Search Results for author: Thibaut Lienart

Found 6 papers, 4 papers with code

MLJ: A Julia package for composable machine learning

1 code implementation23 Jul 2020 Anthony D. Blaom, Franz Kiraly, Thibaut Lienart, Yiannis Simillides, Diego Arenas, Sebastian J. Vollmer

MLJ (Machine Learing in Julia) is an open source software package providing a common interface for interacting with machine learning models written in Julia and other languages.

BIG-bench Machine Learning

Model-based Asynchronous Hyperparameter and Neural Architecture Search

3 code implementations24 Mar 2020 Aaron Klein, Louis C. Tiao, Thibaut Lienart, Cedric Archambeau, Matthias Seeger

We introduce a model-based asynchronous multi-fidelity method for hyperparameter and neural architecture search that combines the strengths of asynchronous Hyperband and Gaussian process-based Bayesian optimization.

Hyperparameter Optimization Neural Architecture Search

Piecewise Deterministic Markov Processes for Scalable Monte Carlo on Restricted Domains

4 code implementations16 Jan 2017 Joris Bierkens, Alexandre Bouchard-Côté, Arnaud Doucet, Andrew B. Duncan, Paul Fearnhead, Thibaut Lienart, Gareth Roberts, Sebastian J. Vollmer

Piecewise Deterministic Monte Carlo algorithms enable simulation from a posterior distribution, whilst only needing to access a sub-sample of data at each iteration.

Methodology Computation

Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server

no code implementations31 Dec 2015 Leonard Hasenclever, Stefan Webb, Thibaut Lienart, Sebastian Vollmer, Balaji Lakshminarayanan, Charles Blundell, Yee Whye Teh

The posterior server allows scalable and robust Bayesian learning in cases where a data set is stored in a distributed manner across a cluster, with each compute node containing a disjoint subset of data.

Variational Inference

Expectation Particle Belief Propagation

1 code implementation NeurIPS 2015 Thibaut Lienart, Yee Whye Teh, Arnaud Doucet

The computational complexity of our algorithm at each iteration is quadratic in the number of particles.

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