Vehicle Type Specific Waypoint Generation. IROS 2022: 12225-12230 - [c67]William Harvey, Saeid Naderiparizi, Vaden Masrani, Christian Weilbach, Frank Wood:
Flexible Diffusion Modeling of Long Videos. NeurIPS 2022 - [c66]Jinsoo Yoo, Frank Wood:
BayesPCN: A Continually Learnable Predictive Coding Associative Memory. NeurIPS 2022 - [c65]William Harvey, Andreas Munk, Atilim Günes Baydin, Alexander Bergholm, Frank Wood:
Attention for Inference Compilation. SIMULTECH 2022: 80-91 - [c64]Andreas Munk, Berend Zwartsenberg, Adam Scibior, Atilim Günes Baydin, Andrew Stewart, Goran Fernlund, Anoush Poursartip, Frank Wood:
Probabilistic surrogate networks for simulators with unbounded randomness. UAI 2022: 1423-1433 - [c63]Peyman Bateni, Jarred Barber, Jan-Willem van de Meent, Frank Wood:
Enhancing Few-Shot Image Classification with Unlabelled Examples. WACV 2022: 1597-1606 - [p1]David Poole, Frank Wood:
Probabilistic Programming Languages: Independent Choices and Deterministic Systems. Probabilistic and Causal Inference 2022: 691-712 - [i68]Peyman Bateni, Jarred Barber, Raghav Goyal, Vaden Masrani, Jan-Willem van de Meent, Leonid Sigal, Frank Wood:
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning. CoRR abs/2201.05151 (2022) - [i67]Michael Teng, Michiel van de Panne, Frank Wood:
Exploration with Multi-Sample Target Values for Distributional Reinforcement Learning. CoRR abs/2202.02693 (2022) - [i66]Atilim Günes Baydin, Barak A. Pearlmutter, Don Syme, Frank Wood, Philip H. S. Torr:
Gradients without Backpropagation. CoRR abs/2202.08587 (2022) - [i65]Jason Yoo, Frank Wood:
BayesPCN: A Continually Learnable Predictive Coding Associative Memory. CoRR abs/2205.09930 (2022) - [i64]William Harvey, Saeid Naderiparizi, Vaden Masrani, Christian Weilbach, Frank Wood:
Flexible Diffusion Modeling of Long Videos. CoRR abs/2205.11495 (2022) - [i63]Vasileios Lioutas, Jonathan Wilder Lavington, Justice Sefas, Matthew Niedoba, Yunpeng Liu, Berend Zwartsenberg, Setareh Dabiri, Frank Wood, Adam Scibior:
Critic Sequential Monte Carlo. CoRR abs/2205.15460 (2022) - [i62]Berend Zwartsenberg, Adam Scibior, Matthew Niedoba, Vasileios Lioutas, Yunpeng Liu, Justice Sefas, Setareh Dabiri, Jonathan Wilder Lavington, Trevor Campbell, Frank Wood:
Conditional Permutation Invariant Flows. CoRR abs/2206.09021 (2022) - [i61]Yunpeng Liu, Jonathan Wilder Lavington, Adam Scibior, Frank Wood:
Vehicle Type Specific Waypoint Generation. CoRR abs/2208.04987 (2022) - [i60]Christian Weilbach, William Harvey, Frank Wood:
Graphically Structured Diffusion Models. CoRR abs/2210.11633 (2022) - [i59]Andreas Munk, Alexander Mead, Frank Wood:
Uncertain Evidence in Probabilistic Models and Stochastic Simulators. CoRR abs/2210.12236 (2022) - 2021
- [j11]Frank Wood, Andrew Warrington, Saeid Naderiparizi, Christian Weilbach, Vaden Masrani, William Harvey, Adam Scibior, Boyan Beronov
, John Grefenstette, Duncan Campbell, Seyed Ali Nasseri:
Planning as Inference in Epidemiological Dynamics Models. Frontiers Artif. Intell. 4 (2021) - [c62]Andrew Warrington, Jonathan Wilder Lavington, Adam Scibior, Mark Schmidt, Frank Wood:
Robust Asymmetric Learning in POMDPs. ICML 2021: 11013-11023 - [c61]Andreas Munk, William Harvey, Frank Wood:
Assisting the Adversary to Improve GAN Training. IJCNN 2021: 1-8 - [c60]Adam Scibior, Vasileios Lioutas, Daniele Reda, Peyman Bateni, Frank Wood:
Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation. ITSC 2021: 720-725 - [c59]Vaden Masrani, Rob Brekelmans, Thang Bui, Frank Nielsen, Aram Galstyan, Greg Ver Steeg, Frank Wood:
q-Paths: Generalizing the geometric annealing path using power means. UAI 2021: 1938-1947 - [c58]Boyan Beronov, Christian Weilbach, Frank Wood, Trevor Campbell:
Sequential core-set Monte Carlo. UAI 2021: 2165-2175 - [i58]William Harvey, Saeid Naderiparizi, Frank Wood:
