Publications

Check out our people page to get an impression of the research done by the people at AMLab, and of course follow us on Twitter to stay tuned! Below you'll find an archive our groups output in ML research, all the way back to 1994!

2024

  1. NeurIPS
    Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling
    Bartosh, Grigory, Vetrov, Dmitry, and Naesseth, Christian A
    Advances in Neural Information Processing Systems Dec 2024
  2. NeurIPS
    Variational Flow Matching for Graph Generation
    Eijkelboom*, Floor, Bartosh*, Grigory, Naesseth, Christian Andersson, Welling, Max, and Meent, Jan-Willem
    Advances in Neural Information Processing Systems Dec 2024
  3. NeurIPS
    Equivariant Neural Diffusion for Molecule Generation
    Cornet, François RJ, Bartosh, Grigory, Schmidt, Mikkel N, and Naesseth, Christian A
    Advances in Neural Information Processing Systems Dec 2024
  4. ICML
    Neural Diffusion Models
    Bartosh, Grigory, Vetrov, Dmitry, and Naesseth, Christian A
    The 41st International Conference on Machine Learning (ICML) Jul 2024
  5. ICAPS
    Planning with a Learned Policy Basis to Optimally Solve Complex Tasks
    Kuric, D., Infante, G., Gómez, V., Jonsson, A., and Hoof, H.
    In International Conference on Automated Planning and Scheduling Jul 2024
  6. AAMAS
    Uncoupled Learning of Differential Stackelberg Equilibria with Commitments
    Loftin, Robert, Çelikok, Mustafa Mert, Hoof, Herke, Kaski, Samuel, and Oliehoek, Frans
    In Artificial Agents and Multi-Agent Systems (AAMAS) Jul 2024
  7. AISTATS
    Learning to Defer to a Population: A Meta-Learning Approach
    Tailor, Dharmesh, Patra, Aditya, Verma, Rajeev, Manggala, Putra, and Nalisnick, Eric
    In 27th International Conference on Artificial Intelligence and Statistics (to appear) May 2024
  8. ICLR
    Entropy Coding of Unordered Data Structures
    Kunze, Julius, Severo, Daniel, Zani, Giulio, van de Meent, Jan-Willem, and Townsend, James
    In International Conference on Learning Representations (ICLR) May 2024

