{"id":86,"date":"2019-04-17T11:35:15","date_gmt":"2019-04-17T09:35:15","guid":{"rendered":"https:\/\/project.inria.fr\/sfc2019\/?page_id=86"},"modified":"2019-07-23T17:46:49","modified_gmt":"2019-07-23T15:46:49","slug":"invites","status":"publish","type":"page","link":"https:\/\/project.inria.fr\/sfc2019\/invites\/","title":{"rendered":"Invit\u00e9s"},"content":{"rendered":"

Karell Bertet, Laboratoire L3i, Universit\u00e9 de La Rochelle<\/h3>\n

Structure de Treillis : panorama des aspects structurels et algorithmiques.<\/h4>\n

Le premier ouvrage de r\u00e9f\u00e9rence de la th\u00e9orie des treillis est la premi\u00e8re \u00e9dition du livre de Birkhoff en 1940. Cependant, la notion de treillis a \u00e9t\u00e9 introduite d\u00e8s la fin du 19\u00e8me si\u00e8cle comme une structure alg\u00e9brique munie de deux op\u00e9rateurs appel\u00e9s borne inf\u00e9rieure et borne sup\u00e9rieure. Depuis les ann\u00e9es 2000, l’\u00e9mergence de l’analyse formelle des concepts (FCA) dans divers domaines de l’informatique, que ce soit en analyse de donn\u00e9es et classification, en repr\u00e9sentation des connaissances ou en recherche d’information, a mis en avant les structures de treillis des concepts et de bases de r\u00e8gles d’implication. Plus r\u00e9cemment, les structures de patrons permettent d’\u00e9tendre FCA \u00e0 des donn\u00e9es non binaires.<\/p>\n

Une manipulation efficace de ces structures passe par une bonne connaissance du formalisme, des propri\u00e9t\u00e9s structurelles et des principaux r\u00e9sultats de la th\u00e9orie des treillis et de l’AFC. Nous pr\u00e9senterons un panorama des concepts de base de la th\u00e9orie des treillis, de FCA et des structures de patrons, ainsi que les principaux algorithmes de g\u00e9n\u00e9ration des objets qui la composent.<\/p>\n

 <\/p>\n

\"\"Karell Bertet est Ma\u00eetre de conf\u00e9rences au laboratoire L3i de l’universit\u00e9 de La Rochelle depuis 1999, titulaire d’un doctorat en algorithmique de l’universit\u00e9 Paris 7, et d’une habilitation en informatique de l’universit\u00e9 de La Rochelle. Ses activit\u00e9s scientifiques ont pour fil directeur la structure de treillis, avec des r\u00e9sultats \u00e0 la fois structurels, algorithmiques et applicatifs. Elle a publi\u00e9 plusieurs articles et encadr\u00e9 plusieurs th\u00e8ses sur ce th\u00e8me de recherche, et participe en particulier \u00e0 l’animation de la communaut\u00e9 internationale de l’analyse formelle des concepts.<\/p>\n

 <\/p>\n

Antoine Cornu\u00e9jols, Professeur, AgroParisTech Paris<\/h3>\n

Apprentissage et classification par m\u00e9thodes collaboratives : comment choisir ses collaborateurs et qu\u2019\u00e9changer avec eux ?<\/h4>\n

 <\/p>\n

Des probl\u00e8mes d\u2019intelligence artificielle aussi divers que les jeux \u00e0 plusieurs joueurs, l\u2019apprentissage supervis\u00e9 par des m\u00e9thodes d\u2019ensemble ou le clustering collaboratif peuvent \u00eatre consid\u00e9r\u00e9es avec les m\u00eames questions fondamentales :
\n(1) comment choisir ses collaborateurs c\u2019est-\u00e0-dire les sources d\u2019information ?
\n(2) quelles informations il est int\u00e9ressant d\u2019\u00e9changer ?
\net (3) comment combiner les informations re\u00e7ues ?<\/p>\n

Dans cet expos\u00e9 nous examinerons successivement les algorithmes de jeux, un nouveau algorithme d\u2019apprentissage supervis\u00e9 par transfert puis le clustering collaboratif pour essayer d\u2019en tirer des le\u00e7ons sur les m\u00e9thodes collaboratives pour la classification et, \u00e0 tout le moins, de poser les bonnes questions.<\/p>\n

