YoVDO

Interpretable Machine Learning via Program Synthesis - IPAM at UCLA

Offered By: Institute for Pure & Applied Mathematics (IPAM) via YouTube

Tags

Interpretable Machine Learning Courses Reinforcement Learning Courses Imitation Learning Courses Program Synthesis Courses

Course Description

Overview

Explore a lecture on interpretable machine learning through program synthesis presented by Osbert Bastani at IPAM's Explainable AI for the Sciences workshop. Delve into novel approaches for creating custom model families using domain-specific programming languages, moving beyond traditional fixed model families. Discover applications in learning interpretable control policies and RNA splice prediction. Examine topics such as parallel parking control, video trajectory queries, deep reinforcement learning, and multi-agent reinforcement learning. Investigate the Viper algorithm, state machine policies, and neurosymbolic transformers. Gain insights into programmatic attention rules, sparse communication structures, and modular networks for RNA splicing in this comprehensive exploration of cutting-edge interpretable machine learning techniques.

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)

Related Courses

Stanford Seminar - Concepts and Questions as Programs
Stanford University via YouTube
DreamCoder- Growing Generalizable, Interpretable Knowledge With Wake-Sleep Bayesian Program Learning
Yannic Kilcher via YouTube
A Neural Network Solves and Generates Mathematics Problems by Program Synthesis - Paper Explained
Aleksa Gordić - The AI Epiphany via YouTube
EI Seminar - Recent Papers in Embodied Intelligence
Massachusetts Institute of Technology via YouTube
Using Program Synthesis to Build Compilers
Simons Institute via YouTube