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Mathematical Sciences Research Institute

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Workshop

Hot Topics: Foundations of Stable, Generalizable and Transferable Statistical Learning March 07, 2022 - March 11, 2022
Parent Program: --
Series: Hot Topic, Hot Topic
Location: MSRI: Simons Auditorium, Atrium
Organizers LEAD Peter B├╝hlmann (ETH Zurich), John Duchi (Stanford University), Elizabeth Tipton (Northwestern University), Bin Yu (University of California, Berkeley)
Speaker(s)

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Description
Image
When data automatically drop from the sky: intelligent approaches in data science change the way humans and computers interact. (Illustration: Niklas Briner)
Despite the remarkable success in extracting information from complex and (often) large-scale datasets over the last two decades, further progress is needed to making automated statistical and machine learning algorithms more reliable, robust, interpretable and trustworthy. This workshop has its focus on foundational aspects of this goal, linking areas at the interface between statistics, optimization, machine learning and computer science, such as distributional robustness and stability, adversarial and transfer learning, generalizability and meta analysis, and causality.
Keywords and Mathematics Subject Classification (MSC)
Tags/Keywords
  • Artificial Intelligence

  • data science

  • machine learning

  • optimization

  • Stochastic Modeling

  • statistics

Primary Mathematics Subject Classification
Secondary Mathematics Subject Classification No Secondary AMS MSC