83 citations · 175 across the 14 of their papers we have counts for
16 papers
Bit Blasting Probabilistic Programs
Poorva Garg, Steven Holtzen, Guy Van den Broeck +1
Probabilistic programming languages (PPLs) are expressive means for creating and reasoning about probabilistic models. Unfortunately hybrid probabilistic programs, involving both c…
Scaling Integer Arithmetic in Probabilistic Programs
William X. Cao, Poorva Garg, Ryan Tjoa +3
Distributions on integers are ubiquitous in probabilistic modeling but remain challenging for many of today's probabilistic programming languages (PPLs). The core challenge comes f…
What do LLMs need to Synthesize Correct Router Configurations?
Rajdeep Mondal, Alan Tang, Ryan Beckett +2
We investigate whether Large Language Models (e.g., GPT-4) can synthesize correct router configurations with reduced manual effort. We find GPT-4 works very badly by itself, produc…
LIGHTYEAR: Using Modularity to Scale BGP Control Plane Verification
Alan Tang, Ryan Beckett, Steven Benaloh +4
Current network control plane verification tools cannot scale to large networks, because of the complexity of jointly reasoning about the behaviors of all nodes in the network. In…
flip-hoisting: Exploiting Repeated Parameters in Discrete Probabilistic Programs
Ellie Y. Cheng, Todd Millstein, Guy Van den Broeck +1
Many of today's probabilistic programming languages (PPLs) have brittle inference performance: the performance of the underlying inference algorithm is very sensitive to the precis…
Model Checking Finite-Horizon Markov Chains with Probabilistic Inference
Steven Holtzen, Sebastian Junges, Marcell Vazquez-Chanlatte +3
We revisit the symbolic verification of Markov chains with respect to finite horizon reachability properties. The prevalent approach iteratively computes step-bounded state reachab…