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]