2023

  1. ECML
    Learning Hierarchical Planning-Based Policies from Offline Data
    Woehlke, J., Schmitt, F., and Hoof, H.
    In Machine Learning and Knowledge Discovery in Databases: Research Track (ECML PKDD) 2023
  2. EMNLP
    CHiLL: Zero-shot Custom Interpretable Feature Extraction from Clinical Notes with Large Language Models
    McInerney, Denis Jered, Young, Geoffrey, Meent, Jan-Willem, and Wallace, Byron
    In The 2023 Conference on Empirical Methods in Natural Language Processing (to appear) 2023
  3. EMNLP
    Aligning Predictive Uncertainty with Clarification Questions in Grounded Dialog
    Naszadi, Kata, Manggala, Putra, and Monz, Christof
    In The 2023 Conference on Empirical Methods in Natural Language Processing (to appear) Dec 2023
  4. NeurIPS
    Implicit Neural Convolutional Kernels for Steerable CNNs
    Zhdanov, Maksim, Hoffmann, Nico, and Cesa, Gabriele
    In Thirty-seventh Conference on Neural Information Processing Systems (to appear) Dec 2023
  5. NeurIPS
    Flow Factorzied Representation Learning
    Song, Yue, Keller, T Anderson, Sebe, Nicu, and Welling, Max
    In Thirty-seventh Conference on Neural Information Processing Systems (to appear) Dec 2023
  6. NeurIPS
    Rotating Features for Object Discovery
    In Thirty-seventh Conference on Neural Information Processing Systems (to appear) Dec 2023
  7. NeurIPS
    Latent Field Discovery in Interacting Dynamical Systems with Neural Fields
    Kofinas, Miltiadis, Bekkers, Erik J, Nagaraja, Naveen Shankar, and Gavves, Efstratios
    In Thirty-seventh Conference on Neural Information Processing Systems (to appear) Dec 2023
  8. NeurIPS
    Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity
    Jazbec, Metod, Allingham, James Urquhart, Zhang, Dan, and Nalisnick, Eric
    In Thirty-seventh Conference on Neural Information Processing Systems (to appear) Dec 2023
  9. NeurIPS
    Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning
    Feng, Fan, and Magliacane, Sara
    In Thirty-seventh Conference on Neural Information Processing Systems (to appear) Dec 2023
  10. NeurIPS
    Invariant Neural Ordinary Differential Equations
    Auzina, Ilze Amanda, Yıldız, Çağatay, Magliacane, Sara, Bethge, Matthias, and Gavves, Efstratios
    In Thirty-seventh Conference on Neural Information Processing Systems (to appear) Dec 2023
  11. NeurIPS
    Clifford group equivariant neural networks
    Ruhe, David, Brandstetter, Johannes, and Forré, Patrick
    In Thirty-seventh Conference on Neural Information Processing Systems (to appear) Dec 2023
  12. NeurIPS
    Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems
    Lippert, Fiona, Kranstauber, Bart, Loon, E Emiel, and Forré, Patrick
    In Thirty-seventh Conference on Neural Information Processing Systems (to appear) Dec 2023
  13. NeurIPS
    Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
    Wu, Luhuan, Trippe, Brian L, Naesseth, Christian A, Blei, David M, and Cunningham, John P
    In Thirty-seventh Conference on Neural Information Processing Systems (to appear) Dec 2023
  14. NeurIPS
    Topological Obstructions and How to Avoid Them
    Esmaeili, Babak, Walters, Robin, Zimmermann, Heiko, and van de Meent, Jan-Willem
