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 8 faculty. Max Welling and Jan-Willem van de Meent serve as co-directors. Herke van Hoof, Patrick Forré, Eric Nalisnick, Erik Bekkers, Christian Naesseth, and Sara Magliacane serve as tenure-track faculty. The lab participates in 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

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.
Nov 27, 2023 We are looking for a PhD candidate to work on fundamental questions around AI methods supporting human operators in critical infrastructure. You will be working with Herke van Hoof.The application deadline is January 16th. 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.
Apr 5, 2023 Herke van Hoof and Annette ten Teije have an open position on “Learning and reasoning for medical decision making”. Deadline for applications is May 1st. For all details and how to apply, please see the official vacancy.
Apr 3, 2023 AMLab members will be (co-)supervising 3 new postdoc positions on AI for Meta-materials, which are part of the AI for Sustainable Molecules and Materials program. The deadline for applications has been extended to 10 June. Apply here.
Nov 17, 2022 Max Welling, Jan-Willem van de Meent and Alfons Hoekstra have a PhD opening on Learning PDEs. Deadline for applications is 16 December. Apply here.
Oct 25, 2022 Dr. Sara Magliacane is moving from the INDElab to join AMLab as a tenure-track assistant professor. This move strengthens the collaboration between the two groups under the ELLIS umbrella. She will continue working on causality and applications of causality to machine learning. Welcome to Sara!
Sep 5, 2022 Sindy Löwe received Google PhD Fellowship! What a wonderful acknowledgement of her as a top ML researcher!
Jul 19, 2022 AMLab will be presenting 8 papers at ICML 2022! Please see our blog for a full list.

Recent Publications

  1. 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) 2024
  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
    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 (to appear) Dec 2023
  6. 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
  7. NeurIPS
    Rotating Features for Object Discovery
    In Thirty-seventh Conference on Neural Information Processing Systems (to appear) Dec 2023
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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
  43. ICLR
    Self-Supervised Inference in State-Space Models
    Ruhe, David, and Forré, Patrick
    In International Conference on Learning Representations Nov 2022
  44. 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
  45. ICLR
    Multi-Agent MDP Homomorphic Networks
    Pol, Elise, Hoof, Herke, Oliehoek, Frans, and Welling, Max
    In International Conference on Learning Representations Nov 2022
  46. 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
  47. 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
  48. IJCAI
    Value Refinement Network (VRN)
    Wöhlke, Jan, Schmitt, Felix, and Hoof, Herke
    In International Joint Conference on Artificial Intelligence Nov 2022
  49. 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
  50. 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