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 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. |
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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.
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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. |
May 18, 2022 | Erik Bekkers has been named Lecturer of the Year for the FNWI. Congratulations to Erik for this fantastic achievement! |
Recent Publications
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EMNLPCHiLL: Zero-shot Custom Interpretable Feature Extraction from Clinical Notes with Large Language ModelsIn The 2023 Conference on Empirical Methods in Natural Language Processing (to appear) 2023
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EMNLPAligning Predictive Uncertainty with Clarification Questions in Grounded DialogIn The 2023 Conference on Empirical Methods in Natural Language Processing (to appear) Dec 2023
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NeurIPSImplicit Neural Convolutional Kernels for Steerable CNNsIn Thirty-seventh Conference on Neural Information Processing Systems (to appear) Dec 2023
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NeurIPSThe Memory-Perturbation Equation: Understanding Model’s Sensitivity to DataIn Thirty-seventh Conference on Neural Information Processing Systems (to appear) Dec 2023
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NeurIPSFlow Factorzied Representation LearningIn Thirty-seventh Conference on Neural Information Processing Systems (to appear) Dec 2023
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NeurIPSRotating Features for Object DiscoveryIn Thirty-seventh Conference on Neural Information Processing Systems (to appear) Dec 2023
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NeurIPSLatent Field Discovery in Interacting Dynamical Systems with Neural FieldsIn Thirty-seventh Conference on Neural Information Processing Systems (to appear) Dec 2023
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NeurIPSTowards Anytime Classification in Early-Exit Architectures by Enforcing Conditional MonotonicityIn Thirty-seventh Conference on Neural Information Processing Systems (to appear) Dec 2023
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NeurIPSLearning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement LearningIn Thirty-seventh Conference on Neural Information Processing Systems (to appear) Dec 2023
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NeurIPSInvariant Neural Ordinary Differential EquationsIn Thirty-seventh Conference on Neural Information Processing Systems (to appear) Dec 2023
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NeurIPSClifford group equivariant neural networksIn Thirty-seventh Conference on Neural Information Processing Systems (to appear) Dec 2023
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NeurIPSDeep Gaussian Markov Random Fields for Graph-Structured Dynamical SystemsIn Thirty-seventh Conference on Neural Information Processing Systems (to appear) Dec 2023
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NeurIPSPractical and Asymptotically Exact Conditional Sampling in Diffusion ModelsIn Thirty-seventh Conference on Neural Information Processing Systems (to appear) Dec 2023
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NeurIPSTopological Obstructions and How to Avoid ThemIn Thirty-seventh Conference on Neural Information Processing Systems (to appear) Dec 2023
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CoRLOne-shot Imitation Learning via Interaction WarpingIn 7th Annual Conference on Robot Learning Nov 2023
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UAIExploiting Inferential Structure in Neural ProcessesIn The 39th Conference on Uncertainty in Artificial Intelligence Aug 2023
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ICLRBridge the Inference Gaps of Neural Processes via Expectation MaximizationIn International Conference on Learning Representations Apr 2023
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ICLRSampling-Based Inference for Large Linear Models, with Application to Linearised LaplaceIn International Conference on Learning Representations Apr 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 Apr 2023
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NeurIPS
WorkshopActive Learning Policies for Solving Inverse ProblemsIn Neural Information Processing Systems Workshop on Adaptive Experimental Design and Active Learning in the Real World Dec 2023 -
NeurIPS
WorkshopSwitching policies for solving inverse problemsIn Neural Information Processing Systems Workshop on Deep Learning and Inverse Problems Dec 2023 -
IJCNNLogic-based AI for Interpretable Board Game Winner Prediction with Tsetlin MachineIn International Joint Conference on Neural Networks Nov 2022
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NeurIPSLearning Expressive Meta-Representations with Mixture of Expert Neural ProcessesIn Advances in Neural Information Processing Systems Nov 2022
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IJCAIValue Refinement Network (VRN)In International Joint Conference on Artificial Intelligence Nov 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