 <\/p>\n

\"\"Antoine Cornu\u00e9jols est professeur \u00e0 AgroParisTech. Il est co-auteur avec Laurent Miclet et Vincent Barra de l\u2019ouvrage \u00ab Apprentissage Artificiel. Deep learning, concepts et algorithmes \u00bb. Il s\u2019int\u00e9resse particuli\u00e8rement \u00e0 l\u2019apprentissage par transfert et aux m\u00e9thodes d\u2019apprentissage collaboratives supervis\u00e9es et non supervis\u00e9e.<\/p>\n

 <\/p>\n

Bernard De Baets, Professor,\u00a0Ghent University<\/h3>\n

Monotonicity, a deep property in data science<\/h4>\n

 <\/p>\n

In many modelling problems, there exists a monotone relationship between one or more of the input variables and the output variable, although this may not always be fully the case in the observed input-output data due to data imperfections. Monotonicity is also a common property of evaluation and selection procedures. In contrast to a local property such as continuity, monotonicity is of a global nature and any violation of it is therefore simply unacceptable. We explore several problem settings where monotonicity matters, including fuzzy modelling, machine learning and decision making. Central to the above three settings is the cumulative approach, which matches nicely with the monotonicity requirement.<\/p>\n

By far the most popular fuzzy modelling paradigm, despite its weak theoretical foundations, is the rule-based approach of Mamdani and Assilian. In numerous applied papers, authors innocently assume that given a fuzzy rule base that appears monotone at the linguistic level, this will be the case for the generated input-output mapping as well. Unfortunately, this assumption is false, and we will show how to counter it. Moreover, we will show that an implication-based interpretation, accompanied with a cumulative approach based on at-least and\/or at-most quantifiers, might be a much more reasonable alternative.<\/p>\n

Next, we deal with a particular type of classification problem, in which there exists a linear ordering on the label set (as in ordinal regression) as well as on the domain of each of the features. Moreover, there exists a monotone relationship between the features and the class labels. Such problems of monotone classification typically arise in a multi-criteria evaluation setting. When learning such a model from a data set, we are confronted with data impurity in the form of reversed preference. We present the Ordinal Stochastic Dominance Learner framework, which permits to build various instance-based algorithms able to process such data.<\/p>\n

Finally, we explore a pairwise preference setting where each stakeholder expresses his\/her preferences in the shape of a reciprocal relation that is monotone w.r.t. a linear order on the set of alternatives. The goal is to come up with an overall monotone reciprocal relation reflecting `best’ the opinions. We formulate the problem as an optimization problem, where the aggregated linear order is that for which the implied stochastic monotonicity conditions are closest to being satisfied by the distribution of the input monotone reciprocal relations. Interesting links with social choice will be pointed out.<\/p>\n

 <\/p>\n

\"\"Bernard De Baets is a senior full professor in applied mathematics at the Faculty of Bioscience Engineering (Shanghai rank 37 in Life and Agriculture Sciences) of Ghent University, the top-ranked Belgian university (Shanghai rank 61). He is leading the research unit KERMIT and acts as head of the Department of Data Analysis and Mathematical Modelling. Furthermore, he is an affiliated professor at the Anton de Kom Universiteit (Suriname), an Honorary Professor of Budapest Tech (Hungary), a Doctor Honoris Causa of the University of Turku (Finland) and a Profesor Invitado of the Universidad Central \u201cMarta Abreu\u201d de las Villas (Cuba).<\/p>\n

As a trained mathematician, computer scientist and knowledge engineer, Bernard has developed a passion for multi- and interdisciplinary research. He is not only deeply involved in fundamental research in three interlaced research threads, namely knowledge-based, predictive and spatio-temporal modelling, but he also aims at innovative applications in the applied biological sciences. At present, over 30 researchers are involved in the activities of KERMIT. Over the past 20 years, 75 PhD students have graduated under his (co-)supervision.<\/p>\n

Bernard is a prolific writer, with a bibliography comprising over 500 peer-reviewed journal papers, 60 book chapters and 300 contributions to conference proceedings, accumulating more than 20000 Google Scholar citations (h-index 70). Several of his works have been bestowed upon with a best paper award. Moreover, he is a muchinvited speaker, having delivered over 250 lectures world-wide. In 2011, he was elected Fellow of IFSA (International Fuzzy Systems Association) and in 2012, he was a nominee for the Ghent University Prometheus Award for Research. In 2019, he received the EUSFLAT Scientific Excellence Award.<\/p>\n

