Frank D. Wood

According to our database, Frank D. Wood authored at least 130 papers between 1996 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

On the Challenges and Opportunities in Generative AI.

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

CoRR, 2024

Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning.

Jason Yoo, Yunpeng Liu, Frank Wood, Geoff Pleiss

CoRR, 2024

Nearest Neighbour Score Estimators for Diffusion Generative Models.

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

CoRR, 2024

2023

Don't be so negative! Score-based Generative Modeling with Oracle-assisted Guidance.

Saeid Naderiparizi, Xiaoxuan Liang, Berend Zwartsenberg, Frank Wood

CoRR, 2023

Realistically distributing object placements in synthetic training data improves the performance of vision-based object detection models.

Setareh Dabiri, Vasileios Lioutas, Berend Zwartsenberg, Yunpeng Liu, Matthew Niedoba, Xiaoxuan Liang, Dylan Green, Justice Sefas, Jonathan Wilder Lavington, Frank Wood, Adam Scibior

CoRR, 2023

Video Killed the HD-Map: Predicting Driving Behavior Directly From Drone Images.

Yunpeng Liu, Vasileios Lioutas, Jonathan Wilder Lavington, Matthew Niedoba, Justice Sefas, Setareh Dabiri, Dylan Green, Xiaoxuan Liang, Berend Zwartsenberg, Adam Scibior, Frank Wood

CoRR, 2023

Visual Chain-of-Thought Diffusion Models.

William Harvey, Frank Wood

CoRR, 2023

A Diffusion-Model of Joint Interactive Navigation.

Matthew Niedoba, Jonathan Wilder Lavington, Yunpeng Liu, Vasileios Lioutas, Justice Sefas, Xiaoxuan Liang, Dylan Green, Setareh Dabiri, Berend Zwartsenberg, Adam Scibior, Frank Wood

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Video Killed the HD-Map: Predicting Multi-Agent Behavior Directly From Aerial Images.

Yunpeng Liu, Vasileios Lioutas, Jonathan Wilder Lavington, Matthew Niedoba, Justice Sefas, Setareh Dabiri, Dylan Green, Xiaoxuan Liang, Berend Zwartsenberg, Adam Scibior, Frank Wood

Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2023

Graphically Structured Diffusion Models.

Christian Dietrich Weilbach, William Harvey, Frank Wood

Proceedings of the International Conference on Machine Learning, 2023

Uncertain Evidence in Probabilistic Models and Stochastic Simulators.

Andreas Munk, Alexander Mead, Frank Wood

Proceedings of the International Conference on Machine Learning, 2023

Critic Sequential Monte Carlo.

Vasileios Lioutas, Jonathan Wilder Lavington, Justice Sefas, Matthew Niedoba, Yunpeng Liu, Berend Zwartsenberg, Setareh Dabiri, Frank Wood, Adam Scibior

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

TITRATED: Learned Human Driving Behavior without Infractions via Amortized Inference.

Vasileios Lioutas, Adam Scibior, Frank Wood

Trans. Mach. Learn. Res., 2022

Conditional Permutation Invariant Flows.

Berend Zwartsenberg, Adam Scibior, Matthew Niedoba, Vasileios Lioutas, Yunpeng Liu, Justice Sefas, Setareh Dabiri, Jonathan Wilder Lavington, Trevor Campbell, Frank Wood

CoRR, 2022

BayesPCN: A Continually Learnable Predictive Coding Associative Memory.

Jason Yoo, Frank Wood

CoRR, 2022

Gradients without Backpropagation.

Atilim Günes Baydin, Barak A. Pearlmutter, Don Syme, Frank Wood, Philip H. S. Torr

CoRR, 2022

Exploration with Multi-Sample Target Values for Distributional Reinforcement Learning.

Michael Teng, Michiel van de Panne, Frank Wood

CoRR, 2022

Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning.

Peyman Bateni, Jarred Barber, Raghav Goyal, Vaden Masrani, Jan-Willem van de Meent, Leonid Sigal, Frank Wood

CoRR, 2022

Enhancing Few-Shot Image Classification with Unlabelled Examples.

Peyman Bateni, Jarred Barber, Jan-Willem van de Meent, Frank Wood

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022

Probabilistic surrogate networks for simulators with unbounded randomness.

Andreas Munk, Berend Zwartsenberg, Adam Scibior, Atilim Günes Baydin, Andrew Stewart, Goran Fernlund, Anoush Poursartip, Frank Wood

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Attention for Inference Compilation.

