1 code implementation • 15 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.
no code implementations • NeurIPS 2021 • Guillaume Baudart, Martin Hirzel, Kiran Kate, Parikshit Ram, Avraham Shinnar, Jason Tsay
Automated machine learning (AutoML) can make data scientists more productive.
no code implementations • 25 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.
1 code implementation • 4 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.
no code implementations • 30 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.
1 code implementation • 6 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
1 code implementation • 30 Sep 2018 • Guillaume Baudart, Javier Burroni, Martin Hirzel, Louis Mandel, Avraham Shinnar
We use our compilation scheme to build two new backends for the Stanc3 compiler targeting Pyro and NumPyro.
no code implementations • 17 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.