Interpretable Machine Learning via Program Synthesis - IPAM at UCLA
Offered By: Institute for Pure & Applied Mathematics (IPAM) via YouTube
Course Description
Overview
Syllabus
Intro
What is Interpretability?
RNA Splicing Mechanism
RNA Splice Prediction
Control: Parallel Parking
Learning Interpretable Models
Program Synthesis for Interpretable ML
Video Trajectory Queries
Control & Reinforcement Learning
Deep Reinforcement Learning
Imitation Learning
Dataset Aggregation (DAgger)
Our Approach: Leverage the Q-Function
Viper Algorithm
Verifying Correctness of a Toy Pong Controller
Learning State Machine Policies
Teacher Policy
Interpretability of State Machine Policies
Example: Single Group
Multi-Agent Reinforcement Learning
Transformer Communication Graph
Neurosymbolic Transformers
Learning Algorithm
Programmatic Attention Rules
Sparse Communication Structure
Modular Networks for RNA Splicing
Conclusion
Taught by
Institute for Pure & Applied Mathematics (IPAM)
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