William Harvey, Andreas Munk, Atilim Günes Baydin, Alexander Bergholm, Frank Wood

Proceedings of the 12th International Conference on Simulation and Modeling Methodologies, 2022

BayesPCN: A Continually Learnable Predictive Coding Associative Memory.

Jinsoo Yoo, Frank Wood

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Flexible Diffusion Modeling of Long Videos.

William Harvey, Saeid Naderiparizi, Vaden Masrani, Christian Weilbach, Frank Wood

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Vehicle Type Specific Waypoint Generation.

Yunpeng Liu, Jonathan Wilder Lavington, Adam Scibior, Frank Wood

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022

Near-Optimal Glimpse Sequences for Improved Hard Attention Neural Network Training.

William Harvey, Michael Teng, Frank Wood

Proceedings of the International Joint Conference on Neural Networks, 2022

Conditional Image Generation by Conditioning Variational Auto-Encoders.

William Harvey, Saeid Naderiparizi, Frank Wood

Proceedings of the Tenth International Conference on Learning Representations, 2022

Amortized Rejection Sampling in Universal Probabilistic Programming.

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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Probabilistic Programming Languages: Independent Choices and Deterministic Systems.

David Poole, Frank Wood

Proceedings of the Probabilistic and Causal Inference: The Works of Judea Pearl, 2022

2021

Planning as Inference in Epidemiological Dynamics Models.

Frank Wood, Andrew Warrington, Saeid Naderiparizi, Christian Weilbach, Vaden Masrani, William Harvey, Adam Scibior, Boyan Beronov, John Grefenstette, Duncan Campbell, Seyed Ali Nasseri

Frontiers Artif. Intell., 2021

Differentiable Particle Filtering without Modifying the Forward Pass.

Adam Scibior, Vaden Masrani, Frank Wood

CoRR, 2021

Image Completion via Inference in Deep Generative Models.

William Harvey, Saeid Naderiparizi, Frank Wood

CoRR, 2021

q-Paths: Generalizing the geometric annealing path using power means.

Vaden Masrani, Rob Brekelmans, Thang Bui, Frank Nielsen, Aram Galstyan, Greg Ver Steeg, Frank Wood

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Sequential core-set Monte Carlo.

Boyan Beronov, Christian Weilbach, Frank Wood, Trevor Campbell

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation.

Adam Scibior, Vasileios Lioutas, Daniele Reda, Peyman Bateni, Frank Wood

Proceedings of the 24th IEEE International Intelligent Transportation Systems Conference, 2021

Assisting the Adversary to Improve GAN Training.

Andreas Munk, William Harvey, Frank Wood

Proceedings of the International Joint Conference on Neural Networks, 2021

Robust Asymmetric Learning in POMDPs.

Andrew Warrington, Jonathan Wilder Lavington, Adam Scibior, Mark Schmidt, Frank Wood

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Target-Aware Bayesian Inference: How to Beat Optimal Conventional Estimators.

Tom Rainforth, Adam Golinski, Frank Wood, Sheheryar Zaidi

J. Mach. Learn. Res., 2020

Annealed Importance Sampling with q-Paths.

Rob Brekelmans, Vaden Masrani, Thang Bui, Frank Wood, Aram Galstyan, Greg Ver Steeg, Frank Nielsen

CoRR, 2020

Ensemble Squared: A Meta AutoML System.

Jason Yoo, Tony Joseph, Dylan Yung, Seyed Ali Nasseri, Frank Wood

CoRR, 2020

Gaussian Process Bandit Optimization of theThermodynamic Variational Objective.

Vu Nguyen, Vaden Masrani, Rob Brekelmans, Michael A. Osborne, Frank Wood

CoRR, 2020

Uncertainty in Neural Processes.

Saeid Naderiparizi, Kenny Chiu, Benjamin Bloem-Reddy, Frank Wood

CoRR, 2020

Improving Few-Shot Visual Classification with Unlabelled Examples.

Peyman Bateni, Jarred Barber, Jan-Willem van de Meent, Frank Wood

CoRR, 2020

Planning as Inference in Epidemiological Models.

Frank Wood, Andrew Warrington, Saeid Naderiparizi, Christian Weilbach, Vaden Masrani, William Harvey, Adam Scibior, Boyan Beronov, Seyed Ali Nasseri

CoRR, 2020

Semi-supervised Sequential Generative Models.

Michael Teng, Tuan Anh Le, Adam Scibior, Frank Wood

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective.

Vu Nguyen, Vaden Masrani, Rob Brekelmans, Michael A. Osborne, Frank Wood

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference.

