Frank D. Wood - RankMe

75 papers A* 6A 12B 4Journal 36Unranked 15

Year	Rank	Type	Title / Venue / Authors
2018	J	`jnl`	Discontinuous Hamiltonian Monte Carlo for Probabilistic Programs. CoRR Bradley Gram-Hansen, Yuan Zhou, Tobias Kohn, Hongseok Yang, Frank D. Wood
2018	J	`jnl`	Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. CoRR Atilim Gunes Baydin, Lukas Heinrich, Wahid Bhimji, Bradley Gram-Hansen, Gilles Louppe, Lei Shao, Prabhat, Kyle Cranmer, Frank D. Wood
2017	J	`jnl`	Bayesian Optimization for Probabilistic Programs. CoRR Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank D. Wood
2017	J	`jnl`	Faithful Model Inversion Substantially Improves Auto-encoding Variational Inference. CoRR Stefan Webb, Adam Golinski, Robert Zinkov, N. Siddharth, Yee Whye Teh, Frank D. Wood
2017	J	`jnl`	Generalized Pólya Urn for Time-Varying Pitman-Yor Processes. J. Mach. Learn. Res. François Caron, Willie Neiswanger, Frank D. Wood, Arnaud Doucet, Manuel Davy
2017	J	`jnl`	Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators. CoRR 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
2017	A	`conf`	Inference Compilation and Universal Probabilistic Programming. AISTATS Tuan Anh Le, Atilim Gunes Baydin, Frank D. Wood
2017	A	`conf`	Interpreting Lion Behaviour as Probabilistic Programs. UAI Neil Dhir, Matthijs Vákár, Matthew Wijers, Andrew Markham, Frank D. Wood
2017	—	`conf`	Learning Disentangled Representations with Semi-Supervised Deep Generative Models. NIPS Siddharth Narayanaswamy, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Noah D. Goodman, Pushmeet Kohli, Frank D. Wood, Philip H. S. Torr
2017	J	`jnl`	Learning Disentangled Representations with Semi-Supervised Deep Generative Models. CoRR N. Siddharth, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Frank D. Wood, Noah D. Goodman, Pushmeet Kohli, Philip H. S. Torr
2017	J	`jnl`	Online Learning Rate Adaptation with Hypergradient Descent. CoRR Atilim Gunes Baydin, Robert Cornish, David Martínez-Rubio, Mark Schmidt, Frank D. Wood
2017	J	`jnl`	Updating the VESICLE-CNN Synapse Detector. CoRR Andrew Warrington, Frank D. Wood
2017	J	`jnl`	Using Synthetic Data to Train Neural Networks is Model-Based Reasoning. CoRR Tuan Anh Le, Atilim Gunes Baydin, Robert Zinkov, Frank D. Wood
2017	B	`conf`	Using synthetic data to train neural networks is model-based reasoning. IJCNN Tuan Anh Le, Atilim Günes Baydin, Robert Zinkov, Frank D. Wood
2016	—	`conf`	Automatic Sampler Discovery via Probabilistic Programming and Approximate Bayesian Computation. AGI Yura N. Perov, Frank D. Wood
2016	—	`conf`	Bayesian Optimization for Probabilistic Programs. NIPS Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank D. Wood
2016	A	`conf`	Black-Box Policy Search with Probabilistic Programs. AISTATS Jan-Willem van de Meent, Brooks Paige, David Tolpin, Frank D. Wood
2016	—	`conf`	Design and Implementation of Probabilistic Programming Language Anglican. IFL David Tolpin, Jan-Willem van de Meent, Hongseok Yang, Frank D. Wood
2016	J	`jnl`	Design and Implementation of Probabilistic Programming Language Anglican. CoRR David Tolpin, Jan-Willem van de Meent, Hongseok Yang, Frank D. Wood
2016	J	`jnl`	Inducing Interpretable Representations with Variational Autoencoders. CoRR N. Siddharth, Brooks Paige, Alban Desmaison, Jan-Willem van de Meent, Frank D. Wood, Noah D. Goodman, Pushmeet Kohli, Philip H. S. Torr
2016	J	`jnl`	Inference Compilation and Universal Probabilistic Programming. CoRR Tuan Anh Le, Atilim Gunes Baydin, Frank D. Wood
2016	A*	`conf`	Inference Networks for Sequential Monte Carlo in Graphical Models. ICML Brooks Paige, Frank D. Wood
