Online, 20, 21 and 22 September 2021, 9am - 5pm CEST.

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There will be future editions - stay tuned on Twitter or ...

... join our Zulip stream on simulation-based inference! We are building a community open to both simulation scientists and machine learning methods researchers.

Goal

You use simulation in physics, economics, archaeology and want to find the simulator parameters that best fits the observations?

We teach a practical simulation-based inference workshop to help you understand recent machine learning techniques and apply them to your problem.

Apply to learn, have fun, and participate in a supportive community! (applications closed August 31, 2021).

Program


Instructors

We are a group of advanced users, package developers, and sbi researchers.

Álvaro Tejero-Cantero, mlcolab @ Tübingen University

Álvaro Tejero-Cantero, mlcolab @ Tübingen University

Michael Deistler, mackelab @ Tübingen University

Michael Deistler, mackelab @ Tübingen University

Jan Bölts, mackelab @ Tübingen University

Jan Bölts, mackelab @ Tübingen University

Pedro J. Gonçalves, mackelab @ caesar

Pedro J. Gonçalves, mackelab @ caesar

Jan-Matthis Lückmann, mackelab @ Tübingen University

Jan-Matthis Lückmann, mackelab @ Tübingen University

David Greenberg, Helmholtz Zentrum Hereon

David Greenberg, Helmholtz Zentrum Hereon

Organizers

Álvaro Tejero-Cantero, mlcolab @ Tübingen University

Álvaro Tejero-Cantero, mlcolab @ Tübingen University

Peter Steinbach, Helmholtz-Zentrum Dresden-Rossendorf

Peter Steinbach, Helmholtz-Zentrum Dresden-Rossendorf

Alex Gessner, mlcolab @ Tübingen University

Alex Gessner, mlcolab @ Tübingen University

Stefan Wezel, mlcolab @ Tübingen University

Stefan Wezel, mlcolab @ Tübingen University

Daniela Huppenkothen, SRON Netherlands Institute for Space Research

Daniela Huppenkothen, SRON Netherlands Institute for Space Research

Elena Sizana, mlcolab @ Tübingen University

Elena Sizana, mlcolab @ Tübingen University


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Cluster of excellence Machine Learning, Uni Tübingen

Cluster of excellence Machine Learning, Uni Tübingen

Code of conduct

To ensure a comfortable, supportive, and respectful environment, the workshop was run under a code of conduct. You can download it below.

SBI_Workshop-Code_of_Conduct.pdf

Any other Questions?

Our Zulip stream on simulation-based inference is open to both simulation scientists and machine learning methods researchers. Please write to Elena Sizana if you have trouble joining.