Image Completion via Inference in Deep Generative Models. CoRR abs/2102.12037 (2021) - [i57]Adam Scibior, Vasileios Lioutas, Daniele Reda, Peyman Bateni, Frank Wood:
Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation. CoRR abs/2104.11212 (2021) - [i56]Adam Scibior, Vaden Masrani, Frank Wood:
Differentiable Particle Filtering without Modifying the Forward Pass. CoRR abs/2106.10314 (2021) - [i55]Vaden Masrani, Rob Brekelmans, Thang Bui, Frank Nielsen, Aram Galstyan, Greg Ver Steeg, Frank Wood:
q-Paths: Generalizing the Geometric Annealing Path using Power Means. CoRR abs/2107.00745 (2021) - 2020
- [j10]Tom Rainforth, Adam Golinski, Frank Wood, Sheheryar Zaidi:
Target-Aware Bayesian Inference: How to Beat Optimal Conventional Estimators. J. Mach. Learn. Res. 21: 88:1-88:54 (2020) - [c57]Andrew Warrington, Frank Wood, Saeid Naderiparizi:
Coping With Simulators That Don't Always Return. AISTATS 2020: 1748-1758 - [c56]Christian Weilbach, Boyan Beronov, Frank Wood, William Harvey:
Structured Conditional Continuous Normalizing Flows for Efficient Amortized Inference in Graphical Models. AISTATS 2020: 4441-4451 - [c55]Peyman Bateni, Raghav Goyal, Vaden Masrani, Frank Wood, Leonid Sigal:
Improved Few-Shot Visual Classification. CVPR 2020: 14481-14490 - [c54]Rob Brekelmans, Vaden Masrani, Frank Wood, Greg Ver Steeg, Aram Galstyan:
All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference. ICML 2020: 1111-1122 - [c53]Vu Nguyen, Vaden Masrani, Rob Brekelmans, Michael A. Osborne, Frank Wood:
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective. NeurIPS 2020 - [c52]Michael Teng, Tuan Anh Le, Adam Scibior, Frank Wood:
Semi-supervised Sequential Generative Models. UAI 2020: 649-658 - [i54]Andrew Warrington, Saeid Naderiparizi, Frank Wood:
Coping With Simulators That Don't Always Return. CoRR abs/2003.12908 (2020) - [i53]Frank Wood, Andrew Warrington, Saeid Naderiparizi, Christian Weilbach, Vaden Masrani, William Harvey, Adam Scibior, Boyan Beronov, Seyed Ali Nasseri:
Planning as Inference in Epidemiological Models. CoRR abs/2003.13221 (2020) - [i52]Peyman Bateni, Jarred Barber, Jan-Willem van de Meent, Frank Wood:
Improving Few-Shot Visual Classification with Unlabelled Examples. CoRR abs/2006.12245 (2020) - [i51]Michael Teng, Tuan Anh Le, Adam Scibior, Frank Wood:
Semi-supervised Sequential Generative Models. CoRR abs/2007.00155 (2020) - [i50]Rob Brekelmans, Vaden Masrani, Frank Wood, Greg Ver Steeg, Aram Galstyan:
All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference. CoRR abs/2007.00642 (2020) - [i49]Andreas Munk, William Harvey, Frank Wood:
Assisting the Adversary to Improve GAN Training. CoRR abs/2010.01274 (2020) - [i48]Saeid Naderiparizi, Kenny Chiu, Benjamin Bloem-Reddy, Frank Wood:
Uncertainty in Neural Processes. CoRR abs/2010.03753 (2020) - [i47]Vu Nguyen, Vaden Masrani, Rob Brekelmans, Michael A. Osborne, Frank Wood:
Gaussian Process Bandit Optimization of theThermodynamic Variational Objective. CoRR abs/2010.15750 (2020) - [i46]Jason Yoo, Tony Joseph, Dylan Yung, Seyed Ali Nasseri, Frank Wood:
Ensemble Squared: A Meta AutoML System. CoRR abs/2012.05390 (2020) - [i45]Rob Brekelmans, Vaden Masrani, Thang Bui, Frank Wood, Aram Galstyan, Greg Ver Steeg, Frank Nielsen:
Annealed Importance Sampling with q-Paths. CoRR abs/2012.07823 (2020) - [i44]Andrew Warrington, J. Wilder Lavington, Adam Scibior, Mark Schmidt, Frank Wood:
Robust Asymmetric Learning in POMDPs. CoRR abs/2012.15566 (2020)
2010 – 2019
- 2019
- [c51]Yuan Zhou, Bradley J. Gram-Hansen, Tobias Kohn, Tom Rainforth, Hongseok Yang, Frank Wood:
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models. AISTATS 2019: 148-157 - [c50]Adam Golinski, Frank Wood, Tom Rainforth:
Amortized Monte Carlo Integration. ICML 2019: 2309-2318 - [c49]Atilim Gunes Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Saeid Naderiparizi, Andreas Munk, Jialin Liu, Bradley Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip H. S. Torr, Victor W. Lee, Kyle Cranmer, Prabhat, Frank Wood:
