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)

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

Single-molecule dataset (SMD): a generalized storage format for raw and processed single-molecule data. - Max Greenfeld; Jan-Willem van de Meent; Dmitri S. Pavlichin; Hideo Mabuchi; Chris H. Wiggins 001; Ruben L. Gonzalez; Daniel Herschlag (BMC Bioinform., 2015)

Adaptive Scheduling in MCMC and Probabilistic Programming. - David Tolpin; Jan-Willem van de Meent; Brooks Paige; Frank D. Wood (CoRR, 2015)

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

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

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)

Learning Disentangled Representations of Texts with Application to Biomedical Abstracts. - Sarthak Jain; Edward Banner; Jan-Willem van de Meent; Iain James Marshall; Byron C. Wallace (EMNLP, 2018)

Hierarchical Disentangled Representations. - Babak Esmaeili 001; Hao Wu 020; Sarthak Jain; N. Siddharth 001; Brooks Paige; Jan-Willem van de Meent (CoRR, 2018)

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

On Exploration, Exploitation and Learning in Adaptive Importance Sampling. - Xiaoyu Lu; Tom Rainforth; Yuan Zhou 013; Jan-Willem van de Meent; Yee Whye Teh (CoRR, 2018)

Composing Modeling and Inference Operations with Probabilistic Program Combinators. - Eli Sennesh; Adam Scibior; Hao Wu 020; Jan-Willem van de Meent (CoRR, 2018)

Modeling Theory of Mind for Autonomous Agents with Probabilistic Programs. - Iris Rubi Seaman; Jan-Willem van de Meent; David Wingate (CoRR, 2018)

Structured Representations for Reviews: Aspect-Based Variational Hidden Factor Models. - Babak Esmaeili 001; Hongyi Huang; Byron C. Wallace; Jan-Willem van de Meent (CoRR, 2018)

Can VAEs Generate Novel Examples? - Alican Bozkurt; Babak Esmaeili 001; Dana H. Brooks; Jennifer G. Dy; Jan-Willem van de Meent (CoRR, 2018)

Structured Disentangled Representations. - Babak Esmaeili 001; Hao Wu 020; Sarthak Jain; Alican Bozkurt; N. Siddharth 001; Brooks Paige; Dana H. Brooks; Jennifer G. Dy; Jan-Willem van de Meent (AISTATS, 2019)

Structured Neural Topic Models for Reviews. - Babak Esmaeili 001; Hongyi Huang; Byron C. Wallace; Jan-Willem van de Meent (AISTATS, 2019)

Neural Topographic Factor Analysis for fMRI Data. - Eli Sennesh; Zulqarnain Khan; Jennifer G. Dy; Ajay B. Satpute; J. Benjamin Hutchinson; Jan-Willem van de Meent (CoRR, 2019)

Evaluating Combinatorial Generalization in Variational Autoencoders. - Alican Bozkurt; Babak Esmaeili 001; Dana H. Brooks; Jennifer G. Dy; Jan-Willem van de Meent (CoRR, 2019)

Amortized Population Gibbs Samplers with Neural Sufficient Statistics. - Hao Wu 020; Heiko Zimmermann; Eli Sennesh; Tuan Anh Le 001; Jan-Willem van de Meent (ICML, 2020)

Query-Focused EHR Summarization to Aid Imaging Diagnosis. - Denis Jered McInerney; Borna Dabiri; Anne-Sophie Touret; Geoffrey S. Young; Jan-Willem van de Meent; Byron C. Wallace (MLHC, 2020)

Neural Topographic Factor Analysis for fMRI Data. - Eli Sennesh; Zulqarnain Khan; Yiyu Wang; J. Benjamin Hutchinson; Ajay B. Satpute; Jennifer G. Dy; Jan-Willem van de Meent (NeurIPS, 2020)

Learning discrete state abstractions with deep variational inference. - Ondrej Biza; Robert Platt Jr. 001; Jan-Willem van de Meent; Lawson L. S. Wong (CoRR, 2020)

Deep Markov Spatio-Temporal Factorization. - Amirreza Farnoosh; Behnaz Rezaei; Eli Zachary Sennesh; Zulqarnain Khan; Jennifer G. Dy; Ajay B. Satpute; J. Benjamin Hutchinson; Jan-Willem van de Meent; Sarah Ostadabbas (CoRR, 2020)

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

Guiding Generative Graph Grammars of Dungeon Mission Graphs via Examples. - Abdelrahman Madkour; Stacy Marsella; Casper Harteveld; Magy Seif El-Nasr; Jan-Willem van de Meent (AIIDE Workshops, 2021)

Rate-Regularization and Generalization in Variational Autoencoders. - Alican Bozkurt; Babak Esmaeili 001; Jean-Baptiste Tristan; Dana H. Brooks; Jennifer G. Dy; Jan-Willem van de Meent (AISTATS, 2021)

Action Priors for Large Action Spaces in Robotics. - Ondrej Biza; Dian Wang 001; Robert Platt Jr. 001; Jan-Willem van de Meent; Lawson L. S. Wong (AAMAS, 2021)

Disentangling Representations of Text by Masking Transformers. - Xiongyi Zhang; Jan-Willem van de Meent; Byron C. Wallace (EMNLP (1), 2021)

Conjugate Energy-Based Models. - Hao Wu 020; Babak Esmaeili 001; Michael L. Wick; Jean-Baptiste Tristan; Jan-Willem van de Meent (ICML, 2021)

On the Impact of Random Seeds on the Fairness of Clinical Classifiers. - Silvio Amir; Jan-Willem van de Meent; Byron C. Wallace (NAACL-HLT, 2021)

