Machines learning techniques plays a preponderant role in dealing with massive amount of data and are employed in almost every possible domain. Building a high quality machine learning model to be deployed in production is a challenging task, from both, the subject matter experts and the machine learning practitioners. For a broader adoption and scalability of machine learning systems, the construction and configuration of machine learning workflow need to gain in automation. In the last few years, several techniques have been developed in this direction, known as autoML. In this paper, we present a two-stage optimization process to build data pipelines and configure machine learning algorithms. First, we study the impact of data pipelines compared to algorithm configuration in order to show the importance of data preprocessing over hyperparameter tuning. The second part presents policies to efficiently allocate search time between data pipeline construction and algorithm configuration. Those policies are agnostic from the metaoptimizer. Last, we present a metric to determine if a data pipeline is specific or independent from the algorithm, enabling fine-grain pipeline pruning and meta-learning for the coldstart problem.
As an alternative to Bayesian optimization, @cite_26 proposes to use Monte-Carlo Tree Search to iteratively explore a tree-structured search space while pruning the less promising configurations.
@cite_26
2038245069
Monte Carlo tree search (MCTS) has brought about great success regarding the evaluation of stochastic and deterministic games in recent years. We present and empirically analyze a data-driven parallelization approach for MCTS targeting large HPC clusters with Infiniband interconnect. Our implementation is based on OpenMPI and makes extensive use of its RDMA based asynchronous tiny message communication capabilities for effectively overlapping communication and computation. We integrate our parallel MCTS approach termed UCT-Treesplit in our state-of-the-art Go engine Gomorra and measure its strengths and limitations in a real-world setting. Our extensive experiments show that we can scale up to 128 compute nodes and 2048 cores in self-play experiments and, furthermore, give promising directions for additional improvement. The generality of our parallelization approach advocates its use to significantly improve the search quality of a huge number of current MCTS applications.