3 papers
cs.LG2026
PPDL: LLM-Based Flows as Probabilistic Programs
Louis Mandel, Guillaume Baudart, Mandana Vaziri +1
Building reliable applications that leverage large language models (LLMs) remains a significant challenge. While LLMs offer impressive capabilities across diverse tasks, their outp…
cs.PL2026
PoTo: A Hybrid Andersen's Points-to Analysis for Python
Ingkarat Rak-amnouykit, Ana Milanova, Guillaume Baudart +2
As Python is increasingly being adopted for large and complex programs, the importance of static analysis for Python (such as type inference) grows. Unfortunately, static analysis…
cs.PL2024
Inference Plans for Hybrid Particle Filtering
Ellie Y. Cheng, Eric Atkinson, Guillaume Baudart +2
Advanced probabilistic programming languages (PPLs) using hybrid particle filtering combine symbolic exact inference and Monte Carlo methods to improve inference performance. These…