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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
- 2023
[c74]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- [c73]Andreas Munk, Alexander Mead, Frank Wood:
Uncertain Evidence in Probabilistic Models and Stochastic Simulators. ICML 2023: 25486-25500 - [c72]Christian Dietrich Weilbach, William Harvey, Frank Wood:
Graphically Structured Diffusion Models. ICML 2023: 36887-36909 - [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
- [j11]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
- [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]Brooks Paige, Dino Sejdinovic, Frank D. Wood:
Super-Sampling with a Reservoir. UAI 2016 - [i19]Sam Staton, Hongseok Yang, Chris Heunen, Ohad Kammar, Frank D. Wood:
Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints. CoRR abs/1601.04943 (2016) - [i18]Mike Wu, Yura N. Perov, Frank D. Wood, Hongseok Yang:
Spreadsheet Probabilistic Programming. CoRR abs/1606.04216 (2016) - [i17]David Tolpin, Jan-Willem van de Meent, Hongseok Yang, Frank D. Wood:
Design and Implementation of Probabilistic Programming Language Anglican. CoRR abs/1608.05263 (2016) - [i16]Tuan Anh Le, Atilim Gunes Baydin, Frank D. Wood:
Inference Compilation and Universal Probabilistic Programming. CoRR abs/1610.09900 (2016) - [i15]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) - [i14]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
- [j8]Finale Doshi-Velez, David Pfau, Frank D. Wood, Nicholas Roy:
Bayesian Nonparametric Methods for Partially-Observable Reinforcement Learning. IEEE Trans. Pattern Anal. Mach. Intell. 37(2): 394-407 (2015) - [c26]Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka, Frank D. Wood:
Particle Gibbs with Ancestor Sampling for Probabilistic Programs. AISTATS 2015 - [c25]David Tolpin, Jan-Willem van de Meent, Frank D. Wood:
Probabilistic Programming in Anglican. ECML/PKDD (3) 2015: 308-311 - [c24]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 - [c23]David Tolpin, Frank D. Wood:
Maximum a Posteriori Estimation by Search in Probabilistic Programs. SOCS 2015: 201-205 - [i13]David Tolpin, Jan-Willem van de Meent, Brooks Paige, Frank D. Wood:
Adaptive Scheduling in MCMC and Probabilistic Programming. CoRR abs/1501.05677 (2015) - [i12]Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka, Frank D. Wood:
Particle Gibbs with Ancestor Sampling for Probabilistic Programs. CoRR abs/1501.06769 (2015) - [i11]David Tolpin, Brooks Paige, Frank D. Wood:
Path Finding under Uncertainty through Probabilistic Inference. CoRR abs/1502.07314 (2015) - [i10]David Tolpin, Frank D. Wood:
Maximum a Posteriori Estimation by Search in Probabilistic Programs. CoRR abs/1504.06848 (2015) - [i9]Frank D. Wood, Jan-Willem van de Meent, Vikash Mansinghka:
A New Approach to Probabilistic Programming Inference. CoRR abs/1507.00996 (2015) - [i8]Jan-Willem van de Meent, David Tolpin, Brooks Paige, Frank D. Wood:
Black-Box Policy Search with Probabilistic Programs. CoRR abs/1507.04635 (2015) - [i7]