    In Thirty-seventh Conference on Neural Information Processing Systems (to appear) Dec 2023
  15. NeurIPS
    The Memory-Perturbation Equation: Understanding Model’s Sensitivity to Data
    Nickl, Peter, Xu, Lu*, Tailor, Dharmesh*, Möllenhoff, Thomas, and Khan, Mohammad Emtiyaz
    In Thirty-seventh Conference on Neural Information Processing Systems Dec 2023
  16. CoRL
    One-shot Imitation Learning via Interaction Warping
    Biza, Ondrej, Thompson, Skye, Pagidi, Kishore Reddy, Kumar, Abhinav, Pol, Elise, Walters, Robin, Kipf, Thomas, Meent, Jan-Willem, Wong, Lawson L.S., and Platt, Robert
    In 7th Annual Conference on Robot Learning Nov 2023
  17. UAI
    Exploiting Inferential Structure in Neural Processes
    Tailor, Dharmesh, Khan, Mohammad Emtiyaz, and Nalisnick, Eric
    In The 39th Conference on Uncertainty in Artificial Intelligence Aug 2023
  18. ACT
    String Diagrams with Factorized Densities
    Sennesh, Eli, and van de Meent, Jan-Willem
    In Applied Category Theory Jul 2023
  19. TMLR
    Reusable Options through Gradient-based Meta Learning
    Kuric, David, and Hoof, Herke
    Transactions on Machine Learning Research Mar 2023
  20. TMLR
    A Variational Perspective on Generative Flow Networks
    Zimmermann, Heiko, Lindsten, Fredrik, Meent, Jan-Willem, and Naesseth, Christian A
    Transactions on Machine Learning Research Apr 2023
  21. ICLR
    Bridge the Inference Gaps of Neural Processes via Expectation Maximization
    Wang, Qi, Federici, Marco, and Hoof, Herke
    In International Conference on Learning Representations Apr 2023
  22. ICLR
    Sampling-Based Inference for Large Linear Models, with Application to Linearised Laplace
    Antorán, Javier, Padhy, Shreyas, Barbano, Riccardo, Nalisnick, Eric, Janz, David, and Miguel Hernández-Lobato, José
    In International Conference on Learning Representations Apr 2023
  23. AISTATS
    Do Bayesian Neural Networks Need To Be Fully Stochastic?
    Sharma, Mrinank, Farquhar, Sebastian, Nalisnick, Eric, and Rainforth, Tom
    In Proceedings of The 26th International Conference on Artificial Intelligence and Statistics Apr 2023
  24. AISTATS
    Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensembles
    Verma, Rajeev, Barrejón, Daniel, and Nalisnick, Eric
    In Proceedings of The 26th International Conference on Artificial Intelligence and Statistics Apr 2023
  25. ECML
    Learning objective-specific active learning strategies with Attentive Neural Processes
    Bakker, Tim, Hoof, Herke, and Welling, Max
    In Proceedings of the European Conference on Machine Learning Sep 2023
  26. NeurIPS
    Workshop
    Active Learning Policies for Solving Inverse Problems
    Bakker, T., Hehn, T., Orekondy, T., Behboodi, A., and Massoli, F. Valerio
    In Neural Information Processing Systems Workshop on Adaptive Experimental Design and Active Learning in the Real World Dec 2023
  27. NeurIPS
    Workshop
    Switching policies for solving inverse problems
    Bakker, T., Massoli, F. Valerio, Hehn, T., Orekondy, T., and Behboodi, A.
    In Neural Information Processing Systems Workshop on Deep Learning and Inverse Problems Dec 2023

