Jonathan Wenger

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I am a postdoctoral research scientist in Columbia University’s Department of Statistics and at the Zuckerman Institute working with John P. Cunningham.

My research focuses on resource-efficient methods for large-scale probabilistic machine learning. Much of my work views numerical algorithms through the lens of probabilistic inference. This perspective enables the acceleration of learning algorithms via an explicit trade-off between computational efficiency and predictive precision.

I received my PhD in Computer Science from the University of Tübingen advised by Philipp Hennig and I was an IMPRS-IS fellow at the Max-Planck Institute for Intelligent Systems.

Selected Publications

  1. cagp_model_selection.png
    Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference
    Jonathan Wenger, Kaiwen Wu, Philipp Hennig, Jacob R. Gardner, Geoff Pleiss, and John P. Cunningham
    In Advances in Neural Information Processing Systems (NeurIPS), 2024
  2. posterior_computational_uncertainty.png
    Posterior and Computational Uncertainty in Gaussian Processes
    Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, and John P. Cunningham
    In Advances in Neural Information Processing Systems (NeurIPS), 2022
  3. nonparametric_calibration.png
    Non-Parametric Calibration for Classification
    Jonathan Wenger, Hedvig Kjellström, and Rudolph Triebel
    In International Conference on Artificial Intelligence and Statistics (AISTATS), 2020