11 citations · 17 across the 4 of their papers we have counts for
5 papers
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…
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…
Generating and Sampling Orbits for Lifted Probabilistic Inference
Steven Holtzen, Todd Millstein, Guy Van den Broeck
A key goal in the design of probabilistic inference algorithms is identifying and exploiting properties of the distribution that make inference tractable. Lifted inference algorith…