Guided Sketch-Based Program Induction by Search Gradients
Ahmad Ayaz Amin
Many tasks can be easily solved using machine learning techniques. However,
some tasks cannot readily be solved using statistical models, requiring a
symbolic approach instead. Program induction is one of the ways that such tasks
can be solved by means of capturing an interpretable and generalizable
algorithm through training. However, contemporary approaches to program
induction are not sophisticated enough to readily be applied to various types
of tasks as they tend to be formulated as a single, all-encompassing model,
usually parameterized by neural networks. In an attempt to make program
induction a viable solution for many scenarios, we propose a framework for
learning parameterized programs via search gradients using evolution
strategies. This formulation departs from traditional program induction as it
allows for the programmer to impart task-specific code to the program 'sketch',
while also enjoying the benefits of accelerated learning through end-to-end
gradient-based optimization.