NDLI: Joint Model Choice and Hyperparameter Optimization with Factorized Multilayer Perceptrons

Content Provider	IEEE Xplore Digital Library
Author	Schilling, N. Wistuba, M. Drumond, L. Schmidt-Thieme, L.
Copyright Year	2015
Description	Author affiliation: Learning Lab., Univ. of Hildesheim Inf. Syst. & Machine, Hildesheim, Germany (Schilling, N.; Wistuba, M.; Drumond, L.; Schmidt-Thieme, L.)
Abstract	Recent work has demonstrated that hyperparameter optimization within the sequential model-based optimization (SMBO) framework is generally possible. This approach replaces the expensive-to-evaluate function that maps hyperparameters to the performance of a learned model on validation data by a surrogate model which is much cheaper to evaluate. The current state of the art in hyperparameter optimization learns these surrogate models across a variety of solved data sets where a grid search has already been employed. In this way, surrogate models are learned across data sets, and thus able to generalize better. However, meta features that describe characteristics of a data set are usually needed in order for the surrogate model to differentiate between same hyperparameter configurations on different data sets. Another research area that is closely related focuses on model choice, i.e. picking the right model for a given task, which is also a problem that many practitioners face in machine learning. In this paper, we aim to solve both of these problems with a unified surrogate model that learns across different data sets, different classifiers and their respective hyperparameters. We employ factorized multilayer perceptrons, a surrogate model that consists of a multilayer perceptron architecture, but offers the prediction of a factorization machine in the first layer. In this way, data sets, models and hyperparameters are being represented in a joint lower dimensional latent feature space. Experiments on a publicly available meta data set containing 59 individual data sets and 19 prediction models demonstrate the efficiency of our approach.
Starting Page	72
Ending Page	79
File Size	291850
Page Count	8
File Format	PDF
ISSN	10823409
e-ISBN	9781509001637
DOI	10.1109/ICTAI.2015.24
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-09
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data models Predictive models Multilayer perceptrons Kernel Optimization methods Computational modeling Sequential Model-Based Optimization Hyperparameter Optimization Model Choice
Content Type	Text
Resource Type	Article

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