Scalable Bayesian Optimization Using Deep Neural Networks
arXiv:1502.05700
Abstract
Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations. It relies on querying a distribution over functions defined by a relatively cheap surrogate model. An accurate model for this distribution over functions is critical to the effectiveness of the approach, and is typically fit using Gaussian processes (GPs). However, since GPs scale cubically with the number of observations, it has been challenging to handle objectives whose optimization requires many evaluations, and as such, massively parallelizing the optimization. In this work, we explore the use of neural networks as an alternative to GPs to model distributions over functions. We show that performing adaptive basis function regression with a neural network as the parametric form performs competitively with state-of-the-art GP-based approaches, but scales linearly with the number of data rather than cubically. This allows us to achieve a previously intractable degree of parallelism, which we apply to large scale hyperparameter optimization, rapidly finding competitive models on benchmark object recognition tasks using convolutional networks, and image caption generation using neural language models.
References in corpus (7)
- Improving neural networks by preventing co-adaptation of feature detectors
- Practical Bayesian Optimization of Machine Learning Algorithms
- Recurrent Neural Network Regularization
- Portfolio Allocation for Bayesian Optimization
- Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations
- Automated Machine Learning on Big Data using Stochastic Algorithm Tuning
- Anomaly Detection and Removal Using Non-Stationary Gaussian Processes