3 citations · 6 across the 4 of their papers we have counts for
6 papers
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…