58,836 research outputs found
Mean field variational Bayesian inference for support vector machine classification
A mean field variational Bayes approach to support vector machines (SVMs)
using the latent variable representation on Polson & Scott (2012) is presented.
This representation allows circumvention of many of the shortcomings associated
with classical SVMs including automatic penalty parameter selection, the
ability to handle dependent samples, missing data and variable selection. We
demonstrate on simulated and real datasets that our approach is easily
extendable to non-standard situations and outperforms the classical SVM
approach whilst remaining computationally efficient.Comment: 18 pages, 4 figure
OBOE: Collaborative Filtering for AutoML Model Selection
Algorithm selection and hyperparameter tuning remain two of the most
challenging tasks in machine learning. Automated machine learning (AutoML)
seeks to automate these tasks to enable widespread use of machine learning by
non-experts. This paper introduces OBOE, a collaborative filtering method for
time-constrained model selection and hyperparameter tuning. OBOE forms a matrix
of the cross-validated errors of a large number of supervised learning models
(algorithms together with hyperparameters) on a large number of datasets, and
fits a low rank model to learn the low-dimensional feature vectors for the
models and datasets that best predict the cross-validated errors. To find
promising models for a new dataset, OBOE runs a set of fast but informative
algorithms on the new dataset and uses their cross-validated errors to infer
the feature vector for the new dataset. OBOE can find good models under
constraints on the number of models fit or the total time budget. To this end,
this paper develops a new heuristic for active learning in time-constrained
matrix completion based on optimal experiment design. Our experiments
demonstrate that OBOE delivers state-of-the-art performance faster than
competing approaches on a test bed of supervised learning problems. Moreover,
the success of the bilinear model used by OBOE suggests that AutoML may be
simpler than was previously understood
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