2016	A*	`conf`	Interacting Particle Markov Chain Monte Carlo. ICML Tom Rainforth, Christian A. Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem van de Meent, Arnaud Doucet, Frank D. Wood
2016	A	`conf`	Nonparametric Bayesian models for unsupervised activity recognition and tracking. IROS Neil Dhir, Yura N. Perov, Frank D. Wood
2016	J	`jnl`	Probabilistic structure discovery in time series data. CoRR David Janz, Brooks Paige, Tom Rainforth, Jan-Willem van de Meent, Frank D. Wood
2016	A*	`conf`	Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints. LICS Sam Staton, Hongseok Yang, Frank D. Wood, Chris Heunen, Ohad Kammar
2016	J	`jnl`	Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints. CoRR Sam Staton, Hongseok Yang, Chris Heunen, Ohad Kammar, Frank D. Wood
2016	J	`jnl`	Spreadsheet Probabilistic Programming. CoRR Mike Wu, Yura N. Perov, Frank D. Wood, Hongseok Yang
2016	A	`conf`	Super-Sampling with a Reservoir. UAI Brooks Paige, Dino Sejdinovic, Frank D. Wood
2015	J	`jnl`	A New Approach to Probabilistic Programming Inference. CoRR Frank D. Wood, Jan-Willem van de Meent, Vikash Mansinghka
2015	J	`jnl`	Adaptive Scheduling in MCMC and Probabilistic Programming. CoRR David Tolpin, Jan-Willem van de Meent, Brooks Paige, Frank D. Wood
2015	J	`jnl`	Bayesian Nonparametric Methods for Partially-Observable Reinforcement Learning. IEEE Trans. Pattern Anal. Mach. Intell. Finale Doshi-Velez, David Pfau, Frank D. Wood, Nicholas Roy
2015	J	`jnl`	Black-Box Policy Search with Probabilistic Programs. CoRR Jan-Willem van de Meent, David Tolpin, Brooks Paige, Frank D. Wood
2015	J	`jnl`	Canonical Correlation Forests. CoRR Tom Rainforth, Frank D. Wood
2015	J	`jnl`	Data-driven Sequential Monte Carlo in Probabilistic Programming. CoRR Yura N. Perov, Tuan Anh Le, Frank D. Wood
2015	B	`conf`	Maximum a Posteriori Estimation by Search in Probabilistic Programs. SOCS David Tolpin, Frank D. Wood
2015	J	`jnl`	Maximum a Posteriori Estimation by Search in Probabilistic Programs. CoRR David Tolpin, Frank D. Wood
2015	—	`conf`	Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs. ECML/PKDD (2) David Tolpin, Jan-Willem van de Meent, Brooks Paige, Frank D. Wood
2015	A	`conf`	Particle Gibbs with Ancestor Sampling for Probabilistic Programs. AISTATS Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka, Frank D. Wood
2015	J	`jnl`	Particle Gibbs with Ancestor Sampling for Probabilistic Programs. CoRR Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka, Frank D. Wood
2015	J	`jnl`	Path Finding under Uncertainty through Probabilistic Inference. CoRR David Tolpin, Brooks Paige, Frank D. Wood
2015	—	`conf`	Probabilistic Programming in Anglican. ECML/PKDD (3) David Tolpin, Jan-Willem van de Meent, Frank D. Wood
2014	A*	`conf`	A Compilation Target for Probabilistic Programming Languages. ICML Brooks Paige, Frank D. Wood
2014	J	`jnl`	A Compilation Target for Probabilistic Programming Languages. CoRR Brooks Paige, Frank D. Wood
2014	A	`conf`	A New Approach to Probabilistic Programming Inference. AISTATS Frank D. Wood, Jan-Willem van de Meent, Vikash Mansinghka
2014	—	`conf`	Asynchronous Anytime Sequential Monte Carlo. NIPS Brooks Paige, Frank D. Wood, Arnaud Doucet, Yee Whye Teh
2014	J	`jnl`	Diagnosis code assignment: models and evaluation metrics. J. Am. Medical Informatics Assoc. Adler J. Perotte, Rimma Pivovarov, Karthik Natarajan, Nicole Gray Weiskopf, Frank D. Wood, Noemie Elhadad
2014	—	`conf`	Improved activity recognition via Kalman smoothing and multiclass linear discriminant analysis. EMBC Neil Dhir, Frank D. Wood
