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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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Books and Theses
- 2007
[b1]Frank D. Wood:
Nonparametric Bayesian Models for Neural Data. Brown University, USA, 2007
Journal Articles
- 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) - 2022
- [j12]Vasileios Lioutas, Adam Scibior, Frank Wood:
TITRATED: Learned Human Driving Behavior without Infractions via Amortized Inference. Trans. Mach. Learn. Res. 2022 (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) - 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) - 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) - 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) - 2014
- [j7]Adler J. Perotte, Rimma Pivovarov, Karthik Natarajan, Nicole Gray Weiskopf, Frank D. Wood, Noemie Elhadad:
Diagnosis code assignment: models and evaluation metrics. J. Am. Medical Informatics Assoc. 21(2): 231-237 (2014) - 2012
- [j6]Michael Dewar, Chris Wiggins, Frank D. Wood:
Inference in Hidden Markov Models with Explicit State Duration Distributions. IEEE Signal Process. Lett. 19(4): 235-238 (2012) - 2011
- [j5]Frank D. Wood, Jan Gasthaus, Cédric Archambeau, Lancelot James, Yee Whye Teh:
The sequence memoizer. Commun. ACM 54(2): 91-98 (2011) - 2007
- [j4]Ramon Casanova, Ryali Srikanth, Aaron H. Baer, Paul J. Laurienti, Jonathan H. Burdette, Satoru Hayasaka, Lynn Flowers, Frank Wood, Joseph A. Maldjian:
Biological parametric mapping: A statistical toolbox for multimodality brain image analysis. NeuroImage 34(1): 137-143 (2007) - 2006
- [j3]Daniel H. Grollman, Odest Chadwicke Jenkins, Frank D. Wood:
Discovering natural kinds of robot sensory experiences in unstructured environments. J. Field Robotics 23(11-12): 1077-1089 (2006) - 2004
- [j2]Frank D. Wood, Michael J. Black, Carlos Vargas-Irwin, Matthew Fellows, John P. Donoghue:
On the variability of manual spike sorting. IEEE Trans. Biomed. Eng. 51(6): 912-918 (2004) - 1996
- [j1]Frank D. Wood, Daniel Brown, Robert A. Amidon, Jonathan Alferness, Brian Joseph, Richard E. Gillilan, Carlos Faerman:
WorkSpace and the study of Chagas' disease. IEEE Computer Graphics and Applications 16(4): 72-78 (1996)
Conference and Workshop Papers
- 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 2014
- [c22]Willie Neiswanger, Frank D. Wood, Eric P. Xing:
The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling. AISTATS 2014: 660-668 - [c21]Frank D. Wood, Jan-Willem van de Meent, Vikash Mansinghka:
A New Approach to Probabilistic Programming Inference. AISTATS 2014: 1024-1032 - [c20]Neil Dhir, Frank D. Wood:
Improved activity recognition via Kalman smoothing and multiclass linear discriminant analysis. EMBC 2014: 582-585 - [c19]Brooks Paige, Frank D. Wood:
A Compilation Target for Probabilistic Programming Languages. ICML 2014: 1935-1943 - [c18]Brooks Paige, Frank D. Wood, Arnaud Doucet, Yee Whye Teh:
Asynchronous Anytime Sequential Monte Carlo. NIPS 2014: 3410-3418 - 2013
- [c17]Micha Elsner, Sharon Goldwater, Naomi Feldman, Frank Wood:
A Joint Learning Model of Word Segmentation, Lexical Acquisition, and Phonetic Variability. EMNLP 2013: 42-54 - [c16]Jan-Willem van de Meent, Jonathan E. Bronson, Frank D. Wood, Ruben L. Gonzalez, Chris Wiggins:
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data. ICML (2) 2013: 361-369 - 2012
- [c15]Carl Smith, Frank D. Wood, Liam Paninski:
Low rank continuous-space graphical models. AISTATS 2012: 1064-1072 - 2011
- [c14]Nicholas Bartlett, Frank D. Wood:
Deplump for Streaming Data. DCC 2011: 363-372 - [c13]Adler J. Perotte, Frank D. Wood, Noemie Elhadad, Nicholas Bartlett:
Hierarchically Supervised Latent Dirichlet Allocation. NIPS 2011: 2609-2617 - [c12]Frank D. Wood:
