I am a PhD student at the Max Planck ETH Center for Learning Systems advised by Gunnar Rätsch (ETH Zürich) and Bernhard Schölkopf (MPI-IS Tübingen).

My research focuses on probabilistic machine learning and data science.

Currently, I am particularly interested in approximate inference for neural networks and biomedical applications. I want to design algorithms that can incorporate prior knowledge, quantify uncertainty, and automatically select the most likely model given data. Especially in the context of neural networks, these problems remain challenging. I have previously worked on methods for time series, e.g., mobility and political data.

Publications

Continual Deep Learning by Functional Regularisation of Memorable Past

P Pan, S Swaroop, A Immer, R Eschenhagen, R Turner, ME Khan

NeurIPS 2020 (oral), 2020

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Sub-Matrix Factorization for Real-Time Vote Prediction

A Immer, V Kristof, M Grossglauser, P Thiran

KDD (oral), 2020

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Improving predictions of Bayesian neural networks via local linearization

A Immer, M Korzepa, M Bauer

ICML Uncertainty & Robustness in Deep Learning workshop, 2020

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Disentangling the Gauss-Newton Method and Approximate Inference for Neural Networks

A Immer

EPFL MSc Thesis, 2020

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Approximate Inference Turns Deep Networks into Gaussian Processes

ME Khan, A Immer, E Abedi, M Korzepa

NeurIPS, 2019

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Efficient Learning of Smooth Probability Functions from Bernoulli Tests with Guarantees

P Rolland, A Kavis, A Immer, A Singla, V Cevher

ICML, 2019

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Variational Inference with Numerical Derivatives: Variance Reduction through Coupling

A Immer, GP Dehaene

Preprint, 2019

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