8 citations · 24 across the 14 of their papers we have counts for
4 papers · 1 filter
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…
Lale: Consistent Automated Machine Learning
Guillaume Baudart, Martin Hirzel, Kiran Kate +2
Automated machine learning makes it easier for data scientists to develop pipelines by searching over possible choices for hyperparameters, algorithms, and even pipeline topologies…
Mining Documentation to Extract Hyperparameter Schemas
Guillaume Baudart, Peter D. Kirchner, Martin Hirzel +1
AI automation tools need machine-readable hyperparameter schemas to define their search spaces. At the same time, AI libraries often come with good human-readable documentation. Wh…
Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic Programming
Guillaume Baudart, Javier Burroni, Martin Hirzel +2
Stan is a probabilistic programming language that is popular in the statistics community, with a high-level syntax for expressing probabilistic models. Stan differs by nature from…