Mireille Blay-Fornarino

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students:phd_mlws [2017/05/20 21:08] – [Objectives] blaystudents:phd_mlws [2017/05/28 18:03] (current) – [Context] blay
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 ====== Machine Learning Workflow System ====== ====== 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 ===== ===== 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).  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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          * The structural characteristics (size, quality, and nature) of the collected data          * The structural characteristics (size, quality, and nature) of the collected data
          * How the results will be used.          * How the results will be used.
-This task is highly complex because of the increasing number of available algorithms, the difficulty in choosing the correct preprocessing techniques together with the right algorithms as well as the correct tuning of their parameters. To decide which algorithm to choose, data scientists often consider families of algorithms in which they are experts, and can leave aside algorithms that are more “exotic” to them, but could perform better for the problem they are trying to solve.+This task is highly complex because of the increasing number of available algorithms, the difficulty in choosing the correct preprocessing techniques together with the right algorithms as well as the correct tuning of their parameters (Serban at al, 2013). To decide which algorithm to choose, data scientists often consider families of algorithms in which they are experts, and can leave aside algorithms that are more “exotic” to them, but could perform better for the problem they are trying to solve.
  
 ROCKFlows  is a project aiming at helping users to create their own Machine Learning Workflows by simply describing their dataset and objectives.   ROCKFlows  is a project aiming at helping users to create their own Machine Learning Workflows by simply describing their dataset and objectives.  
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 The thesis must address the following challenges: Relevance and quality of predictions and Scalability to manage the huge mass of ML workflows.  The thesis must address the following challenges: Relevance and quality of predictions and Scalability to manage the huge mass of ML workflows. 
 To meet these challenges, attention should be paid to the following aspects:  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.+        * //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 (Salvador et al, 2016)); Variability of hyper-parameter tuning strategies (i.e. dependency with workflows); etc.
         *// 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.          *// 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 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.          * //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. 
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 Rice JR (1976) The Algorithm Selection Problem. Adv Comput 15:65–118. 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
 +
 +Serban F, Vanschoren J, Kietz J-U, Bernstein A (2013) A survey of intelligent assistants for data analysis. ACM Comput Surv. doi: 10.1145/2480741.2480748
  
 Wolpert D (1996) The lack of a priori distinctions between learning algorithms. Neural Computation 8(7):1341–1390  Wolpert D (1996) The lack of a priori distinctions between learning algorithms. Neural Computation 8(7):1341–1390 
  
students/phd_mlws.1495314483.txt.gz · Last modified: 2017/05/20 21:08 by blay