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)

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)

Modeling Neural Population Spiking Activity with Gibbs Distributions. - Frank D. Wood; Stefan Roth 001; Michael J. Black (NIPS, 2005)

A Non-Parametric Bayesian Approach to Spike Sorting. - Frank Wood; Sharon Goldwater; Michael J. Black (EMBC, 2006)

Particle Filtering for Nonparametric Bayesian Matrix Factorization. - Frank D. Wood; Thomas L. Griffiths 001 (NIPS, 2006)

A Non-Parametric Bayesian Method for Inferring Hidden Causes. - Frank D. Wood; Thomas L. Griffiths 001; Zoubin Ghahramani (UAI, 2006)

Discovering natural kinds of robot sensory experiences in unstructured environments. - Daniel H. Grollman; Odest Chadwicke Jenkins; Frank D. Wood (J. Field Robotics, 2006)

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)

Nonparametric Bayesian Models for Neural Data. - Frank D. Wood (2007)

Characterizing neural dependencies with copula models. - Pietro Berkes; Frank D. Wood; Jonathan W. Pillow (NIPS, 2008)

Dependent Dirichlet Process Spike Sorting. - Jan Gasthaus; Frank D. Wood; Dilan Görür; Yee Whye Teh (NIPS, 2008)

A stochastic memoizer for sequence data. - Frank D. Wood; Cédric Archambeau; Jan Gasthaus; Lancelot James; Yee Whye Teh (ICML, 2009)

A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation. - Frank D. Wood; Yee Whye Teh (AISTATS, 2009)

Lossless Compression Based on the Sequence Memoizer. - Jan Gasthaus; Frank D. Wood; Yee Whye Teh (DCC, 2010)

Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process. - Nicholas Bartlett; David Pfau; Frank D. Wood (ICML, 2010)

Probabilistic Deterministic Infinite Automata. - David Pfau; Nicholas Bartlett; Frank D. Wood (NIPS, 2010)

Deplump for Streaming Data. - Nicholas Bartlett; Frank D. Wood (DCC, 2011)

Hierarchically Supervised Latent Dirichlet Allocation. - Adler J. Perotte; Frank D. Wood; Noemie Elhadad; Nicholas Bartlett (NIPS, 2011)

The sequence memoizer. - Frank D. Wood; Jan Gasthaus; Cédric Archambeau; Lancelot James; Yee Whye Teh (Commun. ACM, 2011)

Discussion of "The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling". - Frank D. Wood (AISTATS, 2011)

Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures - Willie Neiswanger; Frank D. Wood (CoRR, 2012)

Low rank continuous-space graphical models. - Carl Smith; Frank D. Wood; Liam Paninski (AISTATS, 2012)

Inference in Hidden Markov Models with Explicit State Duration Distributions. - Michael Dewar; Chris Wiggins 001; Frank D. Wood (IEEE Signal Process. Lett., 2012)

A Joint Learning Model of Word Segmentation, Lexical Acquisition, and Phonetic Variability. - Micha Elsner; Sharon Goldwater; Naomi Feldman; Frank Wood (EMNLP, 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 001 (ICML (2), 2013)

The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling. - Willie Neiswanger; Frank D. Wood; Eric P. Xing (AISTATS, 2014)

A New Approach to Probabilistic Programming Inference. - Frank D. Wood; Jan-Willem van de Meent; Vikash Mansinghka 001 (AISTATS, 2014)

Improved activity recognition via Kalman smoothing and multiclass linear discriminant analysis. - Neil Dhir; Frank D. Wood (EMBC, 2014)

A Compilation Target for Probabilistic Programming Languages. - Brooks Paige; Frank D. Wood (ICML, 2014)

Asynchronous Anytime Sequential Monte Carlo. - Brooks Paige; Frank D. Wood; Arnaud Doucet; Yee Whye Teh (NIPS, 2014)

Learning Probabilistic Programs. - Yura N. Perov; Frank D. Wood (CoRR, 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)

Mixed Membership Classification for Documents with Hierarchically Structured Labels. - Frank D. Wood; Adler J. Perotte (Handbook of Mixed Membership Models and Their Applications, 2014)

