Zach Lipton

Assistant Professor, Machine Learning Department
Carnegie Mellon University

4:40 PM Presentation: The critical issues of interpretability: understanding what it really means?

  • Underspecified and unclear: have you appropriately defined your model and the properties it satisfies?
  • Understanding causal structure: validating the decisions your machine made
-How can you explain why a model did something? Why did the ML choose a certain course of action?
  • Resilience and robustness: The importance of factoring in domain adaptation and distribution shift
  • Anomaly detection: quantifying and actively compensating by modifying your beliefs and constraints

Check out the incredible speaker line-up to see who will be joining Zach.

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