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UID:pretalx-pyconde-pydata-2025-TMBTYH@pretalx.com
DTSTART;TZID=CET:20250424T101500
DTEND;TZID=CET:20250424T114500
DESCRIPTION:From coffee machine settings to chemical reactions to website A
 B testing - iterative make-test-learn cycles are ubiquitous. The [Bayesian
  Back End](https://emdgroup.github.io/baybe/stable/) (BayBE) is an open-so
 urce experimental planner enabling users to smartly navigate such black-bo
 x optimization problems in iterative settings. This tutorial will i) intro
 duce the core concepts enabled by combining Bayesian optimization and mach
 ine learning\; ii) explain our software design choices\, robust tests and 
 open-source libraries this is built on\; and iii) provide a short practica
 l hands-on session.
DTSTAMP:20260413T211502Z
LOCATION:Ferrum
SUMMARY:BayBE: A Bayesian Back End for Experimental Planning in the Low-To-
 No-Data Regime - Martin Fitzner\, Alexander Hopp\, Adrian Šošić
URL:https://pretalx.com/pyconde-pydata-2025/talk/TMBTYH/
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