Existing approaches to interpretability largely focus on fixed model families (e.g., decision trees, sparse linear models, etc.) over features. Program synthesis offers a powerful alternative: rather than use an existing model family, the user can define new, custom model families tailored to their problem via a domain-specific programming language (DSL). In particular, a DSL describes constructs (e.g., if-then-else statements, while loops, etc.) that be composed together to create complex programs, which form the model family. I will describe our work on leveraging program synthesis to learn interpretable control policies, as well as ongoing work using these ideas to train interpretable models for RNA splice prediction.
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