83 citations · 175 across the 14 of their papers we have counts for
3 papers · 1 filter
Counterexample-Guided Learning of Monotonic Neural Networks
Aishwarya Sivaraman, Golnoosh Farnadi, Todd Millstein +1
The widespread adoption of deep learning is often attributed to its automatic feature construction with minimal inductive bias. However, in many real-world tasks, the learned funct…
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…