Parallel Coordinate Descent for the Adaboost Problem
By Olivier Fercoq. 2013
12 citations recorded by the source corpus.
We design a randomised parallel version of Adaboost based on previous studies on parallel coordinate descent. The algorithm uses the fact that the logarithm of the exponential loss is a function with coordinate-wise Lipschitz continuous gradient, in order to define the step lengths. We provide the proof of convergence for this randomised Adaboost algorithm and a theoretical parallelisation speedup factor. We finally provide numerical examples on learning problems of various sizes that show that the algorithm is competitive with concurrent approaches, especially for large scale problems.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Peter Richtárik
- Martin Takác
- Jakub Konecný
- Nicolas Loizou
- Martin Jaggi
- Filip Hanzely
- Dominik Csiba
- Virginia Smith
In Computational Theory and Mathematics
- Peter Richtárik
- Martin Takác
- Nicolas Loizou
- Jakub Konecný
- Filip Hanzely
- Stephen J. Wright
- Robert Mansel Gower
- Zheng Qu
Citations in the corpus
- Distributed Coordinate Descent Method for Learning with Big Data (2013, background): “(2013); Fercoq & Richtárik (2013). We will discuss this issue at length in Sections 4 and 5.”
- On optimal probabilities in stochastic coordinate descent methods (2013, methodology): “The first assumption generalizes the ESO concept introduced in [21] and later used in [5,6,22,27,28] to nonuniform samplings.”