Jan-Willem van de Meent

Orcid: 0000-0001-9465-5398

According to our database1, Jan-Willem van de Meent authored at least 63 papers between 2013 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
On-Robot Reinforcement Learning with Goal-Contrastive Rewards.
CoRR, 2024

Variational Flow Matching for Graph Generation.
CoRR, 2024

Inverse Concave-Utility Reinforcement Learning is Inverse Game Theory.
CoRR, 2024

VISA: Variational Inference with Sequential Sample-Average Approximations.
CoRR, 2024

Towards Reducing Diagnostic Errors with Interpretable Risk Prediction.
CoRR, 2024

Towards Reducing Diagnostic Errors with Interpretable Risk Prediction.
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2024

Entropy Coding of Unordered Data Structures.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
A Variational Perspective on Generative Flow Networks.
Trans. Mach. Learn. Res., 2023

String Diagrams with Factorized Densities.
Proceedings of the Sixth International Conference on Applied Category Theory 2023, 2023

Topological Obstructions and How to Avoid Them.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

CHiLL: Zero-shot Custom Interpretable Feature Extraction from Clinical Notes with Large Language Models.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

One-shot Imitation Learning via Interaction Warping.
Proceedings of the Conference on Robot Learning, 2023

2022
Probabilistic program inference in network-based epidemiological simulations.
PLoS Comput. Biol., November, 2022

A Computational Neural Model for Mapping Degenerate Neural Architectures.
Neuroinformatics, 2022

Verified Reversible Programming for Verified Lossless Compression.
CoRR, 2022

Binding Actions to Objects in World Models.
CoRR, 2022

Factored World Models for Zero-Shot Generalization in Robotic Manipulation.
CoRR, 2022

Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning.
CoRR, 2022

Enhancing Few-Shot Image Classification with Unlabelled Examples.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022

Deriving Time-Averaged Active Inference from Control Principles.
Proceedings of the Active Inference - Third International Workshop, 2022

Learning Symmetric Embeddings for Equivariant World Models.
Proceedings of the International Conference on Machine Learning, 2022

That's the Wrong Lung! Evaluating and Improving the Interpretability of Unsupervised Multimodal Encoders for Medical Data.
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

2021
Generator Surgery for Compressed Sensing.
CoRR, 2021

Learning proposals for probabilistic programs with inference combinators.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Nested Variational Inference.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On the Impact of Random Seeds on the Fairness of Clinical Classifiers.
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

Conjugate Energy-Based Models.
Proceedings of the 38th International Conference on Machine Learning, 2021

Disentangling Representations of Text by Masking Transformers.
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

Action Priors for Large Action Spaces in Robotics.
Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021

Rate-Regularization and Generalization in Variational Autoencoders.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Guiding Generative Graph Grammars of Dungeon Mission Graphs via Examples.
Proceedings of the Joint Proceedings of the AIIDE 2021 Workshops co-located with 17th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2021)(AAAI 2021), 2021

2020
Improving Few-Shot Visual Classification with Unlabelled Examples.
CoRR, 2020

Deep Markov Spatio-Temporal Factorization.
CoRR, 2020

Learning discrete state abstractions with deep variational inference.
CoRR, 2020

Neural Topographic Factor Analysis for fMRI Data.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Query-Focused EHR Summarization to Aid Imaging Diagnosis.
Proceedings of the Machine Learning for Healthcare Conference, 2020

Amortized Population Gibbs Samplers with Neural Sufficient Statistics.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Evaluating Combinatorial Generalization in Variational Autoencoders.
CoRR, 2019

Neural Topographic Factor Analysis for fMRI Data.
CoRR, 2019

Structured Neural Topic Models for Reviews.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Structured Disentangled Representations.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Can VAEs Generate Novel Examples?
CoRR, 2018

Structured Representations for Reviews: Aspect-Based Variational Hidden Factor Models.
CoRR, 2018

Modeling Theory of Mind for Autonomous Agents with Probabilistic Programs.
CoRR, 2018

Composing Modeling and Inference Operations with Probabilistic Program Combinators.
CoRR, 2018

On Exploration, Exploitation and Learning in Adaptive Importance Sampling.
CoRR, 2018

An Introduction to Probabilistic Programming.
CoRR, 2018

Hierarchical Disentangled Representations.
CoRR, 2018

Learning Disentangled Representations of Texts with Application to Biomedical Abstracts.
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31, 2018

2017
Learning Disentangled Representations with Semi-Supervised Deep Generative Models.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016
Inducing Interpretable Representations with Variational Autoencoders.
CoRR, 2016

Probabilistic structure discovery in time series data.
CoRR, 2016

Bayesian Optimization for Probabilistic Programs.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Design and Implementation of Probabilistic Programming Language Anglican.
Proceedings of the 28th Symposium on the Implementation and Application of Functional Programming Languages, 2016

Interacting Particle Markov Chain Monte Carlo.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Black-Box Policy Search with Probabilistic Programs.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Adaptive Scheduling in MCMC and Probabilistic Programming.
CoRR, 2015

Single-molecule dataset (SMD): a generalized storage format for raw and processed single-molecule data.
BMC Bioinform., 2015

Probabilistic Programming in Anglican.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

Particle Gibbs with Ancestor Sampling for Probabilistic Programs.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014
A New Approach to Probabilistic Programming Inference.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data.
Proceedings of the 30th International Conference on Machine Learning, 2013


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