2022

  1. PLOS Comp Bio
    Probabilistic Program Inference in Network-Based Epidemiological Simulations
    Smedemark-Margulies, Niklas, Walters, Robin, Zimmermann, Heiko, Laird, Lucas, Loo, Christian, Kaushik, Neela, Caceres, Rajmonda, and Meent, Jan-Willem
    PLOS Computational Biology Nov 2022
  2. IJCNN
    Logic-based AI for Interpretable Board Game Winner Prediction with Tsetlin Machine
    Giri, Charul, Granmo, Ole-Christopher, Hoof, Herke, and Blakely, Christian D.
    In International Joint Conference on Neural Networks Nov 2022
  3. NeurIPS
    Learning Expressive Meta-Representations with Mixture of Expert Neural Processes
    Wang, Qi, and Hoof, Herke
    In Advances in Neural Information Processing Systems Nov 2022
  4. NeurIPS
    Factored Adaptation for Non-Stationary Reinforcement Learning
    Feng, Fan, Huang, Biwei, Zhang, Kun, and Magliacane, Sara
    In Advances in Neural Information Processing Systems Nov 2022
  5. NeurIPS
    Neural Topological Ordering for Computation Graphs
    Gagrani, Mukul, Rainone, Corrado, Yang, Yang, Teague, Harris, Jeon, Wonseok, Hoof, Herke, Zeng, Weiliang Will, Zappi, Piero, Lott, Christopher, and Bondesan, Roberto
    In Advances in Neural Information Processing Systems Nov 2022
  6. ICML
    Equivariant diffusion for molecule generation in 3d
    Hoogeboom, Emiel, Satorras, Vı́ctor Garcia, Vignac, Clément, and Welling, Max
    In International Conference on Machine Learning Nov 2022
  7. ICML
    Lie Point Symmetry Data Augmentation for Neural PDE Solvers
    Brandstetter, Johannes, Welling, Max, and Worrall, Daniel E
    In International Conference on Machine Learning Nov 2022
  8. ICML
    Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups
    Knigge, David M, Romero, David W, and Bekkers, Erik J
    International Conference on Machine Learning Nov 2022
  9. ICML
    CITRIS: Causal Identifiability from Temporal Intervened Sequences
    Lippe, Phillip, Magliacane, Sara, Löwe, Sindy, Asano, Yuki M, Cohen, Taco, and Gavves, Efstratios
    International Conference on Machine Learning Nov 2022
  10. ICML
    Learning Symmetric Embeddings for Equivariant World Models
    Park, Jung Yeon, Biza, Ondrej, Zhao, Linfeng, Meent, Jan-Willem, and Walters, Robin
    In International Conference on Machine Learning Nov 2022
  11. ICML
    Adapting the Linearised Laplace Model Evidence for Modern Deep Learning
    Antoran, Javier, Janz, David, Allingham, James Urquhart, Daxberger, Erik, Barbano, Riccardo, Nalisnick, Eric, and Hernandez-Lobato, Jose Miguel
    In Proceedings of the 39th International Conference on Machine Learning Nov 2022
  12. ICML
    Calibrated Learning to Defer with One-vs-All Classifiers
    Verma, Rajeev, and Nalisnick, Eric
    In Proceedings of the 39th International Conference on Machine Learning Nov 2022
  13. ICML
    Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models and Amortized Policy Search
    Wang, Q., and Hoof, H.
    In International Conference on Machine Learning Nov 2022
  14. CLeaR
    Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data
    Löwe, S., Madras, D., Zemel, R., and Welling, M.
    Causal Learning and Reasoning Nov 2022
  15. ICLR
    Geometric and Physical Quantities improve E (3) Equivariant Message Passing
    Brandstetter, Johannes, Hesselink, Rob, Pol, Elise, Bekkers, Erik, and Welling, Max
    In International Conference on Learning Representations Nov 2022
  16. ICLR
    Self-Supervised Inference in State-Space Models
    Ruhe, David, and Forré, Patrick
    In International Conference on Learning Representations Nov 2022
  17. ASCOM
    Detecting dispersed radio transients in real time using convolutional neural networks
    Ruhe, David, Kuiack, Mark, Rowlinson, Antonia, Wijers, Ralph, and Forré, Patrick
    Astronomy and Computing Nov 2022
  18. ICLR
    Multi-Agent MDP Homomorphic Networks
    Pol, Elise, Hoof, Herke, Oliehoek, Frans, and Welling, Max
    In International Conference on Learning Representations Nov 2022
  19. CPAIOR
    Deep Policy Dynamic Programming for Vehicle Routing Problems
    Kool, Wouter, Hoof, Herke, Gromicho, Joaquim, and Welling, Max
    In International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research Nov 2022
  20. AAAI
    Fast and Data Efficient Reinforcement Learning from Pixels via Non-Parametric Value Approximation
    Long, Alex, Blair, Alan, and Hoof, Herke
    In AAAI National Conference on Artificial Intelligence Nov 2022
  21. IJCAI
    Value Refinement Network (VRN)
    Wöhlke, Jan, Schmitt, Felix, and Hoof, Herke
    In International Joint Conference on Artificial Intelligence Nov 2022
  22. IJCAI
    Leveraging class abstraction for commonsense reinforcement learning via residual policy gradient methods
    Höpner, Niklas, Tiddi, Ilaria, and Hoof, Herke
    In International Joint Conference on Artificial Intelligence Nov 2022
  23. MIDL
    On learning adaptive acquisition policies for undersampled multi-coil MRI reconstruction
    Bakker, T., Muckley, M., Romero-Soriano, A., Drozdzal, M., and Pineda, L.
    In Proceedings of Machine Learning Research Jul 2022

