Frank Wood

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Associate Academic Member
Frank Wood
Associate Professor, University of British Columbia
Frank Wood
https://plai.cs.ubc.ca/
+1(604) 822-3061

Frank Wood is Associate Professor of Computer Science at the University of British Columbia, and Chair in AI CIFAR-Canada at Mila. His research interests include probabilistic programming, as well as automatic learning and probabilistic artificial intelligence. He is particularly interested in Bayesian methods and unsupervised learning.

Publications

2021-02

Image Completion via Inference in Deep Generative Models
William Harvey, Saeid Naderiparizi and Frank Wood
arXiv preprint arXiv:2102.12037
(2021-02-24)
tw.arxiv.orgPDF

2020-12

Robust Asymmetric Learning in POMDPs.
Andrew Warrington, J. Wilder Lavington, Adam Scibior, Mark Schmidt and Frank Wood
arXiv: Learning
(2020-12-31)
arxiv.orgPDF
Annealed Importance Sampling with q-Paths
Rob Brekelmans, Vaden Masrani, Thang Bui, Frank Wood, Aram Galstyan, Greg Ver Steeg and Frank Nielsen
arXiv preprint arXiv:2012.07823
(2020-12-14)
arxiv.orgPDF
Ensemble Squared: A Meta AutoML System.
Jason Yoo, Tony Joseph, Dylan Yung, S. Ali Nasseri and Frank Wood
arXiv preprint arXiv:2012.05390
(2020-12-10)
arxiv.orgPDF

2020-10

Gaussian Process Bandit Optimization of theThermodynamic Variational Objective
Vu Nguyen, Vaden Masrani, Rob Brekelmans, Michael A. Osborne and Frank Wood
arXiv preprint arXiv:2010.15750
(2020-10-29)
arxiv.orgPDF
Uncertainty in Neural Processes
Saeid Naderiparizi, Kenny Chiu, Benjamin Bloem-Reddy and Frank Wood
arXiv: Learning
(2020-10-08)
arxiv.orgPDF
Assisting the Adversary to Improve GAN Training.
Andreas Munk, William Harvey and Frank Wood
arXiv: Learning
(2020-10-05)
arxiv.orgPDF

2020-09

Near-Optimal Glimpse Sequences for Training Hard Attention Neural Networks
William Harvey, Michael Teng and Frank Wood
(venue unknown)
(2020-09-28)
openreview.net

2020-07

All in the (Exponential) Family: Information Geometry and Thermodynamic Variational Inference
Robert Brekelmans, Vaden W Masrani, Frank Wood, Greg Ver Steeg and Aram Galstyan
ICML 2020
(2020-07-12)
icml.cc
All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference.
Rob Brekelmans, Vaden Masrani, Frank Wood, Greg Ver Steeg and Aram Galstyan
arXiv preprint arXiv:2007.00642
(2020-07-01)
dblp.uni-trier.dePDF

2020-06

Semi-supervised Sequential Generative Models.
Michael Teng, Tuan Anh Le, Adam Scibior and Frank Wood
arXiv preprint arXiv:2007.00155
(2020-06-30)
dblp.uni-trier.dePDF
Improving Few-Shot Visual Classification with Unlabelled Examples.
Peyman Bateni, Jarred Barber, Jan-Willem van de Meent and Frank Wood
arXiv preprint arXiv:2006.12245
(2020-06-17)
dblp.uni-trier.dePDF
Improved Few-Shot Visual Classification
Peyman Bateni, Raghav Goyal, Vaden Masrani, Frank Wood and Leonid Sigal
CVPR 2020
(2020-06-14)
openaccess.thecvf.comPDF[Also on arXiv preprint arXiv:1912.03432 (2019-12-07)]

2020-03

Planning as Inference in Epidemiological Models.
Frank Wood, Andrew Warrington, Saeid Naderiparizi, Christian Weilbach, Vaden Masrani, William Harvey, Adam Scibior, Boyan Beronov and Ali Nasseri
arXiv preprint arXiv:2003.13221
(2020-03-30)
dblp.uni-trier.dePDF
Coping With Simulators That Don't Always Return
Andrew Warrington, Saeid Naderiparizi and Frank Wood
arXiv preprint arXiv:2003.12908
(2020-03-28)
ui.adsabs.harvard.eduPDF

