no code implementations • 14 Jul 2023 • Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni, Federico Fusco, Stefano Leonardi
We study the problem of regret minimization for a single bidder in a sequence of first-price auctions where the bidder discovers the item's value only if the auction is won.
no code implementations • 21 Feb 2023 • Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni, Federico Fusco, Stefano Leonardi
We provide a complete characterization of the regret regimes for fixed-price mechanisms under different feedback models in the two cases where the learner can post either the same or different prices to buyers and sellers.
no code implementations • 8 Jan 2023 • Vashist Avadhanula, Andrea Celli, Riccardo Colini-Baldeschi, Stefano Leonardi, Matteo Russo
A successful approach to online selection problems in the adversarial setting is given by the notion of Online Contention Resolution Scheme (OCRS), that uses a priori information to formulate a linear relaxation of the underlying optimization problem, whose optimal fractional solution is rounded online for any adversarial order of the input sequence.
no code implementations • 13 Jun 2022 • Marianna Maranghi, Aris Anagnostopoulos, Irene Cannistraci, Ioannis Chatzigiannakis, Federico Croce, Giulia Di Teodoro, Michele Gentile, Giorgio Grani, Maurizio Lenzerini, Stefano Leonardi, Andrea Mastropietro, Laura Palagi, Massimiliano Pappa, Riccardo Rosati, Riccardo Valentini, Paola Velardi
The Associazione Medici Diabetologi (AMD) collects and manages one of the largest worldwide-available collections of diabetic patient records, also known as the AMD database.
no code implementations • 17 Sep 2021 • Georgios Amanatidis, Georgios Birmpas, Federico Fusco, Philip Lazos, Stefano Leonardi, Rebecca Reiffenhäuser
For Round-Robin we show that all of its pure Nash equilibria induce allocations that are EF1 with respect to the underlying true values, while for the algorithm of Plaut and Roughgarden we show that the corresponding allocations not only are EFX but also satisfy maximin share fairness, something that is not true for this algorithm in the non-strategic setting!
no code implementations • 8 Sep 2021 • Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni, Federico Fusco, Stefano Leonardi
In this paper, we cast the bilateral trade problem in a regret minimization framework over $T$ rounds of seller/buyer interactions, with no prior knowledge on their private valuations.
no code implementations • 16 Mar 2021 • Vashist Avadhanula, Riccardo Colini-Baldeschi, Stefano Leonardi, Karthik Abinav Sankararaman, Okke Schrijvers
We modify the algorithm proposed in Badanidiyuru \emph{et al.,} to extend it to the case of multiple platforms to obtain an algorithm for both the discrete and continuous bid-spaces.
no code implementations • 9 Mar 2021 • Yossi Azar, Stefano Leonardi, Noam Touitou
We consider the problem of online scheduling on a single machine in order to minimize weighted flow time.
Data Structures and Algorithms
no code implementations • 16 Feb 2021 • Georgios Amanatidis, Federico Fusco, Philip Lazos, Stefano Leonardi, Alberto Marchetti Spaccamela, Rebecca Reiffenhäuser
Submodular maximization is a classic algorithmic problem with multiple applications in data mining and machine learning; there, the growing need to deal with massive instances motivates the design of algorithms balancing the quality of the solution with applicability.
no code implementations • 16 Feb 2021 • Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni, Federico Fusco, Stefano Leonardi
Despite the simplicity of this problem, a classical result by Myerson and Satterthwaite (1983) affirms the impossibility of designing a mechanism which is simultaneously efficient, incentive compatible, individually rational, and budget balanced.
no code implementations • NeurIPS 2020 • Georgios Amanatidis, Federico Fusco, Philip Lazos, Stefano Leonardi, Rebecca Reiffenhäuser
Constrained submodular maximization problems encompass a wide variety of applications, including personalized recommendation, team formation, and revenue maximization via viral marketing.
no code implementations • 16 Feb 2020 • Aris Anagnostopoulos, Carlos Castillo, Adriano Fazzone, Stefano Leonardi, Evimaria Terzi
In this paper, we provide algorithms for outsourcing and hiring workers in a general setting, where workers form a team and contribute different skills to perform a task.
no code implementations • 14 Feb 2020 • Giorgio Barnabò, Adriano Fazzone, Stefano Leonardi, Chris Schwiegelshohn
In this short paper, we define the Fair Team Formation problem in the following way: given an online labour marketplace where each worker possesses one or more skills, and where all workers are divided into two or more not overlapping classes (for examples, men and women), we want to design an algorithm that is able to find a team with all the skills needed to complete a given task, and that has the same number of people from all classes.
no code implementations • NeurIPS 2016 • Aris Anagnostopoulos, Jakub Łącki, Silvio Lattanzi, Stefano Leonardi, Mohammad Mahdian
In many of these applications, the input graph evolves over time in a continual and decentralized manner, and, to maintain a good clustering, the clustering algorithm needs to repeatedly probe the graph.