text: 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.
  @cite_26: The sensitivity of machine learning (ML) algorithms w.r.t. their hyper-parameters and the difficulty of finding the ML algorithm and hyper-parameter setting best suited to a given dataset has led to the rapidly developing field of automated machine learning (AutoML), at the crossroad of meta-learning and structured optimization. Several international AutoML challenges have been organized since 2015, motivating the development of the Bayesian optimization-based approach Auto-Sklearn (, 2015) and the Bandit-based approach Hyperband (, 2016). In this paper, a new approach, called Monte Carlo Tree Search for Algorithm Configuration (Mosaic), is presented, fully exploiting the tree structure of the algorithm portfolio and hyper-parameter search space. Experiments (on 133 datasets of the OpenML repository) show that Mosaic performances match that of Auto-Sklearn.
  @cite_10: We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.
summary: The intrinsic difficulty of building a machine learning pipeline lies in the nature of the search space: the objective is non-separable i.e., the marginal performance of an operator @math depends on all the operators in all the paths leading to @math from the source, within the configuration space of a specific operator @math , there might be some dependencies between the hyperparameters (e.g. for Neural Networks, the coefficients @math , @math and @math make sense only for Adam solver @cite_10 ). Therefore, building a machine learning pipeline is a mix between selecting a proper sequence of operations and, for each operation, selecting the proper configuration in a structured and conditional space. On the contrary, most systems handle the problem by aggregating the whole search space, losing the sequential aspect of it. A notable exception is @cite_26 , inspired by , that explores the search space in terms of actions on operators (insertion, deletion, etc.).