Events at The University of Manchester
  • University home
  • Events
  • Home
  • Exhibitions
  • Conferences
  • Lectures and seminars
  • Performances
  • Events for prospective students
  • Sustainability events
  • Family events
  • All Events

AI-Fun with ELLIS Seminar | Yusuf Sale: Online Selective Conformal Inference: Errors and Solutions

AI FUN & ELLIS
Dates:25 March 2025
Times:14:00 - 15:00
What is it:Seminar
Organiser:Faculty of Science and Engineering
Who is it for:University staff, External researchers, Alumni, Current University students
Speaker:Yusuf Sale
See travel and contact information
Add to your calendar

More information

  • AI Fundamentals Website
  • Online registration link

Other events

  • In category "Seminar"
  • In group "(DF) Data Science and AI"
  • By Faculty of Science and Engineering

Title: Online Selective Conformal Inference: Errors and Solutions

Abstract: Conformal inference provides a principled way to construct prediction intervals with guaranteed coverage. However, in online selective conformal inference, where predictions are made only when a selection rule is met, things become trickier — selections can break the exchangeability of the data, leading to incorrect coverage guarantees.

In this talk, I will critically examine existing calibration selection strategies used to restore exchangeability and highlight some fundamental errors in their theoretical claims regarding selection-conditional coverage and false-coverage statement rate (FCR) control. To address these shortcomings, I will introduce new calibration selection strategies that maintain exchangeability, ensuring both valid selection-conditional coverage and FCR control. Beyond the theory, I will discuss practical trade-offs between different approaches and present experimental results that illustrate their effectiveness.

Bio: Yusuf Sale is a final-year PhD student at the Chair of Artificial Intelligence and Machine Learning (AIML) at LMU Munich, supervised by Prof. Eyke Hüllermeier. He is a member of the Konrad Zuse School of Excellence in Reliable AI (relAI), an initiative supported by the Federal Ministry of Education and Research.

His research focuses on uncertainty quantification in machine learning, with a particular emphasis on the representation and quantification of different types of uncertainty (viz. aleatoric, and epistemic uncertainty). His work aims to develop reliable and trustworthy AI and ML systems that effectively account for inherent uncertainties in their predictions, thereby enhancing decision-making across a wide range of (high-stake) applications. Moreover, he is interested in distribution-free uncertainty quantification, exploring both its theoretical foundations and practical applications.

  • -

As ever, in-person attendance is encouraged but if you are unable to attend in person, you can register (link on this page) to receive a link to join online.

Speaker

Yusuf Sale

Organisation: LMU Munich

  • https://yusufsale.com/

Travel and Contact Information

Find event

6.210
University Place
Manchester

Contact event

Centre for AI Fundamentals

ai-fun@manchester.ac.uk

Contact us

  • +44 (0) 161 306 6000

Find us

The University of Manchester
Oxford Rd
Manchester
M13 9PL
UK

Connect with the University

  • Facebook page for The University of Manchester
  • X (formerly Twitter) page for The University of Manchester
  • YouTube page for The University of Manchester
  • Instagram page for The University of Manchester
  • TikTok page for The University of Manchester
  • LinkedIn page for The University of Manchester

  • Privacy /
  • Copyright notice /
  • Accessibility /
  • Freedom of information /
  • Charitable status /
  • Royal Charter Number: RC000797
  • Close menu
  • Home
    • Featured events
    • Today's events
    • The Whitworth events
    • Manchester Museum events
    • Jodrell Bank Discovery Centre events
    • Martin Harris Centre events
    • The John Rylands Library events
    • Exhibitions
    • Conferences
    • Lectures and seminars
    • Performances
    • Events for prospective students
    • Sustainability events
    • Family events
    • All events