Particle Gibbs with Ancestor Sampling for Probabilistic Programs. - Jan-Willem van de Meent; Hongseok Yang; Vikash Mansinghka 001; Frank D. Wood (AISTATS, 2015)

Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs. - David Tolpin; Jan-Willem van de Meent; Brooks Paige; Frank D. Wood (ECML/PKDD (2), 2015)

Probabilistic Programming in Anglican. - David Tolpin; Jan-Willem van de Meent; Frank D. Wood (ECML/PKDD (3), 2015)

Maximum a Posteriori Estimation by Search in Probabilistic Programs. - David Tolpin; Frank D. Wood (SOCS, 2015)

Data-driven Sequential Monte Carlo in Probabilistic Programming. - Yura N. Perov; Tuan Anh Le 001; Frank D. Wood (CoRR, 2015)

Canonical Correlation Forests. - Tom Rainforth; 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)

Path Finding under Uncertainty through Probabilistic Inference. - David Tolpin; Brooks Paige; Frank D. Wood (CoRR, 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)

Automatic Sampler Discovery via Probabilistic Programming and Approximate Bayesian Computation. - Yura N. Perov; Frank D. Wood (AGI, 2016)

Black-Box Policy Search with Probabilistic Programs. - Jan-Willem van de Meent; Brooks Paige; David Tolpin; Frank D. Wood (AISTATS, 2016)

Inference Networks for Sequential Monte Carlo in Graphical Models. - Brooks Paige; Frank D. Wood (ICML, 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 (ICML, 2016)

Design and Implementation of Probabilistic Programming Language Anglican. - David Tolpin; Jan-Willem van de Meent; Hongseok Yang; Frank D. Wood (IFL, 2016)

Nonparametric Bayesian models for unsupervised activity recognition and tracking. - Neil Dhir; Yura N. Perov; Frank D. Wood (IROS, 2016)

Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints. - Sam Staton; Hongseok Yang; Frank D. Wood; Chris Heunen; Ohad Kammar (LICS, 2016)

Bayesian Optimization for Probabilistic Programs. - Tom Rainforth; Tuan Anh Le 001; Jan-Willem van de Meent; Michael A. Osborne; Frank D. Wood (NIPS, 2016)

Super-Sampling with a Reservoir. - Brooks Paige; Dino Sejdinovic; Frank D. Wood (UAI, 2016)

Probabilistic structure discovery in time series data. - David Janz; Brooks Paige; Tom Rainforth; Jan-Willem van de Meent; Frank D. Wood (CoRR, 2016)

Inducing Interpretable Representations with Variational Autoencoders. - N. Siddharth 001; Brooks Paige; Alban Desmaison; Jan-Willem van de Meent; Frank D. Wood; Noah D. Goodman; Pushmeet Kohli; Philip H. S. Torr (CoRR, 2016)

Spreadsheet Probabilistic Programming. - Mike Wu; Yura N. Perov; Frank D. Wood; Hongseok Yang (CoRR, 2016)

Inference Compilation and Universal Probabilistic Programming. - Tuan Anh Le 001; Atilim Gunes Baydin; Frank D. Wood (AISTATS, 2017)

Using synthetic data to train neural networks is model-based reasoning. - Tuan Anh Le 001; Atilim Günes Baydin; Robert Zinkov; Frank D. Wood (IJCNN, 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 (NIPS, 2017)

Interpreting Lion Behaviour as Probabilistic Programs. - Neil Dhir; Matthijs Vákár; Matthew Wijers; Andrew Markham; Frank D. Wood (UAI, 2017)

Updating the VESICLE-CNN Synapse Detector. - Andrew Warrington; Frank D. Wood (CoRR, 2017)

Faithful Model Inversion Substantially Improves Auto-encoding Variational Inference. - Stefan Webb; Adam Golinski; Robert Zinkov; N. Siddharth 001; Yee Whye Teh; Frank D. Wood (CoRR, 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 001; Frank D. Wood; Lukas Heinrich; Gilles Louppe; Kyle Cranmer; Karen Ng; Wahid Bhimji; Prabhat (CoRR, 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)