2021

  1. NeurIPS
    Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders
    Keller, T. Anderson, Gao, Qinghe, and Welling, Max
    In SVRHM 2021 Workshop at NeurIPS 2021
  2. ICCV
    Predictive Coding With Topographic Variational Autoencoders
    Keller, T. Anderson, and Welling, Max
    In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops Oct 2021
  3. NeurIPS
    Topographic VAEs learn Equivariant Capsules
    Keller, T. Anderson, and Welling, Max
    In Advances in Neural Information Processing Systems Oct 2021
  4. ICML
    Self Normalizing Flows
    Keller, T. Anderson, Peters, Jorn W.T., Jaini, Priyank, Hoogeboom, Emiel, Forré, Patrick, and Welling, Max
    In Proceedings of the 38th International Conference on Machine Learning 18–24 jul 2021
  5. NeurIPS
    As easy as APC: Leveraging self-supervised learning in the context of time series classification with varying levels of sparsity and severe class imbalance
    Wever, Fiorella, Keller, T. Anderson, Garcia, Victor, and Symul, Laura
    In Self-Supervised Learning Workshop at NeurIPS 18–24 jul 2021
  6. UAI
    Variational combinatorial sequential Monte Carlo methods for Bayesian phylogenetic inference
    Moretti, Antonio Khalil, Zhang, Liyi, Naesseth, Christian A., Venner, Hadiah, Blei, David, and Pe’er, Itsik
    In Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence 27–30 jul 2021
  7. AISTATS
    Rate-Regularization and Generalization in Variational Autoencoders
    Bozkurt, Alican, Esmaeili, Babak, Tristan, Jean-Baptiste, Brooks, Dana, Dy, Jennifer, and Meent, Jan-Willem
    In International Conference on Artificial Intelligence and Statistics Mar 2021
  8. NeurIPS
    Nested Variational Inference
    Zimmermann, Heiko, Wu, Hao, Esmaeili, Babak, and Meent, Jan-Willem
    In Advances in Neural Information Processing Systems Mar 2021
  9. ICML
    Conjugate Energy-Based Models
    Wu, Hao*, Esmaeili, Babak*, Wick, Michael, Tristan, Jean-Baptiste, and van de Meent, Jan-Willem
    In Proceedings of the 38th International Conference on Machine Learning (ICML) 18–24 jul 2021
  10. UAI
    Learning proposals for probabilistic programs with inference combinators
    Stites, Sam, Zimmermann, Heiko, Wu, Hao, Sennesh, Eli, and Meent, Jan-Willem
    In Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence 27–30 jul 2021
  11. AISTATS
    Predictive Complexity Priors
    Nalisnick, Eric, Gordon, Jonathan, and Miguel Hernandez-Lobato, Jose
    In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics 13–15 apr 2021
  12. ICML
    Bayesian Deep Learning via Subnetwork Inference
    Daxberger, Erik, Nalisnick, Eric, Allingham, James U, Antoran, Javier, and Hernandez-Lobato, Jose Miguel
    In Proceedings of the 38th International Conference on Machine Learning 18–24 jul 2021
  13. JMLR
    Normalizing Flows for Probabilistic Modeling and Inference
    Papamakarios, George, Nalisnick, Eric, Rezende, Danilo Jimenez, Mohamed, Shakir, and Lakshminarayanan, Balaji
    Journal of Machine Learning Research 18–24 jul 2021
  14. JMS
    Optimizing Adaptive Notifications in Mobile Health Interventions Systems: Reinforcement Learning from a Data-driven Behavioral Simulator
    Wang, Shihan, Zhang, Chao, Kröse, Ben, and Hoof, Herke
    Journal of Medical Systems 18–24 jul 2021
  15. IJERPH
    Reinforcement Learning to Send Reminders at Right Moments in Smartphone Exercise Application: A Feasibility Study
    Wang, S., Sporrel, K., Hoof, H., Simons, M., Boer, R., Ettema, D., Nibbeling, N., Deutekom, M., and Kröse, B.
    International Journal of Environmental Research and Public Health, Special Issue 18–24 jul 2021
  16. ICRA
    Hierarchies of Planning and Reinforcement Learning for Robot Navigation
    Wöhlke, J., Schmitt, F., and Hoof, H.
    In IEEE International Conference on Robotics and Automation 18–24 jul 2021
  17. ICML
    Deep Coherent Exploration For Continuous Control
    Zhang, Yijie, and Hoof, Herke
    In International Conference on Machine Learning 18–24 jul 2021
  18. UrbComp
    Back to Basics: Deep Reinforcement Learning in Traffic Signal Control
    Kanis, S., Samson, L., Bloembergen, D., and Bakker, T.
    The 10th International Workshop on Urban Computing Nov 2021

All Publications (via Pure)