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JonathanWenger/README.md

Welcome!

I am a postdoctoral research scientist at Columbia University's Zuckerman Institute and I am interested in the connections between numerical analysis 💻 and probabilistic machine learning 🧠.

My research focuses on probabilistic numerics which aims to quantify uncertainty induced by finite computation or stochastic input by interpreting numerical methods as probabilistic inference.

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  1. probabilistic-numerics/probnum probabilistic-numerics/probnum Public

    Probabilistic Numerics in Python.

    Python 438 57

  2. cornellius-gp/gpytorch cornellius-gp/gpytorch Public

    A highly efficient implementation of Gaussian Processes in PyTorch

    Python 3.6k 560

  3. itergp itergp Public

    IterGP: Computation-Aware Gaussian Process Inference (NeurIPS 2022)

    Python 38 2

  4. pycalib pycalib Public

    Non-Parametric Calibration for Classification (AISTATS 2020)

    Python 18 9