2014	J	`jnl`	Learning Probabilistic Programs. CoRR Yura N. Perov, Frank D. Wood
2014	—	`ch.`	Mixed Membership Classification for Documents with Hierarchically Structured Labels. Handbook of Mixed Membership Models and Their Applications Frank D. Wood, Adler J. Perotte
2014	A	`conf`	The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling. AISTATS Willie Neiswanger, Frank D. Wood, Eric P. Xing
2013	—	`conf`	Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data. ICML (2) Jan-Willem van de Meent, Jonathan E. Bronson, Frank D. Wood, Ruben L. Gonzalez, Chris Wiggins
2012	J	`jnl`	A Non-Parametric Bayesian Method for Inferring Hidden Causes. CoRR Frank D. Wood, Thomas L. Griffiths, Zoubin Ghahramani
2012	J	`jnl`	Inference in Hidden Markov Models with Explicit State Duration Distributions CoRR Michael Dewar, Chris Wiggins, Frank D. Wood
2012	J	`jnl`	Inference in Hidden Markov Models with Explicit State Duration Distributions. IEEE Signal Process. Lett. Michael Dewar, Chris Wiggins, Frank D. Wood
2012	A	`conf`	Low rank continuous-space graphical models. AISTATS Carl Smith, Frank D. Wood, Liam Paninski
2012	J	`jnl`	Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures CoRR Willie Neiswanger, Frank D. Wood
2011	B	`conf`	Deplump for Streaming Data. DCC Nicholas Bartlett, Frank D. Wood
2011	A	`conf`	Discussion of "The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling". AISTATS Frank D. Wood
2011	—	`conf`	Hierarchically Supervised Latent Dirichlet Allocation. NIPS Adler J. Perotte, Frank D. Wood, Noemie Elhadad, Nicholas Bartlett
2011	J	`jnl`	The sequence memoizer. Commun. ACM Frank D. Wood, Jan Gasthaus, Cédric Archambeau, Lancelot James, Yee Whye Teh
2010	A*	`conf`	Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process. ICML Nicholas Bartlett, David Pfau, Frank D. Wood
2010	B	`conf`	Lossless Compression Based on the Sequence Memoizer. DCC Jan Gasthaus, Frank D. Wood, Yee Whye Teh
2010	—	`conf`	Probabilistic Deterministic Infinite Automata. NIPS David Pfau, Nicholas Bartlett, Frank D. Wood
2009	A	`conf`	A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation. AISTATS Frank D. Wood, Yee Whye Teh
2009	A*	`conf`	A stochastic memoizer for sequence data. ICML Frank D. Wood, Cédric Archambeau, Jan Gasthaus, Lancelot James, Yee Whye Teh
2008	—	`conf`	Characterizing neural dependencies with copula models. NIPS Pietro Berkes, Frank D. Wood, Jonathan W. Pillow
2008	—	`conf`	Dependent Dirichlet Process Spike Sorting. NIPS Jan Gasthaus, Frank D. Wood, Dilan Görür, Yee Whye Teh
2007	—	`—`	Nonparametric Bayesian Models for Neural Data. Frank D. Wood
2006	A	`conf`	A Non-Parametric Bayesian Method for Inferring Hidden Causes. UAI Frank D. Wood, Thomas L. Griffiths, Zoubin Ghahramani
2006	J	`jnl`	Discovering natural kinds of robot sensory experiences in unstructured environments. J. Field Robotics Daniel H. Grollman, Odest Chadwicke Jenkins, Frank D. Wood
2006	—	`conf`	Particle Filtering for Nonparametric Bayesian Matrix Factorization. NIPS Frank D. Wood, Thomas L. Griffiths
2005	—	`conf`	Modeling Neural Population Spiking Activity with Gibbs Distributions. NIPS Frank D. Wood, Stefan Roth, Michael J. Black
2004	J	`jnl`	On the variability of manual spike sorting. IEEE Trans. Biomed. Eng. Frank D. Wood, Michael J. Black, Carlos Vargas-Irwin, Matthew Fellows, John P. Donoghue
1996	J	`jnl`	WorkSpace and the study of Chagas' disease. IEEE Computer Graphics and Applications Frank D. Wood, Daniel Brown, Robert A. Amidon, Jonathan Alferness, Brian Joseph, Richard E. Gillilan, Carlos Faerman