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. NeurIPS 2019: 5460-5473 - [c48]Vaden Masrani, Tuan Anh Le, Frank Wood:
The Thermodynamic Variational Objective. NeurIPS 2019: 11521-11530 - [c47]Atilim Günes Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Lawrence Meadows, Jialin Liu, Andreas Munk, Saeid Naderiparizi, Bradley Gram-Hansen, Gilles Louppe, Mingfei Ma, Xiaohui Zhao, Philip H. S. Torr, Victor W. Lee, Kyle Cranmer, Prabhat, Frank Wood:
Etalumis: bringing probabilistic programming to scientific simulators at scale. SC 2019: 29:1-29:24 - [c46]Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood:
Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow. UAI 2019: 1039-1049 - [i43]Yuan Zhou, Bradley J. Gram-Hansen, Tobias Kohn, Tom Rainforth, Hongseok Yang, Frank Wood:
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models. CoRR abs/1903.02482 (2019) - [i42]William Harvey, Michael Teng, Frank Wood:
Near-Optimal Glimpse Sequences for Improved Hard Attention Neural Network Training. CoRR abs/1906.05462 (2019) - [i41]Vaden Masrani, Tuan Anh Le, Frank Wood:
The Thermodynamic Variational Objective. CoRR abs/1907.00031 (2019) - [i40]Atilim Günes Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Lawrence Meadows, Jialin Liu, Andreas Munk, Saeid Naderiparizi, Bradley Gram-Hansen, Gilles Louppe, Mingfei Ma, Xiaohui Zhao, Philip H. S. Torr, Victor W. Lee, Kyle Cranmer, Prabhat, Frank Wood:
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale. CoRR abs/1907.03382 (2019) - [i39]Adam Golinski, Frank Wood, Tom Rainforth:
Amortized Monte Carlo Integration. CoRR abs/1907.08082 (2019) - [i38]Andrew Warrington, Arthur P. C. Spencer, Frank Wood:
The Virtual Patch Clamp: Imputing C. elegans Membrane Potentials from Calcium Imaging. CoRR abs/1907.11075 (2019) - [i37]Saeid Naderiparizi, Adam Scibior, Andreas Munk, Mehrdad Ghadiri, Atilim Günes Baydin, Bradley Gram-Hansen, Christian Schröder de Witt, Robert Zinkov, Philip H. S. Torr, Tom Rainforth, Yee Whye Teh, Frank Wood:
Amortized Rejection Sampling in Universal Probabilistic Programming. CoRR abs/1910.09056 (2019) - [i36]Andreas Munk, Adam Scibior, Atilim Günes Baydin, Andrew Stewart, Goran Fernlund, Anoush Poursartip, Frank Wood:
Deep Probabilistic Surrogate Networks for Universal Simulator Approximation. CoRR abs/1910.11950 (2019) - [i35]William Harvey, Andreas Munk, Atilim Günes Baydin, Alexander Bergholm, Frank Wood:
Attention for Inference Compilation. CoRR abs/1910.11961 (2019) - [i34]Peyman Bateni, Raghav Goyal, Vaden Masrani, Frank Wood, Leonid Sigal:
Improved Few-Shot Visual Classification. CoRR abs/1912.03432 (2019) - 2018
- [c45]Atilim Gunes Baydin, Robert Cornish, David Martínez-Rubio, Mark Schmidt, Frank Wood:
Online Learning Rate Adaptation with Hypergradient Descent. ICLR (Poster) 2018 - [c44]Tuan Anh Le, Maximilian Igl, Tom Rainforth, Tom Jin, Frank Wood:
Auto-Encoding Sequential Monte Carlo. ICLR (Poster) 2018 - [c43]Maximilian Igl, Luisa M. Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson:
Deep Variational Reinforcement Learning for POMDPs. ICML 2018: 2122-2131 - [c42]Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh:
Tighter Variational Bounds are Not Necessarily Better. ICML 2018: 4274-4282 - [c41]Stefan Webb, Adam Golinski, Robert Zinkov, Siddharth Narayanaswamy, Tom Rainforth, Yee Whye Teh, Frank Wood:
Faithful Inversion of Generative Models for Effective Amortized Inference. NeurIPS 2018: 3074-3084 - [c40]Michael Teng, Frank Wood:
Bayesian Distributed Stochastic Gradient Descent. NeurIPS 2018: 6380-6390 - [i33]Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh:
Tighter Variational Bounds are Not Necessarily Better. CoRR abs/1802.04537 (2018) - [i32]Michael Teng, Frank Wood:
