Wood, Frank

63 publications

AAAI 2025 Constrained Generative Modeling with Manually Bridged Diffusion Models Saeid Naderiparizi, Xiaoxuan Liang, Berend Zwartsenberg, Frank Wood

TMLR 2025 Daphne: Multi-Pass Compilation of Probabilistic Programs into Graphical Models and Neural Networks Christian Dietrich Weilbach, Frank Wood

TMLR 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, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric 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

ICML 2025 Towards a Mechanistic Explanation of Diffusion Model Generalization Matthew Niedoba, Berend Zwartsenberg, Kevin Patrick Murphy, Frank Wood

ICML 2024 All-in-One Simulation-Based Inference Manuel Gloeckler, 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, Setareh Cohan, Berend Zwartsenberg, Frank Wood

ICML 2024 Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning Jinsoo Yoo, Yunpeng Liu, Frank Wood, Geoff Pleiss

ICML 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

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

TMLR 2023 Conditional Permutation Invariant Flows Berend Zwartsenberg, Adam Scibior, Matthew Niedoba, Vasileios Lioutas, Justice Sefas, Yunpeng Liu, Setareh Dabiri, Jonathan Wilder Lavington, Trevor Campbell, Frank Wood

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

ICML 2023 Graphically Structured Diffusion Models Christian Dietrich Weilbach, William Harvey, Frank Wood

ICMLW 2023 Scaling Graphically Structured Diffusion Models Christian Dietrich Weilbach, William Harvey, Hamed Shirzad, Frank Wood

ICML 2023 Uncertain Evidence in Probabilistic Models and Stochastic Simulators Andreas Munk, Alexander Mead, Frank Wood

ICMLW 2023 Visual Chain-of-Thought Diffusion Models William Harvey, Frank Wood

AISTATS 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. Schroeder De Witt, Robert Zinkov, Philip Torr, Tom Rainforth, Yee Whye Teh, Frank Wood

NeurIPS 2022 BayesPCN: A Continually Learnable Predictive Coding Associative Memory Jinsoo Yoo, Frank Wood

ICLR 2022 Conditional Image Generation by Conditioning Variational Auto-Encoders William Harvey, Saeid Naderiparizi, Frank Wood

WACV 2022 Enhancing Few-Shot Image Classification with Unlabelled Examples Peyman Bateni, Jarred Barber, Jan-Willem van de Meent, Frank Wood

NeurIPS 2022 Flexible Diffusion Modeling of Long Videos William Harvey, Saeid Naderiparizi, Vaden Masrani, Christian Weilbach, Frank Wood

UAI 2022 Probabilistic Surrogate Networks for Simulators with Unbounded Randomness Andreas Munk, Berend Zwartsenberg, Adam Ścibior, Atılım Güneş G. Baydin, Andrew Stewart, Goran Fernlund, Anoush Poursartip, Frank Wood

TMLR 2022 TITRATED: Learned Human Driving Behavior Without Infractions via Amortized Inference Vasileios Lioutas, Adam Scibior, Frank Wood

NeurIPSW 2021 A Closer Look at Gradient Estimators with Reinforcement Learning as Inference Jonathan Wilder Lavington, Michael Teng, Mark Schmidt, Frank Wood

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

ICML 2021 Robust Asymmetric Learning in POMDPs Andrew Warrington, Jonathan W Lavington, Adam Scibior, Mark Schmidt, Frank Wood

UAI 2021 Sequential Core-Set Monte Carlo Boyan Beronov, Christian Weilbach, Frank Wood, Trevor Campbell

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

NeurIPSW 2020 Annealed Importance Sampling with Q-Paths Rob Brekelmans, Vaden Masrani, Thang D Bui, Frank Wood, Aram Galstyan, Greg Ver Steeg, Frank Nielsen

AISTATS 2020 Coping with Simulators That Don’t Always Return Andrew Warrington, Saeid Naderiparizi, Frank Wood

NeurIPS 2020 Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective Vu Nguyen, Vaden Masrani, Rob Brekelmans, Michael Osborne, Frank Wood

UAI 2020 Semi-Supervised Sequential Generative Models Michael Teng, Tuan Anh Le, Adam Scibior, Frank Wood

AISTATS 2020 Structured Conditional Continuous Normalizing Flows for Efficient Amortized Inference in Graphical Models Christian Weilbach, Boyan Beronov, Frank Wood, William Harvey

