Resources

Invited Talks

  • Grounding deep models with uncertainty quantification [Slides]
  • Designing calibrated uncertainty estimators for deep models, DLCT (ML Collective) [Slides]
  • Improving reliability and interpretability of AI models in clinical diagnosis, LLNL DSI AI in Healthcare Workshop [Slides]
  • Building reliable and generalizable deep models using prediction calibration [Slides]
  • Thoughts on building machine learning models with scientific data [Slides]
  • Explaining real-word shifts and explaining under shifts [Slides]
  • Understanding behavior of clinical models under domain shifts [Slides]
  • Tutorial on unsupervised learning [Part 1] [Part 2] [Part 3]
  • Teaching machines to be intelligent: Pitfalls, Challenges and Opportunities [Slides]