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UID:pretalx-euroscipy-2023-WBSYCM@pretalx.com
DTSTART;TZID=CET:20230814T153000
DTEND;TZID=CET:20230814T170000
DESCRIPTION:This tutorial will introduce how to train machine learning mode
 ls for time-to-event prediction tasks (health care\, predictive maintenanc
 e\, marketing\, insurance...) without introducing a bias from censored tra
 ining (and evaluation) data.
DTSTAMP:20260312T231758Z
LOCATION:Aula
SUMMARY:Predictive survival analysis with scikit-learn\, scikit-survival an
 d lifelines - Olivier Grisel\, Vincent Maladiere
URL:https://pretalx.com/euroscipy-2023/talk/WBSYCM/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-euroscipy-2023-CB9WMH@pretalx.com
DTSTART;TZID=CET:20230817T103000
DTEND;TZID=CET:20230817T120000
DESCRIPTION:This slot will cover the effort regarding interoperability in t
 he scientific Python ecosystem. Topics:\n\n- Using the Array API for array
 -producing and array-consuming libraries\n- DataFrame interchange and name
 space APIs\n- Apache Arrow: connecting and accelerating dataframe librarie
 s across the PyData ecosystem\n- Entry Points: Enabling backends and plugi
 ns for your libraries\n\n### Using the Array API for array-producing and a
 rray-consuming libraries\n\nAlready using the Array API or wondering if yo
 u should in a project you maintain? Join this maintainer track session to 
 share your experience and exchange knowledge and tips around building arra
 y libraries that implement the standard or libraries that consume arrays.\
 n\n### DataFrame-agnostic code using the DataFrame API standard\n\nThe Dat
 aFrame Standard provides you with a minimal\, strict\, and predictable API
 \, to write code that will work regardless of whether the caller uses pand
 as\, polars\, or some other library.\n\n### DataFrame Interchange protocol
  and Apache Arrow\n\nThe DataFrame interchange protocol and Arrow C Data i
 nterface are two ways to interchange data between dataframe libraries. Wha
 t are the challenges and requirements that maintainers encounter when inte
 grating this into consuming libraries?\n\n### Entry Points: Enabling backe
 nds and plugins for your libraries\n\nIn this talk\, we will discuss how N
 etworkX used entry points to enable more efficient computation backends to
  plug into NetworkX
DTSTAMP:20260312T231758Z
LOCATION:HS 119 - Maintainer track
SUMMARY:Interoperability in the Scientific Python Ecosystem - Joris Van den
  Bossche\, Tim Head\, Olivier Grisel\, Franck Charras\, Mridul Seth\, Seba
 stian Berg
URL:https://pretalx.com/euroscipy-2023/talk/CB9WMH/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-euroscipy-2023-UVBBQZ@pretalx.com
DTSTART;TZID=CET:20230817T153000
DTEND;TZID=CET:20230817T160000
DESCRIPTION:Could scikit-learn future be GPU-powered ? This talk will discu
 ss the performance improvements that GPU computing could bring to existing
  scikit-learn algorithms\, and will describe a plugin-based design that is
  being foresighted to open-up scikit-learn compatibility to faster compute
  backends\, with special concern for user-friendliness\, ease of installat
 ion\, and interoperability.
DTSTAMP:20260312T231758Z
LOCATION:Aula
SUMMARY:Exploring GPU-powered backends for scikit-learn - Olivier Grisel\, 
 Franck Charras
URL:https://pretalx.com/euroscipy-2023/talk/UVBBQZ/
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