JMLR 2020 Target–Aware Bayesian Inference: How to Beat Optimal Conventional Estimators Tom Rainforth, Adam Golinski, Frank Wood, Sheheryar Zaidi

ICML 2019 Amortized Monte Carlo Integration Adam Golinski, Frank Wood, Tom Rainforth

NeurIPS 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 Torr, Victor Lee, Kyle Cranmer, Mr. Prabhat, Frank Wood

AISTATS 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

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

NeurIPS 2019 The Thermodynamic Variational Objective Vaden Masrani, Tuan Anh Le, Frank Wood

NeurIPSW 2019 The Virtual Patch Clamp: Imputing C. Elegans Membrane Potentials from Calcium Imaging Andrew Warrington, Arthur Spencer, Frank Wood

ICLR 2018 Auto-Encoding Sequential Monte Carlo Tuan Anh Le, Maximilian Igl, Tom Rainforth, Tom Jin, Frank Wood

NeurIPS 2018 Bayesian Distributed Stochastic Gradient Descent Michael Teng, Frank Wood

ICML 2018 Deep Variational Reinforcement Learning for POMDPs Maximilian Igl, Luisa Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson

NeurIPS 2018 Faithful Inversion of Generative Models for Effective Amortized Inference Stefan Webb, Adam Golinski, Rob Zinkov, Siddharth N, Tom Rainforth, Yee Whye Teh, Frank Wood

ICML 2018 On Nesting Monte Carlo Estimators Tom Rainforth, Rob Cornish, Hongseok Yang, Andrew Warrington, Frank Wood

ICLR 2018 Online Learning Rate Adaptation with Hypergradient Descent Atilim Gunes Baydin, Robert Cornish, David Martinez Rubio, Mark Schmidt, Frank Wood

ICML 2018 Tighter Variational Bounds Are Not Necessarily Better Tom Rainforth, Adam Kosiorek, Tuan Anh Le, Chris Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh

JMLR 2017 Generalized P{\'o}lya Urn for Time-Varying Pitman-Yor Processes François Caron, Willie Neiswanger, Frank Wood, Arnaud Doucet, Manuel Davy

NeurIPS 2017 Learning Disentangled Representations with Semi-Supervised Deep Generative Models Siddharth N, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Noah Goodman, Pushmeet Kohli, Frank Wood, Philip Torr

NeurIPS 2016 Bayesian Optimization for Probabilistic Programs Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A Osborne, Frank Wood

ICML 2016 Inference Networks for Sequential Monte Carlo in Graphical Models Brooks Paige, Frank Wood

ICML 2016 Interacting Particle Markov Chain Monte Carlo Tom Rainforth, Christian Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem Vandemeent, Arnaud Doucet, Frank Wood

ICML 2014 A Compilation Target for Probabilistic Programming Languages Brooks Paige, Frank Wood

NeurIPS 2014 Asynchronous Anytime Sequential Monte Carlo Brooks Paige, Frank Wood, Arnaud Doucet, Yee Whye Teh

ICML 2013 Hierarchically-Coupled Hidden Markov Models for Learning Kinetic Rates from Single-Molecule Data Jan-Willem Meent, Jonathan Bronson, Frank Wood, Ruben Gonzalez Jr., Chris Wiggins

AISTATS 2012 Low Rank Continuous-Space Graphical Models Carl Smith, Frank Wood, Liam Paninski

AISTATS 2011 Discussion of “The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling” Frank Wood

NeurIPS 2011 Hierarchically Supervised Latent Dirichlet Allocation Adler J. Perotte, Frank Wood, Noemie Elhadad, Nicholas Bartlett

NeurIPS 2010 Probabilistic Deterministic Infinite Automata David Pfau, Nicholas Bartlett, Frank Wood

AISTATS 2009 A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation Frank Wood, Yee Whye Teh

NeurIPS 2008 Characterizing Neural Dependencies with Copula Models Pietro Berkes, Frank Wood, Jonathan W. Pillow

NeurIPS 2008 Dependent Dirichlet Process Spike Sorting Jan Gasthaus, Frank Wood, Dilan Gorur, Yee W. Teh

NeurIPS 2006 Particle Filtering for Nonparametric Bayesian Matrix Factorization Frank Wood, Thomas L. Griffiths

NeurIPS 2005 Modeling Neural Population Spiking Activity with Gibbs Distributions Frank Wood, Stefan Roth, Michael J. Black