83 citations · 108 across the 14 of their papers we have counts for
15 papers
Multi-Language Probabilistic Programming
Sam Stites, John M. Li, Steven Holtzen
There are many different probabilistic programming languages that are specialized to specific kinds of probabilistic programs. From a usability and scalability perspective, this is…
A Nominal Approach to Probabilistic Separation Logic
John M. Li, Jon Aytac, Philip Johnson-Freyd +2
Currently, there is a gap between the tools used by probability theorists and those used in formal reasoning about probabilistic programs. On the one hand, a probability theorist d…
Synthesizing Tight Privacy and Accuracy Bounds via Weighted Model Counting
Lisa Oakley, Steven Holtzen, Alina Oprea
Programmatically generating tight differential privacy (DP) bounds is a hard problem. Two core challenges are (1) finding expressive, compact, and efficient encodings of the distri…
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…
Lilac: A Modal Separation Logic for Conditional Probability
John M. Li, Amal Ahmed, Steven Holtzen
We present Lilac, a separation logic for reasoning about probabilistic programs where separating conjunction captures probabilistic independence. Inspired by an analogy with mutabl…
Type Prediction With Program Decomposition and Fill-in-the-Type Training
Federico Cassano, Ming-Ho Yee, Noah Shinn +2
TypeScript and Python are two programming languages that support optional type annotations, which are useful but tedious to introduce and maintain. This has motivated automated typ…
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…
Logical Abstractions for Noisy Variational Quantum Algorithm Simulation
Yipeng Huang, Steven Holtzen, Todd Millstein +2
Due to the unreliability and limited capacity of existing quantum computer prototypes, quantum circuit simulation continues to be a vital tool for validating next generation quantu…
On the Relationship Between Probabilistic Circuits and Determinantal Point Processes
Honghua Zhang, Steven Holtzen, Guy Van den Broeck
Scaling probabilistic models to large realistic problems and datasets is a key challenge in machine learning. Central to this effort is the development of tractable probabilistic m…
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…
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…
Probabilistic Program Abstractions
Steven Holtzen, Todd Millstein, Guy Van den Broeck
Abstraction is a fundamental tool for reasoning about complex systems. Program abstraction has been utilized to great effect for analyzing deterministic programs. At the heart of p…