[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119671-en":3,"doc-seo-119671-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119671,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Taking the Human out of Learning Applications: A Survey on Automated Machine Learning","Machine learning is widely used in everyday life, yet achieving strong learning performance still requires substantial human effort across key steps such as feature engineering and model or algorithm selection. Automated machine learning (AutoML) addresses this by reducing dependence on experienced experts and making high-quality model development easier to apply. This survey defines the AutoML problem, proposes a general AutoML framework, and reviews existing work by setup and employed techniques, explaining why successful approaches work.","View metadata, citation and similar [papers at ](papers at core.ac.uk)[core.ac.uk](papers at core.ac.uk) brought to you by CORE  \n [provided by](provided by arXiv.org)[ arXiv.org](provided by arXiv.org) e-Print Archive   \n1  \narXiv : 1810 . 13306v 3 [ cs .AI] 17 Jan 2019  \nTaking the Human out of Learning Applications: A Survey on Automated Machine Learning  \nQuanming Yao, Mengshuo Wang,  \nYuqiang Chen, Wenyuan Dai, Yi-Qi Hu, Yu-Feng Li, Wei-Wei Tu, Qiang Yang, Yang Yu  \nAbstract—Machine learning techniques have deeply rooted in our everyday life. However, since it is knowledge-and labor-intensive to pursue good learning performance, humans are heavily involved in every aspect of machine learning. To make machine learning techniques easier to apply and reduce the demand for experienced human experts, automated machine learning (AutoML) has emerged as a hot topic with both industrial and academic interest. In this paper, we provide an up to date survey on AutoML. First, we introduce and define the AutoML problem, with inspiration from both realms of automation and machine learning. Then, we propose a general AutoML framework that not only covers most existing approaches to date, but also can guide the design for new methods.  \nSubsequently, we categorize and review the existing works from two aspects, i.e., the problem setup and the employed techniques.  \nThe proposed framework and taxonomies provide a detailed analysis of AutoML approaches and explain the reasons underneath their successful applications. We hope this survey can serve as not only an insightful guideline for AutoML beginners but also an inspiration for future research.  \nIndex Terms—automated machine learning,neural architecture search,hyper-parameter optimization,meta-learning,transfer-learning  \n~~ ~~ F ~~ ~~  \n1 INTRODUCTION  \nMitchell's famous machine learning textbook [1] begins with the statement: “Ever since computers were invented, we have wondered whether they might be made to learn. If we could understand how to program them to learn - to improve automatically with experience-the impact would be dramatic”. This quest gave birth to a new research area, i.e., machine learning, for Computer Science decades ago. Till now, machine learning techniques have been deeply rooted in our every day's life, such as recommendation when we are reading news and handwriting recognition when we are using our cell-phones. Furthermore, machine learning has also gained significant achievements. For example, AlphaGO [2] defeated human champion in the game of GO, ResNet [3] surpassed human performance in image recognition, Microsoft's speech system [4] approximated human level in speech transcription.  \nHowever, these successful applications of machine learning are far from fully automated, i.e., “improving automatically with experience ”. Since there are no algorithms that can achieve good performance on all possible learning problems with equal importance (according to No Free Lunch theorems [5] [6]), every aspect of machine learning applications, such as feature engineering, model selection, and algorithm selection (Figure 1), needs to be carefully configured. Human experts are hence heavily involved in machine learning applications. As these experts are rare  \n􀀏 Q. Yao, M. Wang, Y. Chen and W. Dai are with 4Paradigm Inc, Beijing, China; Y. Hu, Y. Li and Y. Yu are with Nanjing uiversity, Jiangsu, China; and Q. Yang is with Hong Kong University of Science and Technology, Hong Kong, China.  \n􀀏 All authors are in alphabetical order of last name (except the first two).  \nCorrespondance to Q. Yao [at yaoquanming@4paradigm.com](at yaoquanming@4paradigm.com)  \nresources, the success of machine learning comes at a great price.  \nThus, automated machine learning (AutoML) does not just remain an academic dream as described in Michell's book, but also attracts more attention from practitioners. If we can take the human out of these machine learning applications, we can enable faster depl","cbCainLDrgguv4Hx","https://ap.wps.com/l/cbCainLDrgguv4Hx","pdf",1888358,1,26,"English","en",105,"# Introduction\n# Survey Overview\n## AutoML Problem Definition\n## General AutoML Framework\n# Related Work\n## Problem Setup Taxonomy\n## Technique Review\n# Index Terms","[{\"question\":\"What problem does this paper address in automated machine learning?\",\"answer\":\"The paper addresses the need to make machine learning performance improvements less dependent on human experts by defining the AutoML problem and its goals.\"},{\"question\":\"How does the paper structure its survey of AutoML approaches?\",\"answer\":\"It organizes the review from two perspectives: the problem setup and the employed techniques, supported by a proposed general framework and taxonomies.\"},{\"question\":\"Why is human involvement still important in standard machine learning workflows?\",\"answer\":\"Because no single algorithm achieves good performance on all learning problems with equal importance, crucial pipeline components like feature engineering and model/algorithm selection require careful configuration.\"}]","Taking the Human out of Learning Applications: A Survey on Automated Machine Learning | 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