Practical Bayesian Optimization of Machine Learning Algorithms

Machine learning algorithms frequently require careful tuning of model hyperparameters, regularization terms, and optimization parameters. Unfortunately, this tuning is often a "black art" that requires expert experience, unwritten rules of thumb, or sometimes brute-force search. Much more appealing is the idea of developing automatic approaches which can optimize the performance of a given learning algorithm to the task at hand. In this work, we consider the automatic tuning problem within the framework of Bayesian optimization, in which a learning algorithm's generalization performance is modeled as a sample from a Gaussian process (GP). The tractable posterior distribution induced by the GP leads to efficient use of the information gathered by previous experiments, enabling optimal choices about what parameters to try next. Here we show how the effects of the Gaussian process prior and the associated inference procedure can have a large impact on the success or failure of Bayesian optimization. We show that thoughtful choices can lead to results that exceed expert-level performance in tuning machine learning algorithms. We also describe new algorithms that take into account the variable cost (duration) of learning experiments and that can leverage the presence of multiple cores for parallel experimentation. We show that these proposed algorithms improve on previous automatic procedures and can reach or surpass human expert-level optimization on a diverse set of contemporary algorithms including latent Dirichlet allocation, structured SVMs and convolutional neural networks

Optimisation of the hydrotesting sequence in tank farm construction using an adaptive genetic algorithm with stochastic preferential logic

In the construction of tank farms there is a requirement for the tanks to be hydro-tested in order to verify that they are leak proof as well as proving the lack of differential settlement in the foundations. The tanks will be required to be filled to a predetermined level and then to maintain this loaded state for a certain period of time before being drained. In areas such as the Middle East water for hydro-testing is not freely available as sea water is often not suitable for this purpose, so fresh water needs to be produced or transported to the construction site for this purpose. It is therefore of major benefit to the project to schedule the hydro-testing of the tanks in such a manner as to minimize the utilization of hydro-test water. This problem is a special case of the Resource Constrained Project Scheduling Problem (RCPSP) and in this research we have modified our previously developed Fitness differential adaptive genetic algorithm [4, 6 & 7] to the solution of this real world problem. The Algorithm has been ported from the original MATLAB code into Microsoft Project using VBA in order to provide a more user friendly, practical interface

Robust Block Coordinate Descent

In this paper we present a novel randomized block coordinate descent method for the minimization of a convex composite objective function. The method uses (approximate) partial second-order (curvature) information, so that the algorithm performance is more robust when applied to highly nonseparable or ill conditioned problems. We call the method Robust Coordinate Descent (RCD). At each iteration of RCD, a block of coordinates is sampled randomly, a quadratic model is formed about that block and the model is minimized approximately/inexactly to determine the search direction. An inexpensive line search is then employed to ensure a monotonic decrease in the objective function and acceptance of large step sizes. We prove global convergence of the RCD algorithm, and we also present several results on the local convergence of RCD for strongly convex functions. Finally, we present numerical results on large-scale problems to demonstrate the practical performance of the method.Comment: 23 pages, 6 figure