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--- students:phd_mlws [2017/05/20 21:00] – [Objectives] blay
+++ students:phd_mlws [2017/05/28 17:37] – [Bibliographie] blay
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 ====== Machine Learning Workflow System ======
-This subject is proposed as part of the [[http://rockflows.i3s.unice.fr/|ROCKFlows]] project involving the following researchers: [[http://mireilleblayfornarino.i3s.unice.fr|Mireille Blay-Fornarino]], [[http://www.i3s.unice.fr/~mosser/start|Sébastien Mosser]] and [[http://www.i3s.unice.fr/~precioso/|Frédéric Precioso]].
+This subject is proposed as part of the [[http://rockflows.i3s.unice.fr/|ROCKFlows]] project.
 ===== Context =====
 For many years, Machine Learning research has been focusing on designing new algorithms for solving similar kinds of problem instances (Kotthoff, 2016). However, Researchers have long ago recognized that a single algorithm will not give the best performance across all problem instances, e.g. the No-Free-Lunch-Theorem (Wolpert, 1996) states that the best classifier will not be the same on every dataset. Consequently, the “winner-take-all” approach should not lead to neglect some algorithms that, while uncompetitive on average, may offer excellent performances on particular problem instances. In 1976, Rice characterized this as the "algorithm selection problem" (Rice, 1976).
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 To meet these challenges, attention should be paid to the following aspects:
         * //Handling Variabilities: // Variability of compositions (e.g. identifying dominated workflows, managing requirements between WF components); Variability of performance metrics (e.g. dependencies among metrics); Variability of Data Sets (e.g. images, text) and consequently how we represent them (meta features); Variability of platforms; Variability of algorithms and preprocessing algorithms (i.e. characterization to distinguish and automate the compositions); Variability of hyper-parameter tuning strategies (i.e. dependency with workflows); etc.
-        *// Architecture of the portfolio : // (1) automatically manage experiment running, (2) collect of experiment results, (3) analyze of results, (4) evolution of algorithm base. It must support the management of execution errors, incremental analyzes, identifying context of experiments.
+        *// Architecture of the portfolio : // automatically manage (1) experiment running, (2) collecting of experiment results, (3) analyzis of results, (4) evolution of algorithm base. It must support the management of execution errors, incremental analyzes, identifying context of experiments.
-        * //Handling Scalability of Portfolio: S//electing discriminating data sets; Detecting “deprecated” algorithms and WF from experiments and literature revues; Dealing with information from scientific literature without deteriorating portfolio computed knowledge.
+        * //Handling Scalability of the Portfolio: //Selecting discriminating data sets; Detecting “deprecated” algorithms and WF from experiments and literature revues; Dealing with information from scientific literature without deteriorating portfolio computed knowledge.
-        * //Ensuring global consistency// of Portfolio and Software Product Line. Such a system is enriched by additions to the portfolio and experiment feedbacks. As "knowledge" evolves (e.g., new data types, new metrics), the entire system needs to be updated. It is therefore to find abstractions not only to manage these changes but also to optimize them (Bischl et al. 2016).
+        * //Ensuring global consistency// of the Portfolio and Software Product Line. Such a system is enriched by additions to the portfolio and experiment feedbacks. As "knowledge" evolves (e.g., new data types, new metrics), the entire system needs to be updated. It is therefore to find abstractions not only to manage these changes but also to optimize them (Bischl et al. 2016).
-We have a two-year experience on this subject which has enabled us to (I) eliminate some approaches (e.g. modeling knowledge as a system of constraints because it generates on our current basis more than 6 billion constraints), (ii) lay the foundations for a platform for collecting experiences and presenting to the user (Camillieri et al., 2016) (see [[http:// http://rockflows.i3s.unice.fr/]]), (iii) study the ML workflows to predict workflows (Master internships Luca Parisi, Miguel Fabian Romero Rondon and Melissa Sanabria Rosas), (iv) address platform evolution introducing deep learning workflows (see Melissa’s Report).
+We have a two-year experience on this subject which has enabled us to (I) eliminate some approaches (e.g. modeling knowledge as a system of constraints because it generates on our current basis more than 6 billion constraints), (ii) lay the foundations for a platform for collecting experiences and presenting to the user (Camillieri et al., 2016) (see [[http:// http://rockflows.i3s.unice.fr/]]), (iii) study the ML workflows to predict workflows (Master internships Luca Parisi, Miguel Fabian Romero Rondon and Melissa Sanabria Rosas), (iv) address platform evolution by introducing deep learning workflows (see Melissa’s Report).
 The thesis must investigate the research around the selection of algorithms, considering the automatic composition of workflows and supporting dynamic evolutions. It is therefore a thesis in software engineering research but to address one of the current most central problems in machine learning.
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 Rice JR (1976) The Algorithm Selection Problem. Adv Comput 15:65–118.
+Martin Salvador M, Budka M, Gabrys B (2016) Towards automatic composition of multicomponent predictive systems. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics). doi: 10.1007/978-3-319-32034-2_3
 Wolpert D (1996) The lack of a priori distinctions between learning algorithms. Neural Computation 8(7):1341–1390