• Program

    The program can be downloaded by clicking here

    Keynote speakers:

    • Denis Bouyssou, Université Paris Dauphine (Slides)
      Title: Preference modelling for multiattributed alternatives: the conjoint measurement approach
      Asbtract: The purpose of this talk is to offer a brief and nontechnical introduction to the main preference models for alternatives evaluated on several attributes that have been developed in the field of conjoint measurement. The emphasis is on the additive value function model.  We outline its axiomatic foundations  and present various possible assessment techniques to implement it.  Some extensions of this model, e.g., nonadditive models or models tolerating intransitive preferences will then be briefly reviewed.
    • Nicolas Usunier, Facebook AI research (Slides)
      Title: Recommender systems and fair allocation
      Abstract: Recommender systems decide who sees what. They implement allocation mechanisms, where the scarce resource is the user’s attention, which many agents compete for: friends, creators, newspapers, advertisers, recruiters, and of course users themselves, who want valuable content in return for the time they spend on the platform. The literature on multi-stakeholder recommender systems emphasizes the need for fairness in this allocation process, in particular with respect to creators – avoiding disparities in exposure of creator groups that do not have a clear justification, or preventing winner-take-all effects of ranking pipelines. Promoting small creators has also been proposed to incentivize the creation of new content, thereby sustaining the ecosystem in the long run.
      The litterature on fairness in recommender systems (or fairness of exposure) routinely mentions the link with fair allocation mechanisms studied in economics, in particular in social choice theory and more specifically fair division. However, the algorithms that have been proposed so far mostly focus on (hard or soft) fairness constraints’’ such as equalizing exposure to users across various creator groups, which fail to satisfy basic fairness properties studied in fair division. In this talk I will describe our work to bring fairness criteria studied in social choice and fair division to recommender systems. In addition to alternative approaches to implement fairness towards creators and/or viewers, we propose new, computationally efficient algorithms for fair online ranking and exploration/exploitation.
    • Thomas Augustin, Ludwig Maximilian University of Munich (Slides)
      Title: Some insights from decision making under strict uncertainty for machine learning
      Abstract: This presentation discusses whether transferring recent developments in decision making to machine learning may provide new opportunities and insights. In decision making under strict uncertainty, appropriately reflecting the underlying weakly structured information needs principled generalizations of the basic decision-theoretic concepts. Consequently, one works with partial orders, imprecise prior probabilities, sets of cardinal utilities, and optimal solutions consisting of a non-singleton set of actions.
      The first part of the presentation consists of an informal tour revisiting several issues and variants of machine learning methods where set-based concepts could be beneficial. In the second part, I will discuss one concrete, recently developed framework yielding a generalized notion of stochastic dominance. This notion allows ranking classifiers under multiple quality criteria and benchmark data sets while powerfully circumventing the cumbersome and possibly self-contradictory reliance on aggregates.

    TimeCONTENT
    8:309:10REGISTRATION and welcome (Innovation center)
    9:109:20Conference opening
    9:2010:50Preference learning (1)
    Chair: Thomas Augustin

    Improving preference learning for MR-Sort using GPU (Paper)

    A Dual Approach for Learning Sparse Representations of Choquet Integrals (Paper)

    Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Applications in Algorithm Configuration (Paper)
    10:5011:10COFFEE BREAK
    11:1012:10Thomas Augustin Keynote: “Some insights from decision making under strict uncertainty for machine learning” (Slides)
    Chair: Sébastien Destercke
    12:1013:30LUNCH BREAK (Showroom)
    13:3014:30Nicolas Usunier Keynote: “Recommender systems and fair allocation” (Slides)
    Chair: Benjamin Quost
    14:3015:30Preference learning (2)
    Chair: Nicolas Usunier
     
    A genetic algorithm for learning the parameters of an SRMP preference model: Bastien Pasdeloup, Arwa Khannoussi, Alexandru-Liviu Olteanu and Patrick Meyer (Paper)

    Universal aggregation of permutations: Ekhine Irurozki and Stephan Clemencon (Paper)
    15:3015:50COFFEE BREAK
    15:5017:20Ordering alternatives
    Chair: Patrick Meyer
     
    Experimental evaluation of similarity index on the MIA molecular family: Mejri Mohamed Yassine, Ozturk Meltem, Olivier Cailloux and Beniddir Ahmed Mehdi (Paper)

    Multiple criteria sorting and maximal antichains: Denis Bouyssou, Thierry Marchant and Marc Pirlot (Paper)

    A Characterization of Choice Functions Representable by Pareto-Embedding of Alternatives: Karlson Pfannschmidt and Eyke Hüllermeier (Paper)
    18:45Short car travel to Gala Dinner (stop at train station and at Guy Denielou bus stop), then gala dinner
    TIMECONTENT
    9:3011:00Denis Bouyssou Keynote: “Preference modelling for multiattributed alternatives: the conjoint measurement approach” (Slides)
    Chair: Marc Pirlot
    11:0011:20COFFEE BREAK
    11:2012:20Space and time in preferences
    Chair: Khaled Belahcene
     
    Preference disaggregation involving time-series: Betania Campello, Sarah Ben Amor, Leonardo Tomazeli Duarte and João Romano (Paper)

    Learning the parameters of a multidimensional spatial preference model in multi-criteria decision aiding: Arwa Khannoussi, Antoine Rolland and Julien Velcin (Paper)
    12:2013:30Lunch
    13:3015:00Explaining preferences
    Chair: Vincent Mousseau
     
    On the Explanation of a General Decision Model: Christophe Labreuche, Nicolas Atienza and Roman Bresson (Paper)

    Fragment of the necessary decisions explained by an argument scheme in a non compensatory sorting model: Khaled Belahcene, Jérome Gaigne and Sylvain Lagrue (Paper)

    Explaining Fairness-Oriented Recommendations using Transfers and Transitive Arguments: Hénoïk Willot, Khaled Belahcene and Sébastien Destercke (Paper)
    15:0016:00Goodbyes and general discussions