Synthesizing Pareto-optimal Interpretations of Black Box ML Models

Overview

Explore the synthesis of Pareto-optimal interpretations for black-box ML models, balancing explainability metrics with accuracy using MaxSAT solving techniques that provide PAC-style guarantees.

Syllabus

• Introduction to Explainability in Machine Learning
Importance of interpretability in ML models
Overview of black-box models
Trade-offs between accuracy and interpretability
• Basics of Pareto Optimality
Definition and examples of Pareto efficiency
Applications in multi-objective optimization
• Explainability Metrics
Common metrics for model interpretability
Quantitative vs qualitative interpretability
Techniques for measuring model explanation quality
• Introduction to MaxSAT
Basics of SAT (Boolean Satisfiability Problem)
Overview of MaxSAT and its applications
Mapping interpretability objectives to MaxSAT problems
• PAC Learning Framework
Fundamentals of Probably Approximately Correct (PAC) learning
Applying PAC guarantees in model interpretation
• Synthesis of Pareto-optimal Interpretations
Using MaxSAT to balance interpretability and accuracy
Constructing Pareto fronts in explainability contexts
Techniques for optimizing model explanations
• Practical Tools and Implementations
Introduction to tools and libraries for MaxSAT solving
Implementing Pareto-optimal solutions in Python
• Case Studies and Applications
Real-world examples of Paretian interpretations
Analyzing results of balancing metrics in model interpretations
• Evaluation of Interpretative Models
Methods to evaluate effectiveness of interpretations
Case-based evaluations of synthesized explanations
• Challenges and Future Directions
Current limitations in interpreting black-box models
Future research opportunities in synthesis of explanations
• Wrap-up and Course Summary
Review of key concepts learned
Discussion on the ethical implications of model interpretability

Subjects

Computer Science