Self-adjusting computation enables writing programs that can automatically and efficiently respond to changes to their data (e.g., inputs). The idea behind the approach is to stor...
Inductive learning of first-order theory based on examples has serious bottleneck in the enormous hypothesis search space needed, making existing learning approaches perform poorl...
Useful type inference must be faster than normalization. Otherwise, you could check safety conditions by running the program. We analyze the relationship between bounds on normali...
We present an automated technique for generating compiler optimizations from examples of concrete programs before and after improvements have been made to them. The key technical ...
Systems programs rely on fine-grain control of data representation and use of state to achieve performance, conformance to hardware specification, and temporal predictability. T...