Interpretable Machine Learning via Program Synthesis - IPAM at UCLA

Interpretable Machine Learning via Program Synthesis - IPAM at UCLA

Institute for Pure & Applied Mathematics (IPAM) via YouTube Direct link

Intro

1 of 27

1 of 27

Intro

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Interpretable Machine Learning via Program Synthesis - IPAM at UCLA

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  1. 1 Intro
  2. 2 What is Interpretability?
  3. 3 RNA Splicing Mechanism
  4. 4 RNA Splice Prediction
  5. 5 Control: Parallel Parking
  6. 6 Learning Interpretable Models
  7. 7 Program Synthesis for Interpretable ML
  8. 8 Video Trajectory Queries
  9. 9 Control & Reinforcement Learning
  10. 10 Deep Reinforcement Learning
  11. 11 Imitation Learning
  12. 12 Dataset Aggregation (DAgger)
  13. 13 Our Approach: Leverage the Q-Function
  14. 14 Viper Algorithm
  15. 15 Verifying Correctness of a Toy Pong Controller
  16. 16 Learning State Machine Policies
  17. 17 Teacher Policy
  18. 18 Interpretability of State Machine Policies
  19. 19 Example: Single Group
  20. 20 Multi-Agent Reinforcement Learning
  21. 21 Transformer Communication Graph
  22. 22 Neurosymbolic Transformers
  23. 23 Learning Algorithm
  24. 24 Programmatic Attention Rules
  25. 25 Sparse Communication Structure
  26. 26 Modular Networks for RNA Splicing
  27. 27 Conclusion

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