8 citations · 24 across the 14 of their papers we have counts for
17 papers
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…
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…
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…
Density-Based Semantics for Reactive Probabilistic Programming
Guillaume Baudart, Louis Mandel, Christine Tasson
Synchronous languages are now a standard industry tool for critical embedded systems. Designers write high-level specifications by composing streams of values using block diagrams.…
Verifying Performance Properties of Probabilistic Inference
Eric Atkinson, Ellie Y. Cheng, Guillaume Baudart +2
In this extended abstract, we discuss the opportunity to formally verify that inference systems for probabilistic programming guarantee good performance. In particular, we focus on…
Semi-Symbolic Inference for Efficient Streaming Probabilistic Programming
Eric Atkinson, Charles Yuan, Guillaume Baudart +2
Efficient inference is often possible in a streaming context using Rao-Blackwellized particle filters (RBPFs), which exactly solve inference problems when possible and fall back on…
Automatic Guide Generation for Stan via NumPyro
Guillaume Baudart, Louis Mandel
Stan is a very popular probabilistic language with a state-of-the-art HMC sampler but it only offers a limited choice of algorithms for black-box variational inference. In this pap…
Statically Bounded-Memory Delayed Sampling for Probabilistic Streams
Eric Atkinson, Guillaume Baudart, Louis Mandel +2
Probabilistic programming languages aid developers performing Bayesian inference. These languages provide programming constructs and tools for probabilistic modeling and automated…
Learning GraphQL Query Costs (Extended Version)
Georgios Mavroudeas, Guillaume Baudart, Alan Cha +5
GraphQL is a query language for APIs and a runtime for executing those queries, fetching the requested data from existing microservices, REST APIs, databases, or other sources. Its…
A Principled Approach to GraphQL Query Cost Analysis
Alan Cha, Erik Wittern, Guillaume Baudart +3
The landscape of web APIs is evolving to meet new client requirements and to facilitate how providers fulfill them. A recent web API model is GraphQL, which is both a query languag…
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…
Reactive Probabilistic Programming
Guillaume Baudart, Louis Mandel, Eric Atkinson +3
Synchronous modeling is at the heart of programming languages like Lustre, Esterel, or Scade used routinely for implementing safety critical control software, e.g., fly-by-wire and…
An Empirical Study of GraphQL Schemas
Erik Wittern, Alan Cha, James C. Davis +2
GraphQL is a query language for APIs and a runtime to execute queries. Using GraphQL queries, clients define precisely what data they wish to retrieve or mutate on a server, leadin…
Yaps: Python Frontend to Stan
Guillaume Baudart, Martin Hirzel, Kiran Kate +2
Stan is a popular probabilistic programming language with a self-contained syntax and semantics that is close to graphical models. Unfortunately, existing embeddings of Stan in Pyt…
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…
Deep Probabilistic Programming Languages: A Qualitative Study
Guillaume Baudart, Martin Hirzel, Louis Mandel
Deep probabilistic programming languages try to combine the advantages of deep learning with those of probabilistic programming languages. If successful, this would be a big step f…