Online Learning Rate Adaptation with Hypergradient Descent. - Atilim Gunes Baydin; Robert Cornish; David Martínez-Rubio; Mark Schmidt 001; Frank Wood (ICLR (Poster), 2018)

Auto-Encoding Sequential Monte Carlo. - Tuan Anh Le 001; Maximilian Igl; Tom Rainforth; Tom Jin; Frank Wood (ICLR (Poster), 2018)

Deep Variational Reinforcement Learning for POMDPs. - Maximilian Igl; Luisa M. Zintgraf; Tuan Anh Le 001; Frank Wood; Shimon Whiteson (ICML, 2018)

Tighter Variational Bounds are Not Necessarily Better. - Tom Rainforth; Adam R. Kosiorek; Tuan Anh Le 001; Chris J. Maddison; Maximilian Igl; Frank Wood; Yee Whye Teh (ICML, 2018)

Bayesian Distributed Stochastic Gradient Descent. - Michael Teng; Frank Wood (NeurIPS, 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 (NeurIPS, 2018)

High Throughput Synchronous Distributed Stochastic Gradient Descent. - Michael Teng; Frank Wood (CoRR, 2018)

Discontinuous Hamiltonian Monte Carlo for Probabilistic Programs. - Bradley Gram-Hansen; Yuan Zhou 013; Tobias Kohn; Hongseok Yang; Frank D. Wood (CoRR, 2018)

Revisiting Reweighted Wake-Sleep. - Tuan Anh Le 001; Adam R. Kosiorek; N. Siddharth 001; Yee Whye Teh; 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)

An Introduction to Probabilistic Programming. - Jan-Willem van de Meent; Brooks Paige; Hongseok Yang; Frank Wood (CoRR, 2018)

LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models. - Yuan Zhou 013; Bradley J. Gram-Hansen; Tobias Kohn; Tom Rainforth; Hongseok Yang; Frank Wood (AISTATS, 2019)

Amortized Monte Carlo Integration. - Adam Golinski; Frank Wood; Tom Rainforth (ICML, 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 001; Jialin Liu 002; Bradley Gram-Hansen; Gilles Louppe; Lawrence Meadows; Philip H. S. Torr; Victor W. Lee; et al. (NeurIPS, 2019)

The Thermodynamic Variational Objective. - Vaden Masrani; Tuan Anh Le 001; Frank Wood (NeurIPS, 2019)

Etalumis: bringing probabilistic programming to scientific simulators at scale. - Atilim Günes Baydin; Lei Shao; Wahid Bhimji; Lukas Heinrich; Lawrence Meadows; Jialin Liu 002; Andreas Munk 001; Saeid Naderiparizi; Bradley Gram-Hansen; Gilles Louppe; Mingfei Ma; Xiaohui Zhao; et al. (SC, 2019)

Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow. - Tuan Anh Le 001; Adam R. Kosiorek; N. Siddharth 001; Yee Whye Teh; Frank Wood (UAI, 2019)

The Virtual Patch Clamp: Imputing C. elegans Membrane Potentials from Calcium Imaging. - Andrew Warrington; Arthur P. C. Spencer; Frank Wood (CoRR, 2019)

Deep Probabilistic Surrogate Networks for Universal Simulator Approximation. - Andreas Munk 001; Adam Scibior; Atilim Günes Baydin; Andrew Stewart; Goran Fernlund; Anoush Poursartip; Frank Wood (CoRR, 2019)

Coping With Simulators That Don't Always Return. - Andrew Warrington; Frank Wood; Saeid Naderiparizi (AISTATS, 2020)

Structured Conditional Continuous Normalizing Flows for Efficient Amortized Inference in Graphical Models. - Christian Weilbach; Boyan Beronov; Frank Wood; William Harvey 002 (AISTATS, 2020)

Improved Few-Shot Visual Classification. - Peyman Bateni; Raghav Goyal; Vaden Masrani; Frank Wood; Leonid Sigal (CVPR, 2020)

All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference. - Rob Brekelmans; Vaden Masrani; Frank Wood; Greg Ver Steeg; Aram Galstyan (ICML, 2020)

Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective. - Vu Nguyen; Vaden Masrani; Rob Brekelmans; Michael A. Osborne; Frank Wood (NeurIPS, 2020)

Semi-supervised Sequential Generative Models. - Michael Teng; Tuan Anh Le 001; Adam Scibior; Frank Wood (UAI, 2020)

Planning as Inference in Epidemiological Models. - Frank Wood; Andrew Warrington; Saeid Naderiparizi; Christian Weilbach; Vaden Masrani; William Harvey 002; Adam Scibior; Boyan Beronov; Seyed Ali Nasseri (CoRR, 2020)

Improving Few-Shot Visual Classification with Unlabelled Examples. - Peyman Bateni; Jarred Barber; Jan-Willem van de Meent; Frank Wood (CoRR, 2020)

Uncertainty in Neural Processes. - Saeid Naderiparizi; Kenny Chiu; Benjamin Bloem-Reddy; 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)

Ensemble Squared: A Meta AutoML System. - Jason Yoo; Tony Joseph; Dylan Yung; Seyed Ali Nasseri; Frank Wood (CoRR, 2020)

Annealed Importance Sampling with q-Paths. - Rob Brekelmans; Vaden Masrani; Thang Bui; Frank Wood; Aram Galstyan; Greg Ver Steeg; Frank Nielsen (CoRR, 2020)

Target-Aware Bayesian Inference: How to Beat Optimal Conventional Estimators. - Tom Rainforth; Adam Golinski; Frank Wood; Sheheryar Zaidi (J. Mach. Learn. Res., 2020)

Robust Asymmetric Learning in POMDPs. - Andrew Warrington; Jonathan Wilder Lavington; Adam Scibior; Mark Schmidt 001; Frank Wood (ICML, 2021)

Assisting the Adversary to Improve GAN Training. - Andreas Munk 001; William Harvey 002; Frank Wood (IJCNN, 2021)

Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation. - Adam Scibior; Vasileios Lioutas; Daniele Reda; Peyman Bateni; Frank Wood (ITSC, 2021)

Sequential core-set Monte Carlo. - Boyan Beronov; Christian Weilbach; Frank Wood; Trevor Campbell (UAI, 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 (UAI, 2021)

Image Completion via Inference in Deep Generative Models. - William Harvey 002; Saeid Naderiparizi; Frank Wood (CoRR, 2021)

Differentiable Particle Filtering without Modifying the Forward Pass. - Adam Scibior; Vaden Masrani; Frank Wood (CoRR, 2021)

Planning as Inference in Epidemiological Dynamics Models. - Frank Wood; Andrew Warrington; Saeid Naderiparizi; Christian Weilbach; Vaden Masrani; William Harvey 002; Adam Scibior; Boyan Beronov; John Grefenstette; Duncan Campbell; Seyed Ali Nasseri (Frontiers Artif. Intell., 2021)

Probabilistic Programming Languages: Independent Choices and Deterministic Systems. - David Poole 001; Frank Wood (Probabilistic and Causal Inference, 2022)

Amortized Rejection Sampling in Universal Probabilistic Programming. - Saeid Naderiparizi; Adam Scibior; Andreas Munk 001; 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 (AISTATS, 2022)

Conditional Image Generation by Conditioning Variational Auto-Encoders. - William Harvey 002; Saeid Naderiparizi; Frank Wood (ICLR, 2022)

Near-Optimal Glimpse Sequences for Improved Hard Attention Neural Network Training. - William Harvey 002; Michael Teng; Frank Wood (IJCNN, 2022)

Vehicle Type Specific Waypoint Generation. - Yunpeng Liu 007; Jonathan Wilder Lavington; Adam Scibior; Frank Wood (IROS, 2022)

Flexible Diffusion Modeling of Long Videos. - William Harvey 002; Saeid Naderiparizi; Vaden Masrani; Christian Weilbach; Frank Wood (NeurIPS, 2022)

BayesPCN: A Continually Learnable Predictive Coding Associative Memory. - Jinsoo Yoo; Frank Wood (NeurIPS, 2022)

Attention for Inference Compilation. - William Harvey 002; Andreas Munk 001; Atilim Günes Baydin; Alexander Bergholm; Frank Wood (SIMULTECH, 2022)

