Zobrazeno 1 - 10
of 97
pro vyhledávání: '"GKATZELIS, VASILIS"'
The widespread use of machine learning models in high-stakes domains can have a major negative impact, especially on individuals who receive undesirable outcomes. Algorithmic recourse provides such individuals with suggestions of minimum-cost improve
Externí odkaz:
http://arxiv.org/abs/2410.01580
In the strategic facility location problem, a set of agents report their locations in a metric space and the goal is to use these reports to open a new facility, minimizing an aggregate distance measure from the agents to the facility. However, agent
Externí odkaz:
http://arxiv.org/abs/2409.07142
We provide the first analysis of (deferred acceptance) clock auctions in the learning-augmented framework. These auctions satisfy a unique list of appealing properties, including obvious strategyproofness, transparency, and unconditional winner priva
Externí odkaz:
http://arxiv.org/abs/2408.06483
Aiming to overcome some of the limitations of worst-case analysis, the recently proposed framework of "algorithms with predictions" allows algorithms to be augmented with a (possibly erroneous) machine-learned prediction that they can use as a guide.
Externí odkaz:
http://arxiv.org/abs/2310.02879
In the metric distortion problem there is a set of candidates $C$ and voters $V$ in the same metric space. The goal is to select a candidate minimizing the social cost: the sum of distances of the selected candidate from all the voters, and the chall
Externí odkaz:
http://arxiv.org/abs/2307.07495
We study fair resource allocation with strategic agents. It is well-known that, across multiple fundamental problems in this domain, truthfulness and fairness are incompatible. For example, when allocating indivisible goods, no truthful and determini
Externí odkaz:
http://arxiv.org/abs/2306.02040
We study the problem of designing voting rules that take as input the ordinal preferences of $n$ agents over a set of $m$ alternatives and output a single alternative, aiming to optimize the overall happiness of the agents. The input to the voting ru
Externí odkaz:
http://arxiv.org/abs/2305.19453
We study the problem of allocating indivisible items to budget-constrained agents, aiming to provide fairness and efficiency guarantees. Specifically, our goal is to ensure that the resulting allocation is envy-free up to any item (EFx) while minimiz
Externí odkaz:
http://arxiv.org/abs/2305.02280
Autor:
Banerjee, Siddhartha, Gkatzelis, Vasilis, Hossain, Safwan, Jin, Billy, Micha, Evi, Shah, Nisarg
We design online algorithms for the fair allocation of public goods to a set of $N$ agents over a sequence of $T$ rounds and focus on improving their performance using predictions. In the basic model, a public good arrives in each round, the algorith
Externí odkaz:
http://arxiv.org/abs/2209.15305
We study the problem faced by a data analyst or platform that wishes to collect private data from privacy-aware agents. To incentivize participation, in exchange for this data, the platform provides a service to the agents in the form of a statistic
Externí odkaz:
http://arxiv.org/abs/2209.06340