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Jan-Willem van de Meent
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2020 – today
- 2024
- [c34]Julius Kunze, Daniel Severo, Giulio Zani, Jan-Willem van de Meent, James Townsend:
Entropy Coding of Unordered Data Structures. ICLR 2024 - [c33]Denis Jered McInerney, William Dickinson, Lucy C. Flynn, Andrea Young, Geoffrey Young, Jan-Willem van de Meent, Byron C. Wallace:
Towards Reducing Diagnostic Errors with Interpretable Risk Prediction. NAACL-HLT 2024: 7193-7210 - [i48]Denis Jered McInerney, William Dickinson, Lucy C. Flynn, Andrea Young, Geoffrey S. Young, Jan-Willem van de Meent, Byron C. Wallace:
Towards Reducing Diagnostic Errors with Interpretable Risk Prediction. CoRR abs/2402.10109 (2024) - [i47]Heiko Zimmermann, Christian A. Naesseth, Jan-Willem van de Meent:
VISA: Variational Inference with Sequential Sample-Average Approximations. CoRR abs/2403.09429 (2024) - [i46]Mustafa Mert Çelikok, Frans A. Oliehoek, Jan-Willem van de Meent:
Inverse Concave-Utility Reinforcement Learning is Inverse Game Theory. CoRR abs/2405.19024 (2024) - [i45]Floor Eijkelboom, Grigory Bartosh, Christian Andersson Naesseth, Max Welling, Jan-Willem van de Meent:
Variational Flow Matching for Graph Generation. CoRR abs/2406.04843 (2024) - [i44]Julius Kunze, Daniel Severo, Giulio Zani, Jan-Willem van de Meent, James Townsend:
Entropy Coding of Unordered Data Structures. CoRR abs/2408.08837 (2024) - [i43]Ondrej Biza, Thomas Weng, Lingfeng Sun, Karl Schmeckpeper, Tarik Kelestemur, Yecheng Jason Ma, Robert Platt, Jan-Willem van de Meent, Lawson L. S. Wong:
On-Robot Reinforcement Learning with Goal-Contrastive Rewards. CoRR abs/2410.19989 (2024) - 2023
- [j4]Heiko Zimmermann, Fredrik Lindsten, Jan-Willem van de Meent, Christian A. Naesseth:
A Variational Perspective on Generative Flow Networks. Trans. Mach. Learn. Res. 2023 (2023) - [c32]Ondrej Biza, Skye Thompson, Kishore Reddy Pagidi, Abhinav Kumar, Elise van der Pol, Robin Walters, Thomas Kipf, Jan-Willem van de Meent, Lawson L. S. Wong, Robert Platt:
One-shot Imitation Learning via Interaction Warping. CoRL 2023: 2519-2536 - [c31]Denis Jered McInerney, Geoffrey Young, Jan-Willem van de Meent, Byron C. Wallace:
CHiLL: Zero-shot Custom Interpretable Feature Extraction from Clinical Notes with Large Language Models. EMNLP (Findings) 2023: 8477-8494 - [c30]Babak Esmaeili, Robin Walters, Heiko Zimmermann, Jan-Willem van de Meent:
Topological Obstructions and How to Avoid Them. NeurIPS 2023 - [c29]Eli Sennesh, Jan-Willem van de Meent:
String Diagrams with Factorized Densities. ACT 2023: 260-278 - [e1]Francisco J. R. Ruiz, Jennifer G. Dy, Jan-Willem van de Meent:
International Conference on Artificial Intelligence and Statistics, 25-27 April 2023, Palau de Congressos, Valencia, Spain. Proceedings of Machine Learning Research 206, PMLR 2023 [contents] - [i42]Denis Jered McInerney, Geoffrey Young, Jan-Willem van de Meent, Byron C. Wallace:
CHiLL: Zero-shot Custom Interpretable Feature Extraction from Clinical Notes with Large Language Models. CoRR abs/2302.12343 (2023) - [i41]Ondrej Biza, Skye Thompson, Kishore Reddy Pagidi, Abhinav Kumar, Elise van der Pol, Robin Walters, Thomas Kipf, Jan-Willem van de Meent, Lawson L. S. Wong, Robert Platt:
One-shot Imitation Learning via Interaction Warping. CoRR abs/2306.12392 (2023) - [i40]Babak Esmaeili, Robin Walters, Heiko Zimmermann, Jan-Willem van de Meent:
Topological Obstructions and How to Avoid Them. CoRR abs/2312.07529 (2023) - 2022
- [j3]Zulqarnain Khan
, Yiyu Wang
A Computational Neural Model for Mapping Degenerate Neural Architectures. Neuroinformatics 20(4): 965-979 (2022) - [j2]Niklas Smedemark-Margulies