Discussion of "The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling". AISTATS 2011: 83-84 - 2010
- [c11]Jan Gasthaus, Frank D. Wood, Yee Whye Teh:
Lossless Compression Based on the Sequence Memoizer. DCC 2010: 337-345 - [c10]Nicholas Bartlett, David Pfau, Frank D. Wood:
Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process. ICML 2010: 63-70 - [c9]David Pfau, Nicholas Bartlett, Frank D. Wood:
Probabilistic Deterministic Infinite Automata. NIPS 2010: 1930-1938 - 2009
- [c8]Frank D. Wood, Cédric Archambeau, Jan Gasthaus, Lancelot James, Yee Whye Teh:
A stochastic memoizer for sequence data. ICML 2009: 1129-1136 - [c7]Frank D. Wood, Yee Whye Teh:
A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation. AISTATS 2009: 607-614 - 2008
- [c6]Pietro Berkes, Frank D. Wood, Jonathan W. Pillow:
Characterizing neural dependencies with copula models. NIPS 2008: 129-136 - [c5]Jan Gasthaus, Frank D. Wood, Dilan Görür, Yee Whye Teh:
Dependent Dirichlet Process Spike Sorting. NIPS 2008: 497-504 - 2006
- [c4]Frank Wood, Sharon Goldwater, Michael J. Black:
A Non-Parametric Bayesian Approach to Spike Sorting. EMBC 2006: 1165-1168 - [c3]Frank D. Wood, Thomas L. Griffiths:
Particle Filtering for Nonparametric Bayesian Matrix Factorization. NIPS 2006: 1513-1520 - [c2]Frank D. Wood, Thomas L. Griffiths, Zoubin Ghahramani:
A Non-Parametric Bayesian Method for Inferring Hidden Causes. UAI 2006 - 2005
- [c1]Frank D. Wood, Stefan Roth, Michael J. Black:
Modeling Neural Population Spiking Activity with Gibbs Distributions. NIPS 2005: 1537-1544
Parts in Books or Collections
- 2022
- [p1]David Poole, Frank Wood:
Probabilistic Programming Languages: Independent Choices and Deterministic Systems. Probabilistic and Causal Inference 2022: 691-712
Reference Works
- 2014
- [r1]Frank D. Wood, Adler J. Perotte:
Mixed Membership Classification for Documents with Hierarchically Structured Labels. Handbook of Mixed Membership Models and Their Applications 2014: 305-323
Informal and Other Publications
- 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
- [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
- [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
- [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
- [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) - 2019
- [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
- [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
- [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
- [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
- [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]Tom Rainforth, Frank D. Wood:
Canonical Correlation Forests. CoRR abs/1507.05444 (2015) - [i6]Yura N. Perov, Tuan Anh Le, Frank D. Wood:
Data-driven Sequential Monte Carlo in Probabilistic Programming. CoRR abs/1512.04387 (2015) - 2014
- [i5]Brooks Paige, Frank D. Wood:
A Compilation Target for Probabilistic Programming Languages. CoRR abs/1403.0504 (2014) - [i4]Yura N. Perov, Frank D. Wood:
Learning Probabilistic Programs. CoRR abs/1407.2646 (2014) - 2012
- [i3]Michael Dewar, Chris Wiggins, Frank D. Wood:
Inference in Hidden Markov Models with Explicit State Duration Distributions. CoRR abs/1203.0038 (2012) - [i2]Frank D. Wood, Thomas L. Griffiths, Zoubin Ghahramani:
A Non-Parametric Bayesian Method for Inferring Hidden Causes. CoRR abs/1206.6865 (2012) - [i1]Willie Neiswanger, Frank D. Wood:
Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures. CoRR abs/1210.3288 (2012)
Coauthor Index
Atilim Günes Baydin
aka: Atilim Gunes Baydin
aka: Atilim Gunes Baydin
Bradley Gram-Hansen
aka: Bradley J. Gram-Hansen
aka: Bradley J. Gram-Hansen
J. Wilder Lavington
aka: Jonathan Wilder Lavington
aka: Jonathan Wilder Lavington
Christian Weilbach
aka: Christian Dietrich Weilbach
aka: Christian Dietrich Weilbach
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