Rob Brekelmans, Vaden Masrani, Frank Wood, Greg Ver Steeg, Aram Galstyan

Proceedings of the 37th International Conference on Machine Learning, 2020

Improved Few-Shot Visual Classification.

Peyman Bateni, Raghav Goyal, Vaden Masrani, Frank Wood, Leonid Sigal

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Structured Conditional Continuous Normalizing Flows for Efficient Amortized Inference in Graphical Models.

Christian Weilbach, Boyan Beronov, Frank Wood, William Harvey

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Coping With Simulators That Don't Always Return.

Andrew Warrington, Frank Wood, Saeid Naderiparizi

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Deep Probabilistic Surrogate Networks for Universal Simulator Approximation.

Andreas Munk, Adam Scibior, Atilim Günes Baydin, Andrew Stewart, Goran Fernlund, Anoush Poursartip, Frank Wood

CoRR, 2019

The Virtual Patch Clamp: Imputing C. elegans Membrane Potentials from Calcium Imaging.

Andrew Warrington, Arthur P. C. Spencer, Frank Wood

CoRR, 2019

Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow.

Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Etalumis: bringing probabilistic programming to scientific simulators at scale.

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

Proceedings of the International Conference for High Performance Computing, 2019

The Thermodynamic Variational Objective.

Vaden Masrani, Tuan Anh Le, Frank Wood

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

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 Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip H. S. Torr, Victor W. Lee, Kyle Cranmer, Prabhat, Frank Wood

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Amortized Monte Carlo Integration.

Adam Golinski, Frank Wood, Tom Rainforth

Proceedings of the 36th International Conference on Machine Learning, 2019

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, Frank Wood

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

An Introduction to Probabilistic Programming.

Jan-Willem van de Meent, Brooks Paige, Hongseok Yang, Frank Wood

CoRR, 2018

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model.

Atilim Gunes Baydin, Lukas Heinrich, Wahid Bhimji, Bradley Gram-Hansen, Gilles Louppe, Lei Shao, Prabhat, Kyle Cranmer, Frank D. Wood

CoRR, 2018

Revisiting Reweighted Wake-Sleep.

Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood

CoRR, 2018

Discontinuous Hamiltonian Monte Carlo for Probabilistic Programs.

Bradley Gram-Hansen, Yuan Zhou, Tobias Kohn, Hongseok Yang, Frank D. Wood

CoRR, 2018

High Throughput Synchronous Distributed Stochastic Gradient Descent.

Michael Teng, Frank Wood

CoRR, 2018

Faithful Inversion of Generative Models for Effective Amortized Inference.

Stefan Webb, Adam Golinski, Robert Zinkov, Siddharth Narayanaswamy, Tom Rainforth, Yee Whye Teh, Frank Wood

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Bayesian Distributed Stochastic Gradient Descent.

Michael Teng, Frank Wood

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Tighter Variational Bounds are Not Necessarily Better.

Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh

Proceedings of the 35th International Conference on Machine Learning, 2018

Deep Variational Reinforcement Learning for POMDPs.

Maximilian Igl, Luisa M. Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson

Proceedings of the 35th International Conference on Machine Learning, 2018

Auto-Encoding Sequential Monte Carlo.

Tuan Anh Le, Maximilian Igl, Tom Rainforth, Tom Jin, Frank Wood

Proceedings of the 6th International Conference on Learning Representations, 2018

Online Learning Rate Adaptation with Hypergradient Descent.

Atilim Gunes Baydin, Robert Cornish, David Martínez-Rubio, Mark Schmidt, Frank Wood

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Generalized Pólya Urn for Time-Varying Pitman-Yor Processes.

François Caron, Willie Neiswanger, Frank D. Wood, Arnaud Doucet, Manuel Davy

J. Mach. Learn. Res., 2017

Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators.

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

CoRR, 2017

Faithful Model Inversion Substantially Improves Auto-encoding Variational Inference.

Stefan Webb, Adam Golinski, Robert Zinkov, N. Siddharth, Yee Whye Teh, Frank D. Wood

CoRR, 2017

Updating the VESICLE-CNN Synapse Detector.

Andrew Warrington, Frank D. Wood

CoRR, 2017

Interpreting Lion Behaviour as Probabilistic Programs.

Neil Dhir, Matthijs Vákár, Matthew Wijers, Andrew Markham, Frank D. Wood

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Learning Disentangled Representations with Semi-Supervised Deep Generative Models.

Siddharth Narayanaswamy, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Noah D. Goodman, Pushmeet Kohli, Frank D. Wood, Philip H. S. Torr

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Using synthetic data to train neural networks is model-based reasoning.