Probabilistic program inference in network-based epidemiological simulations. PLoS Comput. Biol. 18(11): 1010591 (2022) - [c28]Denis Jered McInerney, Geoffrey Young, Jan-Willem van de Meent, Byron C. Wallace:
That's the Wrong Lung! Evaluating and Improving the Interpretability of Unsupervised Multimodal Encoders for Medical Data. EMNLP 2022: 3626-3648 - [c27]Jung Yeon Park, Ondrej Biza, Linfeng Zhao, Jan-Willem van de Meent, Robin Walters:
Learning Symmetric Embeddings for Equivariant World Models. ICML 2022: 17372-17389 - [c26]Eli Sennesh
Deriving Time-Averaged Active Inference from Control Principles. IWAI 2022: 355-370 - [c25]Peyman Bateni, Jarred Barber, Jan-Willem van de Meent, Frank Wood:
Enhancing Few-Shot Image Classification with Unlabelled Examples. WACV 2022: 1597-1606 - [i39]Peyman Bateni, Jarred Barber, Raghav Goyal, Vaden Masrani, Jan-Willem van de Meent, Leonid Sigal, Frank Wood:
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning. CoRR abs/2201.05151 (2022) - [i38]Ondrej Biza, Thomas Kipf, David Klee, Robert Platt Jr., Jan-Willem van de Meent, Lawson L. S. Wong:
Factored World Models for Zero-Shot Generalization in Robotic Manipulation. CoRR abs/2202.05333 (2022) - [i37]Jung Yeon Park, Ondrej Biza, Linfeng Zhao, Jan-Willem van de Meent, Robin Walters:
Learning Symmetric Embeddings for Equivariant World Models. CoRR abs/2204.11371 (2022) - [i36]Ondrej Biza, Robert Platt, Jan-Willem van de Meent, Lawson L. S. Wong, Thomas Kipf:
Binding Actions to Objects in World Models. CoRR abs/2204.13022 (2022) - [i35]Eli Sennesh, Jordan Theriault, Jan-Willem van de Meent, Lisa Feldman Barrett, Karen S. Quigley:
Deriving time-averaged active inference from control principles. CoRR abs/2208.10601 (2022) - [i34]Denis Jered McInerney, Geoffrey Young, Jan-Willem van de Meent, Byron C. Wallace:
That's the Wrong Lung! Evaluating and Improving the Interpretability of Unsupervised Multimodal Encoders for Medical Data. CoRR abs/2210.06565 (2022) - [i33]Heiko Zimmermann, Fredrik Lindsten, Jan-Willem van de Meent, Christian A. Naesseth:
A Variational Perspective on Generative Flow Networks. CoRR abs/2210.07992 (2022) - [i32]James Townsend, Jan-Willem van de Meent:
Verified Reversible Programming for Verified Lossless Compression. CoRR abs/2211.09676 (2022) - 2021
- [c24]Abdelrahman Madkour, Stacy Marsella, Casper Harteveld, Magy Seif El-Nasr, Jan-Willem van de Meent:
Guiding Generative Graph Grammars of Dungeon Mission Graphs via Examples. AIIDE Workshops 2021 - [c23]Alican Bozkurt, Babak Esmaeili, Jean-Baptiste Tristan, Dana H. Brooks, Jennifer G. Dy, Jan-Willem van de Meent:
Rate-Regularization and Generalization in Variational Autoencoders. AISTATS 2021: 3880-3888 - [c22]Ondrej Biza, Dian Wang, Robert Platt Jr., Jan-Willem van de Meent, Lawson L. S. Wong:
Action Priors for Large Action Spaces in Robotics. AAMAS 2021: 205-213 - [c21]Xiongyi Zhang, Jan-Willem van de Meent, Byron C. Wallace:
Disentangling Representations of Text by Masking Transformers. EMNLP (1) 2021: 778-791 - [c20]Hao Wu, Babak Esmaeili
Conjugate Energy-Based Models. ICML 2021: 11228-11239 - [c19]Silvio Amir, Jan-Willem van de Meent, Byron C. Wallace:
On the Impact of Random Seeds on the Fairness of Clinical Classifiers. NAACL-HLT 2021: 3808-3823 - [c18]Heiko Zimmermann, Hao Wu, Babak Esmaeili, Jan-Willem van de Meent:
Nested Variational Inference. NeurIPS 2021: 20423-20435 - [c17]Sam Stites, Heiko Zimmermann, Hao Wu, Eli Sennesh
Learning proposals for probabilistic programs with inference combinators. UAI 2021: 1056-1066 - [i31]Ondrej Biza, Dian Wang, Robert Platt Jr., Jan-Willem van de Meent, Lawson L. S. Wong:
Action Priors for Large Action Spaces in Robotics. CoRR abs/2101.04178 (2021) - [i30]Niklas Smedemark-Margulies, Jung Yeon Park, Max Daniels