Probabilistic surrogate networks for simulators with unbounded randomness. - Andreas Munk 001; Berend Zwartsenberg; Adam Scibior; Atilim Günes Baydin; Andrew Stewart; Goran Fernlund; Anoush Poursartip; Frank Wood (UAI, 2022)

Enhancing Few-Shot Image Classification with Unlabelled Examples. - Peyman Bateni; Jarred Barber; Jan-Willem van de Meent; Frank Wood (WACV, 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)

Exploration with Multi-Sample Target Values for Distributional Reinforcement Learning. - Michael Teng; Michiel van de Panne; Frank Wood (CoRR, 2022)

Gradients without Backpropagation. - Atilim Günes Baydin; Barak A. Pearlmutter; Don Syme; Frank Wood; Philip H. S. Torr (CoRR, 2022)

BayesPCN: A Continually Learnable Predictive Coding Associative Memory. - Jason Yoo; Frank Wood (CoRR, 2022)

TITRATED: Learned Human Driving Behavior without Infractions via Amortized Inference. - Vasileios Lioutas; Adam Scibior; Frank Wood (Trans. Mach. Learn. Res., 2022)

Critic Sequential Monte Carlo. - Vasileios Lioutas; Jonathan Wilder Lavington; Justice Sefas; Matthew Niedoba; Yunpeng Liu 007; Berend Zwartsenberg; Setareh Dabiri; Frank Wood; Adam Scibior (ICLR, 2023)

Uncertain Evidence in Probabilistic Models and Stochastic Simulators. - Andreas Munk 001; Alexander Mead; Frank Wood (ICML, 2023)

Graphically Structured Diffusion Models. - Christian Dietrich Weilbach; William Harvey 002; Frank Wood (ICML, 2023)

Video Killed the HD-Map: Predicting Multi-Agent Behavior Directly From Aerial Images. - Yunpeng Liu 007; Vasileios Lioutas; Jonathan Wilder Lavington; Matthew Niedoba; Justice Sefas; Setareh Dabiri; Dylan Green; Xiaoxuan Liang 001; Berend Zwartsenberg; Adam Scibior; Frank Wood (ITSC, 2023)

A Diffusion-Model of Joint Interactive Navigation. - Matthew Niedoba; Jonathan Wilder Lavington; Yunpeng Liu 007; Vasileios Lioutas; Justice Sefas; Xiaoxuan Liang 001; Dylan Green; Setareh Dabiri; Berend Zwartsenberg; Adam Scibior; Frank Wood (NeurIPS, 2023)

Visual Chain-of-Thought Diffusion Models. - William Harvey 002; Frank Wood (CoRR, 2023)

Video Killed the HD-Map: Predicting Driving Behavior Directly From Drone Images. - Yunpeng Liu 007; Vasileios Lioutas; Jonathan Wilder Lavington; Matthew Niedoba; Justice Sefas; Setareh Dabiri; Dylan Green; Xiaoxuan Liang 001; Berend Zwartsenberg; Adam Scibior; 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 007; Matthew Niedoba; Xiaoxuan Liang 001; Dylan Green; Justice Sefas; Jonathan Wilder Lavington; Frank Wood; Adam Scibior (CoRR, 2023)

Don't be so negative! Score-based Generative Modeling with Oracle-assisted Guidance. - Saeid Naderiparizi; Xiaoxuan Liang 001; Berend Zwartsenberg; Frank Wood (CoRR, 2023)

Conditional Permutation Invariant Flows. - Berend Zwartsenberg; Adam Scibior; Matthew Niedoba; Vasileios Lioutas; Justice Sefas; Yunpeng Liu 007; Setareh Dabiri; Jonathan Wilder Lavington; Trevor Campbell; Frank Wood (Trans. Mach. Learn. Res., 2023)

On Neural Process Pooling Operator Choice and Posterior Calibration. - Saeid Naderiparizi; Kenny Chiu; Benjamin Bloem-Reddy; Frank Wood (Canadian AI, 2024)