Tuan Anh Le, Atilim Günes Baydin, Robert Zinkov, Frank D. Wood

Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Inference Compilation and Universal Probabilistic Programming.

Tuan Anh Le, Atilim Gunes Baydin, Frank D. Wood

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Spreadsheet Probabilistic Programming.

Mike Wu, Yura N. Perov, Frank D. Wood, Hongseok Yang

CoRR, 2016

Inducing Interpretable Representations with Variational Autoencoders.

N. Siddharth, Brooks Paige, Alban Desmaison, Jan-Willem van de Meent, Frank D. Wood, Noah D. Goodman, Pushmeet Kohli, Philip H. S. Torr

CoRR, 2016

Probabilistic structure discovery in time series data.

David Janz, Brooks Paige, Tom Rainforth, Jan-Willem van de Meent, Frank D. Wood

CoRR, 2016

Super-Sampling with a Reservoir.

Brooks Paige, Dino Sejdinovic, Frank D. Wood

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Bayesian Optimization for Probabilistic Programs.

Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank D. Wood

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints.

Sam Staton, Hongseok Yang, Frank D. Wood, Chris Heunen, Ohad Kammar

Proceedings of the 31st Annual ACM/IEEE Symposium on Logic in Computer Science, 2016

Nonparametric Bayesian models for unsupervised activity recognition and tracking.

Neil Dhir, Yura N. Perov, Frank D. Wood

Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016

Design and Implementation of Probabilistic Programming Language Anglican.

David Tolpin, Jan-Willem van de Meent, Hongseok Yang, Frank D. Wood

Proceedings of the 28th Symposium on the Implementation and Application of Functional Programming Languages, 2016

Interacting Particle Markov Chain Monte Carlo.

Tom Rainforth, Christian A. Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem van de Meent, Arnaud Doucet, Frank D. Wood

Proceedings of the 33nd International Conference on Machine Learning, 2016

Inference Networks for Sequential Monte Carlo in Graphical Models.

Brooks Paige, Frank D. Wood

Proceedings of the 33nd International Conference on Machine Learning, 2016

Black-Box Policy Search with Probabilistic Programs.

Jan-Willem van de Meent, Brooks Paige, David Tolpin, Frank D. Wood

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Automatic Sampler Discovery via Probabilistic Programming and Approximate Bayesian Computation.

Yura N. Perov, Frank D. Wood

Proceedings of the Artificial General Intelligence - 9th International Conference, 2016

2015

Bayesian Nonparametric Methods for Partially-Observable Reinforcement Learning.

Finale Doshi-Velez, David Pfau, Frank D. Wood, Nicholas Roy

IEEE Trans. Pattern Anal. Mach. Intell., 2015

Path Finding under Uncertainty through Probabilistic Inference.

David Tolpin, Brooks Paige, Frank D. Wood

CoRR, 2015

Adaptive Scheduling in MCMC and Probabilistic Programming.

David Tolpin, Jan-Willem van de Meent, Brooks Paige, Frank D. Wood

CoRR, 2015

Canonical Correlation Forests.

Tom Rainforth, Frank D. Wood

CoRR, 2015

Data-driven Sequential Monte Carlo in Probabilistic Programming.

Yura N. Perov, Tuan Anh Le, Frank D. Wood

CoRR, 2015

Maximum a Posteriori Estimation by Search in Probabilistic Programs.

David Tolpin, Frank D. Wood

Proceedings of the Eighth Annual Symposium on Combinatorial Search, 2015

Probabilistic Programming in Anglican.

David Tolpin, Jan-Willem van de Meent, Frank D. Wood

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs.

David Tolpin, Jan-Willem van de Meent, Brooks Paige, Frank D. Wood

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

Particle Gibbs with Ancestor Sampling for Probabilistic Programs.

Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka, Frank D. Wood

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Mixed Membership Classification for Documents with Hierarchically Structured Labels.

Frank D. Wood, Adler J. Perotte

Proceedings of the Handbook of Mixed Membership Models and Their Applications., 2014

Diagnosis code assignment: models and evaluation metrics.

Adler J. Perotte, Rimma Pivovarov, Karthik Natarajan, Nicole Gray Weiskopf, Frank D. Wood, Noemie Elhadad

J. Am. Medical Informatics Assoc., 2014

Learning Probabilistic Programs.

Yura N. Perov, Frank D. Wood

CoRR, 2014

Asynchronous Anytime Sequential Monte Carlo.

Brooks Paige, Frank D. Wood, Arnaud Doucet, Yee Whye Teh

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

A Compilation Target for Probabilistic Programming Languages.