Generator Surgery for Compressed Sensing. CoRR abs/2102.11163 (2021) - [i29]Sam Stites, Heiko Zimmermann, Hao Wu, Eli Sennesh, Jan-Willem van de Meent:
Learning Proposals for Probabilistic Programs with Inference Combinators. CoRR abs/2103.00668 (2021) - [i28]Silvio Amir, Jan-Willem van de Meent, Byron C. Wallace:
On the Impact of Random Seeds on the Fairness of Clinical Classifiers. CoRR abs/2104.06338 (2021) - [i27]Xiongyi Zhang, Jan-Willem van de Meent, Byron C. Wallace:
Disentangling Representations of Text by Masking Transformers. CoRR abs/2104.07155 (2021) - [i26]Heiko Zimmermann, Hao Wu, Babak Esmaeili, Jan-Willem van de Meent:
Nested Variational Inference. CoRR abs/2106.11302 (2021) - [i25]Hao Wu, Babak Esmaeili, Michael L. Wick, Jean-Baptiste Tristan, Jan-Willem van de Meent:
Conjugate Energy-Based Models. CoRR abs/2106.13798 (2021) - 2020
- [c16]Hao Wu, Heiko Zimmermann, Eli Sennesh, Tuan Anh Le, Jan-Willem van de Meent:
Amortized Population Gibbs Samplers with Neural Sufficient Statistics. ICML 2020: 10421-10431 - [c15]Denis Jered McInerney, Borna Dabiri, Anne-Sophie Touret, Geoffrey Young, Jan-Willem van de Meent, Byron C. Wallace:
Query-Focused EHR Summarization to Aid Imaging Diagnosis. MLHC 2020: 632-659 - [c14]Eli Sennesh, Zulqarnain Khan, Yiyu Wang, J. Benjamin Hutchinson, Ajay B. Satpute, Jennifer G. Dy, Jan-Willem van de Meent:
Neural Topographic Factor Analysis for fMRI Data. NeurIPS 2020 - [i24]Ondrej Biza, Robert Platt Jr., Jan-Willem van de Meent, Lawson L. S. Wong:
Learning discrete state abstractions with deep variational inference. CoRR abs/2003.04300 (2020) - [i23]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:
Deep Markov Spatio-Temporal Factorization. CoRR abs/2003.09779 (2020) - [i22]Denis Jered McInerney, Borna Dabiri, Anne-Sophie Touret, Geoffrey Young, Jan-Willem van de Meent, Byron C. Wallace:
Query-Focused EHR Summarization to Aid Imaging Diagnosis. CoRR abs/2004.04645 (2020) - [i21]Peyman Bateni, Jarred Barber, Jan-Willem van de Meent, Frank Wood:
Improving Few-Shot Visual Classification with Unlabelled Examples. CoRR abs/2006.12245 (2020)
2010 – 2019
- 2019
- [c13]Babak Esmaeili, Hao Wu, Sarthak Jain, Alican Bozkurt, N. Siddharth, Brooks Paige, Dana H. Brooks, Jennifer G. Dy, Jan-Willem van de Meent:
Structured Disentangled Representations. AISTATS 2019: 2525-2534 - [c12]Babak Esmaeili, Hongyi Huang, Byron C. Wallace, Jan-Willem van de Meent:
Structured Neural Topic Models for Reviews. AISTATS 2019: 3429-3439 - [i20]Eli Sennesh, Zulqarnain Khan, Jennifer G. Dy, Ajay B. Satpute, J. Benjamin Hutchinson, Jan-Willem van de Meent:
Neural Topographic Factor Analysis for fMRI Data. CoRR abs/1906.08901 (2019) - [i19]Hao Wu, Heiko Zimmermann, Eli Sennesh, Tuan Anh Le, Jan-Willem van de Meent:
Amortized Population Gibbs Samplers with Neural Sufficient Statistics. CoRR abs/1911.01382 (2019) - [i18]Alican Bozkurt, Babak Esmaeili, Dana H. Brooks, Jennifer G. Dy, Jan-Willem van de Meent:
Evaluating Combinatorial Generalization in Variational Autoencoders. CoRR abs/1911.04594 (2019) - 2018
- [c11]Sarthak Jain, Edward Banner, Jan-Willem van de Meent, Iain James Marshall, Byron C. Wallace:
Learning Disentangled Representations of Texts with Application to Biomedical Abstracts. EMNLP 2018: 4683-4693 - [i17]Babak Esmaeili, Hao Wu, Sarthak Jain, N. Siddharth, Brooks Paige, Jan-Willem van de Meent:
Hierarchical Disentangled Representations. CoRR abs/1804.02086 (2018) - [i16]Sarthak Jain, Edward Banner, Jan-Willem van de Meent, Iain James Marshall, Byron C. Wallace:
Learning Disentangled Representations of Texts with Application to Biomedical Abstracts. CoRR abs/1804.07212 (2018) - [i15]Jan-Willem van de Meent, Brooks Paige, Hongseok Yang, Frank Wood:
An Introduction to Probabilistic Programming. CoRR abs/1809.10756 (2018) - [i14]Xiaoyu Lu, Tom Rainforth, Yuan Zhou, Jan-Willem van de Meent, Yee Whye Teh:
On Exploration, Exploitation and Learning in Adaptive Importance Sampling. CoRR abs/1810.13296 (2018) - [i13]Eli Sennesh, Adam Scibior, Hao Wu, Jan-Willem van de Meent:
Composing Modeling and Inference Operations with Probabilistic Program Combinators. CoRR abs/1811.05965 (2018) - [i12]Iris Rubi Seaman, Jan-Willem van de Meent, David Wingate:
Modeling Theory of Mind for Autonomous Agents with Probabilistic Programs. CoRR abs/1812.01569 (2018) - [i11]Babak Esmaeili, Hongyi Huang, Byron C. Wallace, Jan-Willem van de Meent:
Structured Representations for Reviews: Aspect-Based Variational Hidden Factor Models. CoRR abs/1812.05035 (2018) - [i10]Alican Bozkurt, Babak Esmaeili, Dana H. Brooks, Jennifer G. Dy, Jan-Willem van de Meent:
Can VAEs Generate Novel Examples? CoRR abs/1812.09624 (2018) - 2017
- [c10]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: 5925-5935 - [i9]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 abs/1706.00400 (2017) - [i8]Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank D. Wood:
Bayesian Optimization for Probabilistic Programs. CoRR abs/1707.04314 (2017) - 2016
- [c9]Jan-Willem van de Meent, Brooks Paige, David Tolpin, Frank D. Wood:
Black-Box Policy Search with Probabilistic Programs. AISTATS 2016: 1195-1204 - [c8]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: 2616-2625 - [c7]David Tolpin
Design and Implementation of Probabilistic Programming Language Anglican. IFL 2016: 6:1-6:12 - [c6]Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank D. Wood:
Bayesian Optimization for Probabilistic Programs. NIPS 2016: 280-288 - [i7]David Tolpin, Jan-Willem van de Meent, Hongseok Yang, Frank D. Wood:
Design and Implementation of Probabilistic Programming Language Anglican. CoRR abs/1608.05263 (2016) - [i6]David Janz, Brooks Paige, Tom Rainforth, Jan-Willem van de Meent, Frank D. Wood:
Probabilistic structure discovery in time series data. CoRR abs/1611.06863 (2016) - [i5]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 abs/1611.07492 (2016) - 2015
- [j1]Max Greenfeld, Jan-Willem van de Meent, Dmitri S. Pavlichin, Hideo Mabuchi
Single-molecule dataset (SMD): a generalized storage format for raw and processed single-molecule data. BMC Bioinform. 16: 3:1-3:4 (2015) - [c5]Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka, Frank D. Wood:
Particle Gibbs with Ancestor Sampling for Probabilistic Programs. AISTATS 2015 - [c4]David Tolpin
Probabilistic Programming in Anglican. ECML/PKDD (3) 2015: 308-311 - [c3]David Tolpin
Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs. ECML/PKDD (2) 2015: 311-326 - [i4]David Tolpin, Jan-Willem van de Meent, Brooks Paige, Frank D. Wood:
Adaptive Scheduling in MCMC and Probabilistic Programming. CoRR abs/1501.05677 (2015) - [i3]Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka, Frank D. Wood:
Particle Gibbs with Ancestor Sampling for Probabilistic Programs. CoRR abs/1501.06769 (2015) - [i2]Frank D. Wood, Jan-Willem van de Meent, Vikash Mansinghka:
A New Approach to Probabilistic Programming Inference. CoRR abs/1507.00996 (2015) - [i1]Jan-Willem van de Meent, David Tolpin, Brooks Paige, Frank D. Wood:
Black-Box Policy Search with Probabilistic Programs. CoRR abs/1507.04635 (2015) - 2014
- [c2]Frank D. Wood, Jan-Willem van de Meent, Vikash Mansinghka:
A New Approach to Probabilistic Programming Inference. AISTATS 2014: 1024-1032 - 2013
- [c1]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: 361-369
Coauthor Index
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