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 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
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. |
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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. |
May 18, 2022 | Erik Bekkers has been named Lecturer of the Year for the FNWI. Congratulations to Erik for this fantastic achievement! |
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. |
Recent Publications
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ICLRBridge the Inference Gaps of Neural Processes via Expectation MaximizationIn International Conference on Learning Representations 2023
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ICLRSampling-Based Inference for Large Linear Models, with Application to Linearised LaplaceIn International Conference on Learning Representations 2023
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AISTATS
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AISTATSLearning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal EnsemblesIn Proceedings of The 26th International Conference on Artificial Intelligence and Statistics 2023
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IJCNNLogic-based AI for Interpretable Board Game Winner Prediction with Tsetlin MachineIn International Joint Conference on Neural Networks 2022
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NeurIPSLearning Expressive Meta-Representations with Mixture of Expert Neural ProcessesIn Advances in Neural Information Processing Systems 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