2020-01

All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference.
Rob Brekelmans, Vaden Masrani, Frank Wood, Greg Ver Steeg and Aram Galstyan
ICML 2020
(2020-01-01)
dblp.uni-trier.de
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective
Vu Nguyen, Vaden Masrani, Rob Brekelmans, Michael Osborne and Frank Wood
NEURIPS 2020
(2020-01-01)
papers.nips.cc
Semi-supervised Sequential Generative Models.
Michael Teng, Tuan Anh Le, Adam Scibior and Frank Wood
UAI 2020
(2020-01-01)
dblp.uni-trier.de
Target–Aware Bayesian Inference: How to Beat Optimal Conventional Estimators
Tom Rainforth, Adam Golinski, Frank Wood and Sheheryar Zaidi
Journal of Machine Learning Research
(2020-01-01)
jmlr.csail.mit.eduPDF
Structured Conditional Continuous Normalizing Flows for Efficient Amortized Inference in Graphical Models.
Christian Weilbach, Boyan Beronov, Frank Wood and William Harvey
AISTATS 2020
(2020-01-01)
proceedings.mlr.press
Coping With Simulators That Don't Always Return.
Andrew Warrington, Frank Wood and Saeid Naderiparizi
AISTATS 2020
(2020-01-01)
proceedings.mlr.pressPDF

2019-12

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
Atilim Gunes Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Saeid Naderiparizi, Andreas Munk, Jialin Liu, Bradley J Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip Torr, Victor Lee, Kyle Cranmer, Prabhat and Frank Wood
NEURIPS 2019
(2019-12-08)
papers.nips.ccPDF
The Thermodynamic Variational Objective
Vaden Masrani, Tuan Anh Le and Frank Wood
NEURIPS 2019
(2019-12-08)
papers.nips.ccPDF[Also on arXiv preprint arXiv:1907.00031 (2019-06-28)]

2019-11

Etalumis: bringing probabilistic programming to scientific simulators at scale
Atilim Güneş 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 Torr, Victor Lee, Kyle Cranmer, Prabhat and Frank Wood
HIPC 2019
(2019-11-17)
inspirehep.net
Etalumis: bringing probabilistic programming to scientific simulators at scale
Atilim Güneş 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 Torr, Victor Lee, Kyle Cranmer, Prabhat and Frank Wood
HIPC 2019
(2019-11-17)
inspirehep.netPDF
Etalumis: bringing probabilistic programming to scientific simulators at scale
Atilim Güneş 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 Torr, Victor Lee, Kyle Cranmer, Prabhat and Frank Wood
HIPC 2019
(2019-11-17)
inspirehep.net

2019-10

Attention for Inference Compilation.
William Harvey, Andreas Munk, Atilim Günes Baydin, Alexander Bergholm and Frank Wood
arXiv preprint arXiv:1910.11961
(2019-10-25)
dblp.uni-trier.dePDF
Deep Probabilistic Surrogate Networks for Universal Simulator Approximation.
Andreas Munk, Adam Scibior, Atilim Günes Baydin, Andrew Stewart, Goran Fernlund, Anoush Poursartip and Frank Wood
arXiv preprint arXiv:1910.11950
(2019-10-25)
dblp.uni-trier.dePDF
Amortized Rejection Sampling in Universal Probabilistic Programming.
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 and Frank Wood
arXiv preprint arXiv:1910.09056
(2019-10-20)
ui.adsabs.harvard.eduPDF
Efficient Bayesian Inference for Nested Simulators
Bradley Gram-Hansen, Christian Schroeder de Witt, Robert Zinkov, Saeid Naderiparizi, Adam Scibior, Andreas Munk, Frank Wood, Mehrdad Ghadiri, Philip Torr, Yee Whye Teh, Atilim Gunes Baydin and Tom Rainforth
(venue unknown)
(2019-10-16)
openreview.netPDF
Efficient Inference Amortization in Graphical Models using Structured Continuous Conditional Normalizing Flows
Christian Weilbach, Boyan Beronov, William Harvey and Frank Wood
(venue unknown)
(2019-10-16)
openreview.netPDF

2019-09

Safer End-to-End Autonomous Driving via Conditional Imitation Learning and Command Augmentation
Renhao Wang, Adam Scibior and Frank Wood
arXiv preprint arXiv:1909.09721
(2019-09-20)
ui.adsabs.harvard.eduPDF
The Virtual Patch Clamp: Imputing C. elegans Membrane Potentials from Calcium Imaging.
Andrew Warrington, Arthur Spencer and Frank Wood
arXiv preprint arXiv:1907.11075
(2019-09-11)
dblp.uni-trier.dePDF

