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Arik Reuter

My general research interests are probabilistic machine learning and machine learning grounded in causal and statistical principles. My current research focuses on in-context learning for probabilistic and causal inference. This relates to the fields of Amortized Inference, Prior-data Fitted Networks, Simulation-Based Inference, Neural Processes, and Large Language Models.

I am a PhD student at the University of Cambridge and the Max Planck Institute for Intelligent Systems working with Bernhard Schölkopf, Miguel Hernandez-Lobato and Adrian Weller. My general research interests are probabilistic machine learning and machine learning grounded in causal and statistical principles. My current research focuses on in-context learning for probabilistic and causal inference. This relates to the fields of Amortized Inference, Prior-data Fitted Networks, Simulation-Based Inference, Neural Processes, and Large Language Models. You can find my most recent publications here: https://scholar.google.com/citations?user=ei6AssYAAAAJ&hl=en