Name
Abstract | ||
|---|---|---|
In order to achieve state-of-the-art performance, modern machine learning techniques require careful data pre-processing and hyperparameter tuning. Moreover, given the ever increasing number of machine learning models being developed, model selection is becoming increasingly important. Automating the selection and tuning of machine learning pipelines, which can include different data preprocessing methods and machine learning models, has long been one of the goals of the machine learning community. In this paper, we propose to solve this meta-learning task by combining ideas from collaborative filtering and Bayesian optimization. Specifically, we use a probabilistic matrix factorization model to transfer knowledge across experiments performed in hundreds of different datasets and use an acquisition function to guide the exploration of the space of possible pipelines. In our experiments, we show that our approach quickly identifies high-performing pipelines across a wide range of datasets, significantly outperforming the current state-of-the-art. |
Year | Venue | Keywords |
|---|---|---|
2018 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018) | machine learning,model selection,bayesian optimization,data pre-processing,collaborative filtering,automated machine learning |
Field | DocType | Volume |
Online machine learning,Data mining,Multi-task learning,Semi-supervised learning,Inductive transfer,Active learning (machine learning),Computer science,Wake-sleep algorithm,Artificial intelligence,Relevance vector machine,Computational learning theory,Machine learning | Conference | 31 |
ISSN | Citations | PageRank |
1049-5258 | 6 | 0.50 |
References | Authors | |
13 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicoló Fusi | 1 | 172 | 10.23 |
| Sheth, Rishit | 2 | 6 | 0.50 |
| Elibol, Melih | 3 | 6 | 0.83 |