Brooks Paige, Frank D. Wood

Proceedings of the 31th International Conference on Machine Learning, 2014

Improved activity recognition via Kalman smoothing and multiclass linear discriminant analysis.

Neil Dhir, Frank D. Wood

Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014

A New Approach to Probabilistic Programming Inference.

Frank D. Wood, Jan-Willem van de Meent, Vikash Mansinghka

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling.

Willie Neiswanger, Frank D. Wood, Eric P. Xing

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data.

Jan-Willem van de Meent, Jonathan E. Bronson, Frank D. Wood, Ruben L. Gonzalez, Chris Wiggins

Proceedings of the 30th International Conference on Machine Learning, 2013

A Joint Learning Model of Word Segmentation, Lexical Acquisition, and Phonetic Variability.

Micha Elsner, Sharon Goldwater, Naomi Feldman, Frank Wood

Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, 2013

2012

Inference in Hidden Markov Models with Explicit State Duration Distributions.

Michael Dewar, Chris Wiggins, Frank D. Wood

IEEE Signal Process. Lett., 2012

Low rank continuous-space graphical models.

Carl Smith, Frank D. Wood, Liam Paninski

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures

Willie Neiswanger, Frank D. Wood

CoRR, 2012

2011

Discussion of "The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling".

Frank D. Wood

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

The sequence memoizer.

Frank D. Wood, Jan Gasthaus, Cédric Archambeau, Lancelot James, Yee Whye Teh

Commun. ACM, 2011

Hierarchically Supervised Latent Dirichlet Allocation.

Adler J. Perotte, Frank D. Wood, Noemie Elhadad, Nicholas Bartlett

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Deplump for Streaming Data.

Nicholas Bartlett, Frank D. Wood

Proceedings of the 2011 Data Compression Conference (DCC 2011), 2011

2010

Probabilistic Deterministic Infinite Automata.

David Pfau, Nicholas Bartlett, Frank D. Wood

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process.

Nicholas Bartlett, David Pfau, Frank D. Wood

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

Lossless Compression Based on the Sequence Memoizer.

Jan Gasthaus, Frank D. Wood, Yee Whye Teh

Proceedings of the 2010 Data Compression Conference (DCC 2010), 2010

2009

A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation.

Frank D. Wood, Yee Whye Teh

Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

A stochastic memoizer for sequence data.

Frank D. Wood, Cédric Archambeau, Jan Gasthaus, Lancelot James, Yee Whye Teh

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008

Dependent Dirichlet Process Spike Sorting.

Jan Gasthaus, Frank D. Wood, Dilan Görür, Yee Whye Teh

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Characterizing neural dependencies with copula models.

Pietro Berkes, Frank D. Wood, Jonathan W. Pillow

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

2007

Nonparametric Bayesian Models for Neural Data.

Frank D. Wood

PhD thesis, 2007

Biological parametric mapping: A statistical toolbox for multimodality brain image analysis.

Ramon Casanova, Ryali Srikanth, Aaron H. Baer, Paul J. Laurienti, Jonathan H. Burdette, Satoru Hayasaka, Lynn Flowers, Frank Wood, Joseph A. Maldjian

NeuroImage, 2007

2006

Discovering natural kinds of robot sensory experiences in unstructured environments.

Daniel H. Grollman, Odest Chadwicke Jenkins, Frank D. Wood

J. Field Robotics, 2006

A Non-Parametric Bayesian Method for Inferring Hidden Causes.

Frank D. Wood, Thomas L. Griffiths, Zoubin Ghahramani

Proceedings of the UAI '06, 2006

Particle Filtering for Nonparametric Bayesian Matrix Factorization.

Frank D. Wood, Thomas L. Griffiths

Proceedings of the Advances in Neural Information Processing Systems 19, 2006

A Non-Parametric Bayesian Approach to Spike Sorting.

Frank Wood, Sharon Goldwater, Michael J. Black

Proceedings of the 28th International Conference of the IEEE Engineering in Medicine and Biology Society, 2006

2005

Modeling Neural Population Spiking Activity with Gibbs Distributions.

Frank D. Wood, Stefan Roth, Michael J. Black

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

2004

On the variability of manual spike sorting.

Frank D. Wood, Michael J. Black, Carlos Vargas-Irwin, Matthew Fellows, John P. Donoghue

IEEE Trans. Biomed. Eng., 2004

1996

WorkSpace and the study of Chagas' disease.

Frank D. Wood, Daniel Brown, Robert A. Amidon, Jonathan Alferness, Brian Joseph, Richard E. Gillilan, Carlos Faerman

IEEE Computer Graphics and Applications, 1996