07-28, 13:20–13:30 (UTC), Red
Every method defines a relation, which contains all the information we need to query possible values of any of the inputs or outputs given information on the others. This talk introduces parametric relational programming, which given a method M; information on any of M's variables, and a query set Q of variables of interest, compiles a new method M̂ that computes possible values of variables in Q. This unifies the forward and inverse execution (and everything in between) as forms of inference.
This talk should be of interest to people interested in any of:
- Compiler transformations
- Probabilistic programming
- Inference and machine learning
Zenna Tavares is a postdoctoral researcher at MIT under the supervision of Armando Solar Lezama. His interests are in probabilistic and causal inference, programming languages, and human-inspired artificial intelligence.