11 citations · 17 across the 5 of their papers we have counts for
4 papers · 1 filter
Scaling Exact Inference for Discrete Probabilistic Programs
Steven Holtzen, Guy Van den Broeck, Todd Millstein
Probabilistic programming languages (PPLs) are an expressive means of representing and reasoning about probabilistic models. The computational challenge of probabilistic inference…
Data-Driven Inference of Representation Invariants
Anders Miltner, Saswat Padhi, Todd Millstein +1
A representation invariant is a property that holds of all values of abstract type produced by a module. Representation invariants play important roles in software engineering and…
Overfitting in Synthesis: Theory and Practice (Extended Version)
Saswat Padhi, Todd Millstein, Aditya Nori +1
In syntax-guided synthesis (SyGuS), a synthesizer's goal is to automatically generate a program belonging to a grammar of possible implementations that meets a logical specificatio…
Symbolic Exact Inference for Discrete Probabilistic Programs
Steven Holtzen, Todd Millstein, Guy Van den Broeck
The computational burden of probabilistic inference remains a hurdle for applying probabilistic programming languages to practical problems of interest. In this work, we provide a…