High Throughput Synchronous Distributed Stochastic Gradient Descent. CoRR abs/1803.04209 (2018) - [i31]Bradley Gram-Hansen, Yuan Zhou, Tobias Kohn, Hongseok Yang, Frank D. Wood:
Discontinuous Hamiltonian Monte Carlo for Probabilistic Programs. CoRR abs/1804.03523 (2018) - [i30]Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood:
Revisiting Reweighted Wake-Sleep. CoRR abs/1805.10469 (2018) - [i29]Maximilian Igl, Luisa M. Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson:
Deep Variational Reinforcement Learning for POMDPs. CoRR abs/1806.02426 (2018) - [i28]Atilim Gunes Baydin, Lukas Heinrich, Wahid Bhimji, Bradley Gram-Hansen, Gilles Louppe, Lei Shao, Prabhat, Kyle Cranmer, Frank D. Wood:
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. CoRR abs/1807.07706 (2018) - [i27]Jan-Willem van de Meent, Brooks Paige, Hongseok Yang, Frank Wood:
An Introduction to Probabilistic Programming. CoRR abs/1809.10756 (2018) - 2017
- [j9]François Caron, Willie Neiswanger, Frank D. Wood, Arnaud Doucet, Manuel Davy:
Generalized Pólya Urn for Time-Varying Pitman-Yor Processes. J. Mach. Learn. Res. 18: 27:1-27:32 (2017) - [c39]Tuan Anh Le, Atilim Gunes Baydin, Frank D. Wood:
Inference Compilation and Universal Probabilistic Programming. AISTATS 2017: 1338-1348 - [c38]Tuan Anh Le, Atilim Günes Baydin, Robert Zinkov, Frank D. Wood:
Using synthetic data to train neural networks is model-based reasoning. IJCNN 2017: 3514-3521 - [c37]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 - [c36]Neil Dhir, Matthijs Vákár, Matthew Wijers, Andrew Markham, Frank D. Wood:
Interpreting Lion Behaviour as Probabilistic Programs. UAI 2017 - [i26]Tuan Anh Le, Atilim Gunes Baydin, Robert Zinkov, Frank D. Wood:
Using Synthetic Data to Train Neural Networks is Model-Based Reasoning. CoRR abs/1703.00868 (2017) - [i25]Atilim Gunes Baydin, Robert Cornish, David Martínez-Rubio, Mark Schmidt, Frank D. Wood:
Online Learning Rate Adaptation with Hypergradient Descent. CoRR abs/1703.04782 (2017) - [i24]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) - [i23]Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank D. Wood:
Bayesian Optimization for Probabilistic Programs. CoRR abs/1707.04314 (2017) - [i22]Andrew Warrington, Frank D. Wood:
Updating the VESICLE-CNN Synapse Detector. CoRR abs/1710.11397 (2017) - [i21]Stefan Webb, Adam Golinski, Robert Zinkov, N. Siddharth, Yee Whye Teh, Frank D. Wood:
Faithful Model Inversion Substantially Improves Auto-encoding Variational Inference. CoRR abs/1712.00287 (2017) - [i20]Mario Lezcano Casado, Atilim Gunes Baydin, David Martínez-Rubio, Tuan Anh Le, Frank D. Wood, Lukas Heinrich, Gilles Louppe, Kyle Cranmer, Karen Ng, Wahid Bhimji, Prabhat:
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators. CoRR abs/1712.07901 (2017) - 2016
- [c35]Yura N. Perov, Frank D. Wood:
Automatic Sampler Discovery via Probabilistic Programming and Approximate Bayesian Computation. AGI 2016: 262-273 - [c34]Jan-Willem van de Meent, Brooks Paige, David Tolpin, Frank D. Wood:
Black-Box Policy Search with Probabilistic Programs. AISTATS 2016: 1195-1204 - [c33]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 - [c32]Brooks Paige, Frank D. Wood:
Inference Networks for Sequential Monte Carlo in Graphical Models. ICML 2016: 3040-3049 - [c31]David Tolpin, Jan-Willem van de Meent, Hongseok Yang, Frank D. Wood:
Design and Implementation of Probabilistic Programming Language Anglican. IFL 2016: 6:1-6:12 - [c30]Neil Dhir, Yura N. Perov, Frank D. Wood:
Nonparametric Bayesian models for unsupervised activity recognition and tracking. IROS 2016: 4040-4045 - [c29]Sam Staton, Hongseok Yang, Frank D. Wood, Chris Heunen, Ohad Kammar:
Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints. LICS 2016: 525-534 - [c28]Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank D. Wood:
Bayesian Optimization for Probabilistic Programs. NIPS 2016: 280-288 - [c27]