2019-06

Near-Optimal Glimpse Sequences for Improved Hard Attention Neural Network Training.
William Harvey, Michael Teng and Frank Wood
arXiv preprint arXiv:1906.05462
(2019-06-13)
dblp.uni-trier.dePDF
Amortized Monte Carlo Integration
Adam Golinski, Frank Wood and Tom Rainforth
ICML 2019
(2019-06-09)
proceedings.mlr.pressPDF[LATEST on arXiv preprint arXiv:1907.08082 (2019-07-18)]

2019-04

LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models
Yuan Zhou, Bradley J. Gram-Hansen, Tobias Kohn, Tom Rainforth, Hongseok Yang and Frank Wood
AISTATS 2019
(2019-04-11)
proceedings.mlr.pressPDF[Also on arXiv preprint arXiv:1903.02482 (2019-03-06)]

2019-03

Imitation Learning of Factored Multi-agent Reactive Models
Michael Teng, Tuan Anh Le, Adam Scibior and Frank Wood
arXiv preprint arXiv:1903.04714
(2019-03-12)
arxiv.orgPDF

2019-01

Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow.
Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh and Frank Wood
UAI 2019
(2019-01-01)
dblp.uni-trier.dePDF[LATEST on arXiv: Machine Learning (2019-09-16)]

2018-12

Faithful Inversion of Generative Models for Effective Amortized Inference
Stefan Webb, Adam Golinski, Rob Zinkov, Siddharth Narayanaswamy, Tom Rainforth, Yee Whye Teh and Frank Wood
NEURIPS 2018
(2018-12-03)
papers.nips.ccPDF
Bayesian Distributed Stochastic Gradient Descent
Michael Teng and Frank Wood
NEURIPS 2018
(2018-12-03)
papers.nips.ccPDF

2018-11

An Introduction to Probabilistic Programming.
J-W van de Meent, B Paige, H Yang and F Wood
arXiv preprint arXiv:1809.10756
(2018-11-14)
publications.eng.cam.ac.ukPDF

2018-09

Revisiting Reweighted Wake-Sleep
Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh and Frank Wood
arXiv preprint arXiv:1805.10469
(2018-09-27)
dblp.uni-trier.dePDF

2018-07

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
Atılım Güneş Baydin, Lukas Heinrich, Wahid Bhimji, Lei Shao, Saeid Naderiparizi, Andreas Munk, Jialin Liu, Bradley Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip Torr, Victor Lee, Prabhat, Kyle Cranmer and Frank Wood
arXiv preprint arXiv:1807.07706
(2018-07-20)
export.arxiv.orgPDF
On Nesting Monte Carlo Estimators
Tom Rainforth, Rob Cornish, Hongseok Yang, andrew warrington and Frank Wood
ICML 2018
(2018-07-10)
proceedings.mlr.pressPDF
Deep Variational Reinforcement Learning for POMDPs
Maximilian Igl, Luisa Zintgraf, Tuan Anh Le, Frank Wood and Shimon Whiteson
ICML 2018
(2018-07-10)
proceedings.mlr.pressPDF[Also on arXiv preprint arXiv:1806.02426 (2018-06-06)]
Tighter Variational Bounds are Not Necessarily Better
Tom Rainforth, Adam Kosiorek, Tuan Anh Le, Chris Maddison, Maximilian Igl, Frank Wood and Yee Whye Teh
ICML 2018
(2018-07-10)
proceedings.mlr.pressPDF

2018-06

Inference Trees: Adaptive Inference with Exploration
Tom Rainforth, Yuan Zhou, Xiaoyu Lu, Yee Whye Teh, Frank Wood, Hongseok Yang and Jan-Willem van de Meent
arXiv preprint arXiv:1806.09550
(2018-06-25)
ui.adsabs.harvard.eduPDF

2018-04

Discontinuous Hamiltonian Monte Carlo for Probabilistic Programs.
Bradley Gram-Hansen, Yuan Zhou, Tobias Kohn, Hongseok Yang and Frank D. Wood
(venue unknown)
(2018-04-07)
dblp.uni-trier.dePDF
Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities
Bradley Gram-Hansen, Yuan Zhou, Tobias Kohn, Tom Rainforth, Hongseok Yang and Frank Wood
arXiv preprint arXiv:1804.03523
(2018-04-07)
ui.adsabs.harvard.eduPDF

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