All-in-one simulation-based inference. - Manuel Glöckler; Michael Deistler; Christian Dietrich Weilbach; Frank Wood; Jakob H. Macke (ICML, 2024)

Don't be so Negative! Score-based Generative Modeling with Oracle-assisted Guidance. - Saeid Naderiparizi; Xiaoxuan Liang 001; Setareh Cohan; Berend Zwartsenberg; Frank Wood (ICML, 2024)

Nearest Neighbour Score Estimators for Diffusion Generative Models. - Matthew Niedoba; Dylan Green; Saeid Naderiparizi; Vasileios Lioutas; Jonathan Wilder Lavington; Xiaoxuan Liang 001; Yunpeng Liu 007; Ke Zhang; Setareh Dabiri; Adam Scibior; Berend Zwartsenberg; Frank Wood (ICML, 2024)

Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning. - Jinsoo Yoo; Yunpeng Liu 007; Frank Wood; Geoff Pleiss (ICML, 2024)

Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning. - Jason Yoo; Yunpeng Liu 007; Frank Wood; Geoff Pleiss (CoRR, 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 004; Matthias Kirchler; Marius Kloft; Yingzhen Li; Christoph Lippert; et al. (CoRR, 2024)

Semantically Consistent Video Inpainting with Conditional Diffusion Models. - Dylan Green; William Harvey 002; Saeid Naderiparizi; Matthew Niedoba; Yunpeng Liu 007; Xiaoxuan Liang 001; Jonathan Wilder Lavington; Ke Zhang; Vasileios Lioutas; Setareh Dabiri; Adam Scibior; Berend Zwartsenberg; et al. (CoRR, 2024)

TorchDriveEnv: A Reinforcement Learning Benchmark for Autonomous Driving with Reactive, Realistic, and Diverse Non-Playable Characters. - Jonathan Wilder Lavington; Ke Zhang; Vasileios Lioutas; Matthew Niedoba; Yunpeng Liu 007; Dylan Green; Saeid Naderiparizi; Xiaoxuan Liang 001; Setareh Dabiri; Adam Scibior; Berend Zwartsenberg; Frank Wood (CoRR, 2024)

Online Continual Learning of Video Diffusion Models From a Single Video Stream. - Jason Yoo; Dylan Green; Geoff Pleiss; Frank Wood (CoRR, 2024)

Prospective Messaging: Learning in Networks with Communication Delays. - Ryan Fayyazi; Christian Weilbach; Frank Wood (CoRR, 2024)

Towards a Mechanistic Explanation of Diffusion Model Generalization. - Matthew Niedoba; Berend Zwartsenberg; Kevin Murphy; Frank Wood (CoRR, 2024)

Constrained Generative Modeling with Manually Bridged Diffusion Models. - Saeid Naderiparizi; Xiaoxuan Liang 001; Berend Zwartsenberg; Frank Wood (AAAI, 2025)

On the Challenges and Opportunities in Generative AI. - Laura Manduchi; Clara Meister; Kushagra Pandey; Robert Bamler; Ryan Cotterell; Sina Däubener; Sophie Fellenz; Asja Fischer; Thomas Gärtner 004; Matthias Kirchler; Marius Kloft; Yingzhen Li; et al. (Trans. Mach. Learn. Res., 2025)

Daphne: Multi-Pass Compilation of Probabilistic Programs into Graphical Models and Neural Networks. - Christian Dietrich Weilbach; Frank Wood (Trans. Mach. Learn. Res., 2025)

Control-ITRA: Controlling the Behavior of a Driving Model. - Vasileios Lioutas; Adam Scibior; Matthew Niedoba; Berend Zwartsenberg; Frank Wood (CoRR, 2025)

Rolling Ahead Diffusion for Traffic Scene Simulation. - Yunpeng (Larry) Liu; Matthew Niedoba; William Harvey 002; Adam Scibior; Berend Zwartsenberg; Frank Wood (CoRR, 2025)

PLAICraft: Large-Scale Time-Aligned Vision-Speech-Action Dataset for Embodied AI. - Yingchen He; Christian D. Weilbach; Martyna E. Wojciechowska; Yuxuan Zhang; Frank Wood (CoRR, 2025)