Search Results for author: Guillaume Baudart

Found 8 papers, 4 papers with code

Automatic Rao-Blackwellization for Sequential Monte Carlo with Belief Propagation

1 code implementation15 Dec 2023 Waïss Azizian, Guillaume Baudart, Marc Lelarge

Exact Bayesian inference on state-space models~(SSM) is in general untractable, and unfortunately, basic Sequential Monte Carlo~(SMC) methods do not yield correct approximations for complex models.

Bayesian Inference

Learning GraphQL Query Costs (Extended Version)

no code implementations25 Aug 2021 Georgios Mavroudeas, Guillaume Baudart, Alan Cha, Martin Hirzel, Jim A. Laredo, Malik Magdon-Ismail, Louis Mandel, Erik Wittern

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.

Lale: Consistent Automated Machine Learning

1 code implementation4 Jul 2020 Guillaume Baudart, Martin Hirzel, Kiran Kate, Parikshit Ram, Avraham Shinnar

Automated machine learning makes it easier for data scientists to develop pipelines by searching over possible choices for hyperparameters, algorithms, and even pipeline topologies.

BIG-bench Machine Learning

Mining Documentation to Extract Hyperparameter Schemas

no code implementations30 Jun 2020 Guillaume Baudart, Peter D. Kirchner, Martin Hirzel, Kiran Kate

Our vision is to reduce the burden to manually create and maintain such schemas for AI automation tools and broaden the reach of automation to larger libraries and richer schemas.

Yaps: Python Frontend to Stan

1 code implementation6 Dec 2018 Guillaume Baudart, Martin Hirzel, Kiran Kate, Louis Mandel, Avraham Shinnar

Stan is a popular probabilistic programming language with a self-contained syntax and semantics that is close to graphical models.

Programming Languages

Deep Probabilistic Programming Languages: A Qualitative Study

no code implementations17 Apr 2018 Guillaume Baudart, Martin Hirzel, Louis Mandel

Deep probabilistic programming languages try to combine the advantages of deep learning with those of probabilistic programming languages.

BIG-bench Machine Learning Probabilistic Programming

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