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Justin Kruger

dblp:151/3645 · DBLP profile ↗
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3ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0001-8186-6073ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
2 papers
Algorithmic game theory and mechanism design · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Algorithmic game theory and mechanism design
fair division
1.022023
Portioning using ordinal preferences: Fairness and efficiency · Artif. Intell. 2023
Portioning Using Ordinal Preferences: Fairness and Efficiency · IJCAI 2019
Algorithmic game theory and mechanism design › social choice
ordinal preferences
1.022023
Portioning using ordinal preferences: Fairness and efficiency · Artif. Intell. 2023
Portioning Using Ordinal Preferences: Fairness and Efficiency · IJCAI 2019
Algorithmic game theory and mechanism design
social choice
1.022023
Portioning using ordinal preferences: Fairness and efficiency · Artif. Intell. 2023
Portioning Using Ordinal Preferences: Fairness and Efficiency · IJCAI 2019
Algorithmic game theory and mechanism design › social choice
participatory budgeting
0.712023
Portioning using ordinal preferences: Fairness and efficiency · Artif. Intell. 2023

Methods — techniques the papers use, named apart from their topics

positional scoring rules · 0.4nash product · 0.4leximin · 0.4
YearPublicationVenuePosition
2023 Portioning using ordinal preferences: Fairness and efficiency
abstract
A divisible public resource is to be divided among projects. We study rules that decide on a distribution of the budget when voters have ordinal preference rankings over projects. Examples of such portioning problems are participatory budgeting, time shares, and parliament elections. We introduce a family of rules for portioning, inspired by positional scoring rules. Rules in this family are given by a scoring vector (such as plurality or Borda) associating a positive value with each rank in a vote, and an aggregation function such as leximin or the Nash product. Our family contains well-studied rules, but most are new. We discuss computational and normative properties of our rules. We focus on fairness, and introduce the SD-core, a group fairness notion. Our Nash rules are in the SD-core, and the leximin rules satisfy individual fairness properties. Both are Pareto-efficient.
Stéphane Airiau, Haris Aziz 0001, Ioannis Caragiannis, Justin Kruger, Jérôme Lang, Dominik Peters
Artif. Intell.4
2019 Portioning Using Ordinal Preferences: Fairness and Efficiency
abstract
A public divisible resource is to be divided among projects. We study rules that decide on a distribution of the budget when voters have ordinal preference rankings over projects. Examples of such portioning problems are participatory budgeting, time shares, and parliament elections. We introduce a family of rules for portioning, inspired by positional scoring rules. Rules in this family are given by a scoring vector (such as plurality or Borda) associating a positive value with each rank in a vote, and an aggregation function such as leximin or the Nash product. Our family contains well-studied rules, but most are new. We discuss computational and normative properties of our rules. We focus on fairness, and introduce the SD-core, a group fairness notion. Our Nash rules are in the SD-core, and the leximin rules satisfy individual fairness properties. Both are Pareto-efficient.
Stéphane Airiau, Haris Aziz 0001, Ioannis Caragiannis, Justin Kruger, Jérôme Lang, Dominik Peters
IJCAI4
2014 Empirical Analysis of Aggregation Methods for Collective Annotation
Ciyang Qing, Ulle Endriss, Raquel Fernández, Justin Kruger
COLING4