Bernard actively serves the research community, in particular as co-editor-in-chief of Fuzzy Sets and Systems and as member of the editorial board of several other journals, including the Internat. J. of Approximate Reasoning, Engineering Applications of Artificial Intelligence, and the Iranian J. of Fuzzy Systems. He is a member of the Administrative Board of the Belgian OR Society.<\/p>\n

 <\/p>\n

Dino Ienco, Charg\u00e9 de recherche IRSTEA, UMR Tetis, Montpellier<\/h3>\n

Apprentissage de repr\u00e9sentations avec des \u00ab\u00a0connaissances faibles\u00a0\u00bb : application au clustering et \u00e0 la classification<\/h4>\n

Dans le contexte de la classification semi-supervis\u00e9e, nous avons \u00e0 notre disposition de gros volumes de donn\u00e9es non \u00e9tiquet\u00e9es, mais tr\u00e8s peu de donn\u00e9es \u00e9tiquet\u00e9es et de connaissances associ\u00e9es aux donn\u00e9es. Ce sc\u00e9nario n’est pas propice aux techniques d’apprentissage profond.
\nDans cette pr\u00e9sentation je vais illustrer mes derniers travaux de recherche en apprentissage semi-supervis\u00e9 en utilisant des techniques d’apprentissage profond, avec des applications au clustering ainsi qu’\u00e0 la classification transductive.<\/p>\n

 <\/p>\n

\"\"<\/p>\n

Dino Ienco est Charg\u00e9 de Recherche IRSTEA \u00e0 l’UMR TETIS \u00e0 Montpellier depuis 2011. Il m\u00e8ne des recherches en science des donn\u00e9es sur la classification, la gestion et l’analyse des donn\u00e9es. Il a travaill\u00e9 sur plusieurs types de donn\u00e9es, comme des graphes, des textes et des transactions. Plus r\u00e9cemment, il s’est pench\u00e9 sur l’analyse de donn\u00e9es spatio-temporelles avec un int\u00e9r\u00eat particulier sur les donn\u00e9es d’Observation de la Terre (i.e. donn\u00e9es de t\u00e9l\u00e9d\u00e9tection).<\/div>\n
Ses activit\u00e9s de recherche rel\u00e8vent de l’extraction de motifs, du clustering, de la classification semi-supervis\u00e9e ainsi que de la classification supervis\u00e9e.<\/div>\n

 <\/p>\n

Alexandre Termier, Professeur, IRISA\/Inria Rennes<\/h3>\n

Discovering habits with periodic patterns<\/h4>\n

In various fields, traces of timestamped events are captured, precisely describing the operation of a system or a process. It can as well be a web server log, the operation log of a manufacture, or even a personal activity journal. It can be interesting to analyze such data to discover periodic patterns, i.e. sets or sequences of events that occur within a regular delay. Two problems arise: first, precisely defining such patterns is non-trivial, as many straightforward definitions break in practice due to noise. Second, the search space of periodic patterns is huge, requiring some effort to make the enumeration efficient, and to output only the most interesting patterns. This talk will present the general problem of mining periodic patterns, our work to represent periodic patterns as\u00a0formal concepts, and our latest work to use MDL approaches to select few relevant periodic patterns.<\/p>\n

\"\"Alexandre Termier is Professor of Computer Sciences at Rennes 1 University, where he leads the Lacodam joint Inria-IRISA research group. His main research interest is pattern mining, both for broadening its application and for discovering new algorithms.<\/p>\n","protected":false},"excerpt":{"rendered":"

Karell Bertet, Laboratoire L3i, Universit\u00e9 de La Rochelle Structure de Treillis : panorama des aspects structurels et algorithmiques. Le premier ouvrage de r\u00e9f\u00e9rence de la th\u00e9orie des treillis est la premi\u00e8re \u00e9dition du livre de Birkhoff en 1940. Cependant, la notion de treillis a \u00e9t\u00e9 introduite d\u00e8s la fin du\u2026<\/p>\n

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