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students:phd_2019
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students:phd_2019 [2019/05/10 16:47] blay [Objectives] |
students:phd_2019 [2019/05/10 20:29] blay [Learning variability of Machine Learning Workflows] |
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| - | ====== Learning variability of Machine Learning Workflows ====== | + | ====== Meta-learning in a Portfolio of Machine Learning Workflows ====== |
| //By Mireille Blay-Fornarino and Frédéric Precioso | //By Mireille Blay-Fornarino and Frédéric Precioso | ||
| // | // | ||
| - | Depending on data set and objectives, different machine learning workflows perform differently, commonly known as the no free lunch theorem [17]. Is it then possible to envision the meta-learning process as a systematic approach to analyzing past experiences to identify, explain and predict the right choices? This PhD thesis will address this issue by correlating research on software architectures (including product lines) and meta-learning. | + | Recent advances in Machine Learning (ML) have brought new solutions for the problems of prediction, decision, and identification. ML is impacting almost all domains of science or industry but determining the right ML workflow for a given problem remains a key question. To allow not only experts in the field to benefit from ML potential, last years have seen an increasing effort from the big data companies (Amazon AWS, Microsoft Azure, Google AutoML...) to provide any user with simple platforms for designing their own ML workflow. However, none of these solutions consider the design of ML workflow as a generic process intending to capture common processing patterns between workflows (even through workflows targeting different application contexts). These platforms either propose a set of dedicated solutions for given classes of problem (i.e. AutoML Vision, AutoML natural language , AutoML Translation...) or propose a recipe to build your own ML workflow from scratch (i.e. MS Azure Machine Learning studio, RapidMiner). |
| + | Is it then possible to envision the meta-learning process of designing ML workflow as a systematic approach analyzing past experiences to identify, explain and predict the right choices? | ||
| + | This PhD thesis will address this issue by correlating research on software architectures (including product lines) and meta-learning, to bring ML workflow design to the next level by producing explanation on algorithm choices and by cutting portfolio exploration complexity identifying common patterns between workflows. | ||
| ===== Context ===== | ===== Context ===== | ||
students/phd_2019.txt · Last modified: 2019/05/10 20:40 by blay
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