AMLab | Amsterdam Machine Learning Lab

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The Amsterdam Machine Learning Lab (AMLab) conducts research in machine learning, artificial intelligence, and its applications to large scale data domains in science and industry. This includes the development of deep generative models, methods for approximate inference, probabilistic programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning.

AMLab comprises 7 faculty. Jan-Willem van de Meent, who serves as director, Max Welling, Herke van Hoof, Patrick Forré, Erik Bekkers, Christian Naesseth, and Sara Magliacane. The lab participates in public-private partnerships with industry through the QUvA Lab (with Qualcomm) and the Delta Lab (with Bosch). The lab also engages in cross-disciplinary collaborations through the AI4Science Lab.

News

Nov 7, 2024 The AMLAB has an open postdoc position on support human decision making using reinforcement learning. You will be working with Herke van Hoof and Frans Oliehoek. Full details and instructions to apply can be found in the official vacancy. The position is part of the AI4REALNET project that receives funding from the European Union’s Horizon Europe programme, and the Hybrid Intelligence project that receives funding from the NWO.
May 7, 2024 Congratulations to Durk Kingma and Max Welling on receiving the inaugural test-of-time award at ICLR 2024!
Apr 26, 2024 The BeNeRL workshop on reinforcement learning will take place June 10th in Amsterdam! The programme includes keynotes by Frans Oliehoek, Roxana Radulescu, Thomas Moerland, Thiago Dias Simao, and Yailen Martinez Jimenez. The workshop is free to attend but registration is required. More information and registration via the workshop website. The workshop is supported by Ellis Amsterdam and NWO.
Jan 26, 2024 Sara Magliacane and Herke van Hoof have two open PhD positions on machine learning in the fintech domain (in collaboration with Adyen). One student will work on causal machine learning, and the second student will work on reinforcement learning. Deadline for applications is March 11th. For all details and how to apply, please see the official vacancy.
Jan 4, 2024 We currently have two postdoc openings at AMLab, both with deadline of Februari 4th:
  1. Christian Naesseth is hiring for a postdoc position focusing topics relating to generative AI, AI4Science, or uncertainty quantification. This position is part of the UvA Bosch Delta Lab. You can apply here.
  2. Jan-Willem van de Meent is hiring for a postdoc position in AI methods for sustainability, including Bayesian optimization and experiment design, data-efficient surrogate modeling, probabilistic programming, and simulation-based inference. This position is part of the ELiAS program. You can apply here.

Recent Publications

  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
  9. 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) May 2023
  10. 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) May 2023
  11. 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
  12. 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
  13. 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
  14. NeurIPS
    Rotating Features for Object Discovery
    In Thirty-seventh Conference on Neural Information Processing Systems (to appear) Dec 2023
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. ACT
    String Diagrams with Factorized Densities
    Sennesh, Eli, and van de Meent, Jan-Willem
    In Applied Category Theory Jul 2023
  27. TMLR
    Reusable Options through Gradient-based Meta Learning
    Kuric, David, and Hoof, Herke
    Transactions on Machine Learning Research Mar 2023
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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
  43. 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
  44. 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
  45. 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
  46. 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
  47. 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
  48. 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
  49. 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
  50. 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
  51. ICLR
    Self-Supervised Inference in State-Space Models
    Ruhe, David, and Forré, Patrick
    In International Conference on Learning Representations Nov 2022
  52. 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
  53. ICLR
    Multi-Agent MDP Homomorphic Networks
    Pol, Elise, Hoof, Herke, Oliehoek, Frans, and Welling, Max
    In International Conference on Learning Representations Nov 2022
  54. 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
  55. 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
  56. IJCAI
    Value Refinement Network (VRN)
    Wöhlke, Jan, Schmitt, Felix, and Hoof, Herke
    In International Joint Conference on Artificial Intelligence Nov 2022
  57. 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
  58. 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

Selected Publications

  1. NeurIPS
    MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning
    Pol, Elise, Worrall, Daniel, Hoof, Herke, Oliehoek, Frans, and Welling, Max
    In Advances in Neural Information Processing Systems 2020
  2. ICLR
    Estimating Gradients for Discrete Random Variables by Sampling without Replacement
    Kool, Wouter, Hoof, Herke, and Welling, Max
    In International Conference on Learning Representations 2020
  3. ICLR
    B-Spline CNNs on Lie groups
    Bekkers, Erik J
    In International Conference on Learning Representations 2019
  4. AISTATS
    Structured Disentangled Representations
    Esmaeili, Babak, Wu, Hao, Jain, Sarthak, Bozkurt, Alican, Siddharth, N., Paige, Brooks, Brooks, Dana H., Dy, Jennifer, and van de Meent, Jan-Willem
    Artificial Intelligence and Statistics 2019
  5. ICLR
    Do Deep Generative Models Know What They Don’t Know?
    Nalisnick, Eric, Matsukawa, Akihiro, Teh, Yee Whye, Gorur, Dilan, and Lakshminarayanan, Balaji
    In International Conference on Learning Representations 2019
  6. ESWC
    Modeling Relational Data with Graph Convolutional Networks
    Schlichtkrull, Michael, Kipf, Thomas N., Bloem, Peter, Berg, Rianne, Titov, Ivan, and Welling, Max
    In The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece 2018
  7. NeurIPS
    Learning Disentangled Representations with Semi-Supervised Deep Generative Models
    Siddharth, N., Paige, Brooks, Meent, Jan-Willem, Desmaison, Alban, Goodman, Noah D., Kohli, Pushmeet, Wood, Frank, and Torr, Philip
    In Advances in Neural Information Processing Systems 30 2017
  8. ICLR
    Semi-supervised classification with graph convolutional networks
    Kipf, Thomas N, and Welling, Max
    In International Conference on Learning Representations 2017
  9. ICML
    Group Equivariant Convolutional Networks
    Cohen, Taco, and Welling, Max
    In Proceedings of The 33rd International Conference on Machine Learning 20–22 jun 2016
  10. NeurIPS
    Improved Variational Inference with Inverse Autoregressive Flow
    Kingma, Durk P, Salimans, Tim, Jozefowicz, Rafal, Chen, Xi, Sutskever, Ilya, and Welling, Max
    In Advances in Neural Information Processing Systems 20–22 jun 2016
  11. NeurIPS
    Semi-Supervised Learning with Deep Generative Models
    Kingma, Durk P, Mohamed, Shakir, Jimenez Rezende, Danilo, and Welling, Max
    In Advances in Neural Information Processing Systems 20–22 jun 2014
  12. ICLR
    Auto-Encoding Variational Bayes
    Kingma, Diederik P., and Welling, Max
    20–22 jun 2013
  13. ICML
    Bayesian learning via stochastic gradient langevin dynamics
    Welling, Max, and Teh, Yee Whye
    In Proceedings of the 28th International Conference on Machine Learning, ICML 2011 20–22 jun 2011