AMLab | Amsterdam Machine Learning Lab

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. Max Welling and Jan-Willem van de Meent serve as co-directors. Herke van Hoof, Patrick Forré, Eric Nalisnick, Erik Bekkers, and Christian Naesseth 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
May 18, 2022 | Erik Bekkers has been named Lecturer of the Year for the FNWI. Congratulations to Erik for this fantastic achievement! |
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May 16, 2022 | In addition to hiring for PhD positions, Jan-Willem van de Meent and Eric Nalisnick have a Postdoc opening as part of the UvA Bosch Delta Lab. Deadline for applications is June 10. Apply here! |
May 5, 2022 | Jan-Willem van de Meent and Eric Nalisnick are hiring for multiple PhD positions as part of our collaboration with the Bosch Center for Artificial Intelligence. Deadline is June 6. Apply here! |
Mar 18, 2022 | We are delighted to announce that we have renewed our collaboration with Bosch through the Delta Lab! Over the next 4 years, this lab will fund 10 students and postdocs, who will be advised by Eric Nalisnick, Jan-Willem van de Meent, Max Welling, and Theo Gevers. More information in this press release. |
Mar 7, 2022 | Christian Naesseth has an open position for a PhD student to work on topics such as approximate statistical inference, causal inference, and generalization. Deadline for applications is April 10. Apply here. |
Jan 25, 2022 | AMLab will be presenting 6 papers at ICLR 2022. Please see our blog for a full list. |
Dec 17, 2021 | We have two VENI awardees at AMLab this year! Eric Nalisnick has received the award for his work on continual learning under human guidance. Jamie Townsend will join AMLab to work on neural lossless compression. |
Recent Publications
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ICMLAdapting the Linearised Laplace Model Evidence for Modern Deep LearningIn Proceedings of the 39th International Conference on Machine Learning 2022
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ICMLModel-based Meta Reinforcement Learning using Graph Structured Surrogate Models and Amortized Policy SearchIn International Conference on Machine Learning 2022
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IJCAIValue Refinement Network (VRN)In International Joint Conference on Artificial Intelligence Jul 2022
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UAIVariational combinatorial sequential Monte Carlo methods for Bayesian phylogenetic inferenceIn Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence 27–30 jul 2021
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ICMLBayesian Deep Learning via Subnetwork InferenceIn Proceedings of the 38th International Conference on Machine Learning 18–24 jul 2021
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JMLRNormalizing Flows for Probabilistic Modeling and InferenceJournal of Machine Learning Research 18–24 jul 2021
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JMSOptimizing Adaptive Notifications in Mobile Health Interventions Systems: Reinforcement Learning from a Data-driven Behavioral SimulatorJournal of Medical Systems 18–24 jul 2021