Program

Abstract: Pareto Optimality is one of the most fundamental solution concepts in economics and computer science. In this talk, I walk through some of our work that takes an algorithmic and complexity lens on the concept. I will discuss Pareto optimality in the context of discrete allocation, random assignment, and other social choice settings. 

• • [R] 9h30-9h45: Inbal Rozenzweig, Reshef Meir and Nicholas Mattei Mitigating Skewed Bidding for Conference Paper Matching [video]

  • 9h45-10h: Toby Walsh. Approximate Mechanism Design: Beyond the Classic Setting for Facility Location

  • 10h-10h15: Linus Boes, Rachael Colley, Umberto Grandi, Jérôme Lang and Arianna Novaro. Collective Combinatorial Optimisation as Judgment Aggregation

  • [R] 10h15-10h30: Jiping Zheng, Meijing Wang, Fanxu Meng and Zhiyang Hao. Preference Learning via Fair Interactive Submodular Set Cover [video]

  • 10h30-10h45: Agnes Rico, Sébastien Destercke and Olivier Strauss. Using atomic bounds to get sub-modular approximations

• • 11h15-11h30: Ariane Ravier, Hugo Gilbert, Meltem Öztürk and Olivier Spanjaard. Ordinal dominance with binary interactions for subset choice: axiomatic analysis and complexity issues

  • 11h30-11h45: Jiehua Chen, Martin Nöllenburg, Sofia Simola, Anaïs Villedieu and Markus Wallinger. Multidimensional Manhattan Preferences

  • 11h45-12h: Jan Maly, Simon Rey, Ulle Endriss and Martin Lackner. Effort-Based Fairness for Participatory Budgeting

  • 12h-12h15: Grzegorz Pierczyński and Piotr Skowron. Core-Stable Committees under Restricted Domains

  • [R] 12h15-12h30: Farhad Mohsin, Qishen Han, Sikai Ruan, Pin-Yu Chen, Francesca Rossi and Lirong Xia. Computational Complexity of Verifying the Group No-show Paradox

Abstract: In order to successfully negotiate with human counterparts, software negotiation agents need models of the factors that influence the behavior of their opponents. In this talk, we discuss how three such factors, a negotiator’s preferences with regard to the issues being negotiated, a negotiator’s confidence in achieving a good result, and reciprocity of actions between the two parties, can be represented in formal models. We also provide empirical evidence on the impact of these factors and the validity of the discussed models. 

• • [R] 14h30-14h45: Francis Rhys Ward, Francesco Belardinelli and Francesca Toni. Argumentative Reward Learning: Reasoning About Human Preferences [video]

  • 14h45-15h: Robert Freiman and Michael Bernreiter. Truth and Preferences - A Game Approach for Qualitative Choice Logic

Abstract: Constructive recommendation is the task of learning to recommend “configurations”, i.e. objects that can be assembled from their components on the basis of the user preferences. Examples include PC configurations, recipes, travel plans, layouts, and other structured objects. We cast this problem as a structured prediction task, in which the recommended objects are created by maximizing a learned utility function over an exponentially large combinatorial space of possible configurations. The utility function is learned through preference elicitation, in which preferences can take different forms, from choosing the best configuration in a set of options to suggesting modifications improving a given configuration. We show how to apply constructive preference elicitation to a variety of problems, from product bundling to housing to algorithmic recourse, which aims at recommending a set of actions to be taken to overturn an unfavourable outcome made by a machine learning decision support system.

• • [R] 16h-16h15: Yanxia Zhang, Francine Chen, Shabnam Hakimi, Totte Harinen, Alex Filipowicz, Yan-Ying Chen, Rumen Iliev, Nikos Arechiga, Kalani Murakami, Kent Lyons, Charlene Wu and Matt Klenk. ConjointNet: Enhancing Conjoint Analysis for Preference Prediction with Representation Learning

  • [R] 16h15-16h30: Arie Glazier, Andrea Loreggia, Nicholas Mattei, Taher Rahgooy, Francesca Rossi and Kristen Brent Venable: Learning Behavioral Soft Constraints from Demonstrations [Video]

  • [R] 16h30-16h45: Nicholas Mattei, Taher Rahgooy and Kristen Brent Venable. Preference Learning leveraging Multi-alternative Decision Field Theory [Video]

• • 17h-17h15: Hugo Gilbert, Mohamed Ouaguenouni, Meltem Ozturk and Olivier Spanjaard. Cautious Learning of Multiattribute Preferences

  • [R] 17h15-17h30: Farhad Mohsin, Inwon Kang, Pin-Yu Chen, Francesca Rossi and Lirong Xia: Learning Individual and Collective Priorities over Moral Dilemmas with the Life Jacket Dataset

  • 17h30-17h45: Margot Herin, Patrice Perny and Nataliya Sokolovska: Learning Utilities and Sparse Representations of Capacities for Multicriteria Decision Making with the Bipolar Choquet Integral

  • 17h45-18: Helene Fargier, Pierre-François Gimenez, Jérôme Mengin and Bao Ngoc Nguyen: The complexity of unsupervised learning of lexicographic preferences.