Frank D. Wood

Publication Activity (10 Years)

Years Active: 1996-2018
Publications (10 Years): 29

Top Topics

Programming Language

Dirichlet Process Mixture

Probabilistic Inference

Top Venues

Publications

Atilim Gunes Baydin, Lukas Alexander 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 (2018)
Bradley Gram-Hansen, Yuan Zhou, Tobias Kohn, Hongseok Yang, Frank D. Wood
Discontinuous Hamiltonian Monte Carlo for Probabilistic Programs. CoRR (2018)
Mario Lezcano Casado, Atilim Gunes Baydin, David Martínez-Rubio, Tuan Anh Le, Frank D. Wood, Lukas Alexander Heinrich, Gilles Louppe, Kyle Cranmer, Karen Ng, Wahid Bhimji, Prabhat
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators. CoRR (2017)
Neil Dhir, Matthijs Vákár, Matthew Wijers, Andrew Markham, Frank D. Wood
Interpreting Lion Behaviour as Probabilistic Programs. UAI (2017)
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 (2017)
Tuan Anh Le, Atilim Günes Baydin, Robert Zinkov, Frank D. Wood
Using synthetic data to train neural networks is model-based reasoning. IJCNN (2017)
Stefan Webb, Adam Golinski, Robert Zinkov, N. Siddharth, Yee Whye Teh, Frank D. Wood
Faithful Model Inversion Substantially Improves Auto-encoding Variational Inference. CoRR (2017)
Atilim Gunes Baydin, Robert Cornish, David Martínez-Rubio, Mark Schmidt, Frank D. Wood
Online Learning Rate Adaptation with Hypergradient Descent. CoRR (2017)
Andrew Warrington, Frank D. Wood
Updating the VESICLE-CNN Synapse Detector. CoRR (2017)
Tuan Anh Le, Atilim Gunes Baydin, Frank D. Wood
Inference Compilation and Universal Probabilistic Programming. AISTATS (2017)
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)
Tuan Anh Le, Atilim Gunes Baydin, Robert Zinkov, Frank D. Wood
Using Synthetic Data to Train Neural Networks is Model-Based Reasoning. CoRR (2017)
Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank D. Wood
Bayesian Optimization for Probabilistic Programs. CoRR (2017)
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 (2017)
Neil Dhir, Yura N. Perov, Frank D. Wood
Nonparametric Bayesian models for unsupervised activity recognition and tracking. IROS (2016)
David Tolpin, Jan-Willem van de Meent, Hongseok Yang, Frank D. Wood
Design and Implementation of Probabilistic Programming Language Anglican. CoRR (2016)
Jan-Willem van de Meent, Brooks Paige, David Tolpin, Frank D. Wood
Black-Box Policy Search with Probabilistic Programs. AISTATS (2016)
David Janz, Brooks Paige, Tom Rainforth, Jan-Willem van de Meent, Frank D. Wood
Probabilistic structure discovery in time series data. CoRR (2016)
Brooks Paige, Dino Sejdinovic, Frank D. Wood
Super-Sampling with a Reservoir. UAI (2016)
Sam Staton, Hongseok Yang, Chris Heunen, Ohad Kammar, Frank D. Wood
Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints. CoRR (2016)
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)
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)
Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank D. Wood
Bayesian Optimization for Probabilistic Programs. NIPS (2016)
Brooks Paige, Frank D. Wood
Inference Networks for Sequential Monte Carlo in Graphical Models. ICML (2016)
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 (2016)
Yura N. Perov, Frank D. Wood
Automatic Sampler Discovery via Probabilistic Programming and Approximate Bayesian Computation. AGI (2016)
Tuan Anh Le, Atilim Gunes Baydin, Frank D. Wood
Inference Compilation and Universal Probabilistic Programming. CoRR (2016)
Mike Wu, Yura N. Perov, Frank D. Wood, Hongseok Yang
Spreadsheet Probabilistic Programming. CoRR (2016)
David Tolpin, Jan-Willem van de Meent, Hongseok Yang, Frank D. Wood
Design and Implementation of Probabilistic Programming Language Anglican. IFL (2016)
David Tolpin, Jan-Willem van de Meent, Brooks Paige, Frank D. Wood
Adaptive Scheduling in MCMC and Probabilistic Programming. CoRR (2015)
Frank D. Wood, Jan-Willem van de Meent, Vikash Mansinghka
A New Approach to Probabilistic Programming Inference. CoRR (2015)
David Tolpin, Brooks Paige, Frank D. Wood
Path Finding under Uncertainty through Probabilistic Inference. CoRR (2015)
Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka, Frank D. Wood
Particle Gibbs with Ancestor Sampling for Probabilistic Programs. AISTATS (2015)
Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka, Frank D. Wood
Particle Gibbs with Ancestor Sampling for Probabilistic Programs. CoRR (2015)
Tom Rainforth, Frank D. Wood
Canonical Correlation Forests. CoRR (2015)
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) (2015)
David Tolpin, Jan-Willem van de Meent, Brooks Paige, Frank D. Wood
Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs. ECML/PKDD (2) (2015)
David Tolpin, Jan-Willem van de Meent, Frank D. Wood
Probabilistic Programming in Anglican. ECML/PKDD (3) (2015)
Jan-Willem van de Meent, David Tolpin, Brooks Paige, Frank D. Wood
Black-Box Policy Search with Probabilistic Programs. CoRR (2015)
Yura N. Perov, Tuan Anh Le, Frank D. Wood
Data-driven Sequential Monte Carlo in Probabilistic Programming. CoRR (2015)
David Tolpin, Frank D. Wood
Maximum a Posteriori Estimation by Search in Probabilistic Programs. SOCS (2015)
David Tolpin, Frank D. Wood
Maximum a Posteriori Estimation by Search in Probabilistic Programs. CoRR (2015)
Brooks Paige, Frank D. Wood, Arnaud Doucet, Yee Whye Teh
Asynchronous Anytime Sequential Monte Carlo. NIPS (2014)
Brooks Paige, Frank D. Wood
A Compilation Target for Probabilistic Programming Languages. ICML (2014)
Willie Neiswanger, Frank D. Wood, Eric P. Xing
The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling. AISTATS (2014)
Brooks Paige, Frank D. Wood
A Compilation Target for Probabilistic Programming Languages. CoRR (2014)
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) (2014)
Frank D. Wood, Jan-Willem van de Meent, Vikash Mansinghka
A New Approach to Probabilistic Programming Inference. AISTATS (2014)
Yura N. Perov, Frank D. Wood
Learning Probabilistic Programs. CoRR (2014)
Neil Dhir, Frank D. Wood
Improved activity recognition via Kalman smoothing and multiclass linear discriminant analysis. EMBC (2014)
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)
Carl Smith, Frank D. Wood, Liam Paninski
Low rank continuous-space graphical models. AISTATS (2012)
Michael Dewar, Chris Wiggins, Frank D. Wood
Inference in Hidden Markov Models with Explicit State Duration Distributions CoRR (2012)
Willie Neiswanger, Frank D. Wood
Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures CoRR (2012)
Michael Dewar, Chris Wiggins, Frank D. Wood
Inference in Hidden Markov Models with Explicit State Duration Distributions. IEEE Signal Process. Lett. 19 (4) (2012)
Frank D. Wood, Thomas L. Griffiths, Zoubin Ghahramani
A Non-Parametric Bayesian Method for Inferring Hidden Causes. CoRR (2012)
Frank D. Wood, Jan Gasthaus, Cédric Archambeau, Lancelot James, Yee Whye Teh
The sequence memoizer. Commun. ACM 54 (2) (2011)
Adler J. Perotte, Frank D. Wood, Noemie Elhadad, Nicholas Bartlett
Hierarchically Supervised Latent Dirichlet Allocation. NIPS (2011)
Frank D. Wood
Discussion of "The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling". AISTATS (2011)
Nicholas Bartlett, Frank D. Wood
Deplump for Streaming Data. DCC (2011)
David Pfau, Nicholas Bartlett, Frank D. Wood
Probabilistic Deterministic Infinite Automata. NIPS (2010)
Nicholas Bartlett, David Pfau, Frank D. Wood
Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process. ICML (2010)
Jan Gasthaus, Frank D. Wood, Yee Whye Teh
Lossless Compression Based on the Sequence Memoizer. DCC (2010)
Frank D. Wood, Yee Whye Teh
A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation. AISTATS (2009)
Frank D. Wood, Cédric Archambeau, Jan Gasthaus, Lancelot James, Yee Whye Teh
A stochastic memoizer for sequence data. ICML (2009)
Jan Gasthaus, Frank D. Wood, Dilan Görür, Yee Whye Teh
Dependent Dirichlet Process Spike Sorting. NIPS (2008)
Pietro Berkes, Frank D. Wood, Jonathan W. Pillow
Characterizing neural dependencies with copula models. NIPS (2008)
Frank D. Wood, Thomas L. Griffiths
Particle Filtering for Nonparametric Bayesian Matrix Factorization. NIPS (2006)
Frank D. Wood, Thomas L. Griffiths, Zoubin Ghahramani
A Non-Parametric Bayesian Method for Inferring Hidden Causes. UAI (2006)
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) (2006)
Frank D. Wood, Stefan Roth, Michael J. Black
Modeling Neural Population Spiking Activity with Gibbs Distributions. NIPS (2005)
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) (2004)
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) (1996)