Nested Variational Inference. - Heiko Zimmermann; Hao Wu 020; Babak Esmaeili 001; Jan-Willem van de Meent (NeurIPS, 2021)

Learning proposals for probabilistic programs with inference combinators. - Sam Stites; Heiko Zimmermann; Hao Wu 020; Eli Sennesh; Jan-Willem van de Meent (UAI, 2021)

Generator Surgery for Compressed Sensing. - Niklas Smedemark-Margulies; Jung Yeon Park; Max Daniels; Rose Yu; Jan-Willem van de Meent; Paul Hand (CoRR, 2021)

That's the Wrong Lung! Evaluating and Improving the Interpretability of Unsupervised Multimodal Encoders for Medical Data. - Denis Jered McInerney; Geoffrey S. Young; Jan-Willem van de Meent; Byron C. Wallace (EMNLP, 2022)

Learning Symmetric Embeddings for Equivariant World Models. - Jung Yeon Park; Ondrej Biza; Linfeng Zhao; Jan-Willem van de Meent; Robin Walters 001 (ICML, 2022)

Deriving Time-Averaged Active Inference from Control Principles. - Eli Sennesh; Jordan Theriault; Jan-Willem van de Meent; Lisa Feldman Barrett; Karen S. Quigley (IWAI, 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)

Factored World Models for Zero-Shot Generalization in Robotic Manipulation. - Ondrej Biza; Thomas Kipf; David Klee; Robert Platt Jr. 001; Jan-Willem van de Meent; Lawson L. S. Wong (CoRR, 2022)

Binding Actions to Objects in World Models. - Ondrej Biza; Robert Platt 001; Jan-Willem van de Meent; Lawson L. S. Wong; Thomas Kipf (CoRR, 2022)

Verified Reversible Programming for Verified Lossless Compression. - James Townsend; Jan-Willem van de Meent (CoRR, 2022)

A Computational Neural Model for Mapping Degenerate Neural Architectures. - Zulqarnain Khan; Yiyu Wang; Eli Sennesh; Jennifer G. Dy; Sarah Ostadabbas; Jan-Willem van de Meent; J. Benjamin Hutchinson; Ajay B. Satpute (Neuroinformatics, 2022)

Probabilistic program inference in network-based epidemiological simulations. - Niklas Smedemark-Margulies; Robin Walters 001; Heiko Zimmermann; Lucas Laird; Christian van der Loo; Neela Kaushik; Rajmonda Caceres; Jan-Willem van de Meent (PLoS Comput. Biol., 2022)

One-shot Imitation Learning via Interaction Warping. - Ondrej Biza; Skye Thompson; Kishore Reddy Pagidi; Abhinav Kumar; Elise van der Pol; Robin Walters 001; Thomas Kipf; Jan-Willem van de Meent; Lawson L. S. Wong; Robert Platt 001 (CoRL, 2023)

CHiLL: Zero-shot Custom Interpretable Feature Extraction from Clinical Notes with Large Language Models. - Denis Jered McInerney; Geoffrey S. Young; Jan-Willem van de Meent; Byron C. Wallace (EMNLP (Findings), 2023)

Topological Obstructions and How to Avoid Them. - Babak Esmaeili 001; Robin Walters 001; Heiko Zimmermann; Jan-Willem van de Meent (NeurIPS, 2023)

String Diagrams with Factorized Densities. - Eli Sennesh; Jan-Willem van de Meent (ACT, 2023)

A Variational Perspective on Generative Flow Networks. - Heiko Zimmermann; Fredrik Lindsten; Jan-Willem van de Meent; Christian A. Naesseth (Trans. Mach. Learn. Res., 2023)

Entropy Coding of Unordered Data Structures. - Julius Kunze; Daniel Severo 001; Giulio Zani; Jan-Willem van de Meent; James Townsend (ICLR, 2024)

Towards Reducing Diagnostic Errors with Interpretable Risk Prediction. - Denis Jered McInerney; William Dickinson; Lucy C. Flynn; Andrea Young; Geoffrey S. Young; Jan-Willem van de Meent; Byron C. Wallace (NAACL-HLT, 2024)

Variational Flow Matching for Graph Generation. - Floor Eijkelboom; Grigory Bartosh; Christian Andersson Naesseth; Max Welling; Jan-Willem van de Meent (NeurIPS, 2024)

Practical Shuffle Coding. - Julius Kunze; Daniel Severo 001; Jan-Willem van de Meent; James Townsend (NeurIPS, 2024)

VISA: Variational Inference with Sequential Sample-Average Approximations. - Heiko Zimmermann; Christian Andersson Naesseth; Jan-Willem van de Meent (NeurIPS, 2024)

Inverse Concave-Utility Reinforcement Learning is Inverse Game Theory. - Mustafa Mert Çelikok; Frans A. Oliehoek; Jan-Willem van de Meent (CoRR, 2024)

On-Robot Reinforcement Learning with Goal-Contrastive Rewards. - Ondrej Biza; Thomas Weng; Lingfeng Sun; Karl Schmeckpeper; Tarik Kelestemur; Yecheng Jason Ma; Robert Platt 001; Jan-Willem van de Meent; Lawson L. S. Wong (CoRR, 2024)

Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systems. - Maksim Zhdanov 001; Max Welling; Jan-Willem van de Meent (CoRR, 2025)

Exponential Family Variational Flow Matching for Tabular Data Generation. - Andrés Guzmán-Cordero; Floor Eijkelboom; Jan-Willem van de Meent (CoRR, 2025)

Controlled Generation with Equivariant Variational Flow Matching. - Floor Eijkelboom; Heiko Zimmermann; Sharvaree Vadgama; Erik J. Bekkers; Max Welling; Christian A. Naesseth; Jan-Willem van de Meent (CoRR, 2025)