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.
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.).
@cite_26 @cite_10
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We study the aggregate group nearest neighbor searching for the MAX operator in the plane. For a set @math of @math points and a query set @math of @math points, the query asks for a point of @math whose maximum distance to the points in @math is minimized. We present data structures for answering such queries for both @math and @math distance measures. Previously, only heuristic and approximation algorithms were given for both versions. For the @math version, we build a data structure of O(n) size in @math time, such that each query can be answered in @math time. For the @math version, we build a data structure in @math time and @math space, such that each query can be answered in @math time, and alternatively, we build a data structure in @math time and space for any @math , such that each query can be answered in @math time. Further, we extend our result for the @math version to the top- @math queries where each query asks for the @math points of @math whose maximum distances to @math are the smallest for any @math with @math : We build a data structure of O(n) size in @math time, such that each top- @math query can be answered in @math time. In this paper, we show that many graph search problems can be solved quite efficiently for a geometric intersection graph of horizontal and vertical line segments. We first extract several basic operations for depth first search and breadth first search on a graph. Then we present data structures for the intersection graph in terms of which those operations can be implemented in an efficient manner. The data structures enable us to solve various graph search problems besides depth first search and breadth first search. Specifying the results obtained in this paper for an intersection graph of n horizontal and vertical segments with m pairs of intersecting segments, we obtain algorithms with the following complexity, where @math . (i) Depth first search and breadth first search can be executed in @math time and @math space. (ii) The biconnected components can be found in @math time and @math space. (iii) A maximum matching and a maximum independent set can be found in $O...