• 11:00-12:30 Session on learning interpretable ML models
    • Peter Stuckey. Building Optimal Decision Trees
    • Andre Schidler. SAT-Based Induction of Explainable Decision Trees
  • 12:30-12:40 Spotlight talk: Miki Hermann. MCP: Capturing Big Data by Satisfiability
  • 14:00-15:30 Session on computation of explanations for black-box ML models
    • Ron Levie. A Rate-Distortion Framework for Explaining Model Decisions and Application to CartoonX
    • Bernardo Subercaseaux. The Exciting Theory of Formal Explanations
  • 16:00-17:30 Session on verification of black-box ML models
    • Nina Narodytska. Verification of Binarized Neural Networks: Challenges and Opportunities
    • Mark Niklas Müller. Verification of Realistic Neural Networks
  • 17:30-18:00 Discussion and outlook

A detailed program with abstracts can be found here