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Documents authored by Procaccia, Ariel D.

Document
DOI: 10.4230/LIPIcs.ITCS.2020.65
Computation-Aware Data Aggregation

Authors: Bernhard Haeupler, D. Ellis Hershkowitz, Anson Kahng, and Ariel D. Procaccia

Published in: LIPIcs, Volume 151, 11th Innovations in Theoretical Computer Science Conference (ITCS 2020)

Abstract
Data aggregation is a fundamental primitive in distributed computing wherein a network computes a function of every nodes' input. However, while compute time is non-negligible in modern systems, standard models of distributed computing do not take compute time into account. Rather, most distributed models of computation only explicitly consider communication time. In this paper, we introduce a model of distributed computation that considers both computation and communication so as to give a theoretical treatment of data aggregation. We study both the structure of and how to compute the fastest data aggregation schedule in this model. As our first result, we give a polynomial-time algorithm that computes the optimal schedule when the input network is a complete graph. Moreover, since one may want to aggregate data over a pre-existing network, we also study data aggregation scheduling on arbitrary graphs. We demonstrate that this problem on arbitrary graphs is hard to approximate within a multiplicative 1.5 factor. Finally, we give an O(log n ⋅ log(OPT/t_m))-approximation algorithm for this problem on arbitrary graphs, where n is the number of nodes and OPT is the length of the optimal schedule.
Cite as

Bernhard Haeupler, D. Ellis Hershkowitz, Anson Kahng, and Ariel D. Procaccia. Computation-Aware Data Aggregation. In 11th Innovations in Theoretical Computer Science Conference (ITCS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 151, pp. 65:1-65:38, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{haeupler_et_al:LIPIcs.ITCS.2020.65,
  author =	{Haeupler, Bernhard and Hershkowitz, D. Ellis and Kahng, Anson and Procaccia, Ariel D.},
  title =	{{Computation-Aware Data Aggregation}},
  booktitle =	{11th Innovations in Theoretical Computer Science Conference (ITCS 2020)},
  pages =	{65:1--65:38},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-134-4},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{151},
  editor =	{Vidick, Thomas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2020.65},
  URN =		{urn:nbn:de:0030-drops-117506},
  doi =		{10.4230/LIPIcs.ITCS.2020.65},
  annote =	{Keywords: Data aggregation, distributed algorithm scheduling, approximation algorithms}
}
Document
DOI: 10.4230/DagRep.6.6.10
Fair Division (Dagstuhl Seminar 16232)

Authors: Yonatan Aumann, Jérôme Lang, and Ariel D. Procaccia

Published in: Dagstuhl Reports, Volume 6, Issue 6 (2016)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 16232 "Fair Division". The seminar was composed of technical sessions with regular talks, and discussion sessions distributed over the full week.
Cite as

Yonatan Aumann, Jérôme Lang, and Ariel D. Procaccia. Fair Division (Dagstuhl Seminar 16232). In Dagstuhl Reports, Volume 6, Issue 6, pp. 10-25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@Article{aumann_et_al:DagRep.6.6.10,
  author =	{Aumann, Yonatan and Lang, J\'{e}r\^{o}me and Procaccia, Ariel D.},
  title =	{{Fair Division (Dagstuhl Seminar 16232)}},
  pages =	{10--25},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2016},
  volume =	{6},
  number =	{6},
  editor =	{Aumann, Yonatan and Lang, J\'{e}r\^{o}me and Procaccia, Ariel D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.6.6.10},
  URN =		{urn:nbn:de:0030-drops-67255},
  doi =		{10.4230/DagRep.6.6.10},
  annote =	{Keywords: cake cutting, computational social choice, envy-freeness, fair division}
}
Document
DOI: 10.4230/DagSemProc.10101.5
Nonmanipulable Selections from a Tournament

Authors: Alon Altman, Ariel D. Procaccia, and Moshe Tennenholtz

Published in: Dagstuhl Seminar Proceedings, Volume 10101, Computational Foundations of Social Choice (2010)

Abstract
A tournament is a binary dominance relation on a set of alternatives. Tournaments arise in many contexts that are relevant to AI, most notably in voting (as a method to aggregate the preferences of agents). There are many works that deal with choice rules that select a desirable alternative from a tournament, but very few of them deal directly with incentive issues, despite the fact that game-theoretic considerations are crucial with respect to systems populated by selfish agents. We deal with the problem of the manipulation of choice rules by considering two types of manipulation. We say that a choice rule is emph{monotonic} if an alternative cannot get itself selected by losing on purpose, and emph{pairwise nonmanipulable} if a pair of alternatives cannot make one of them the winner by reversing the outcome of the match between them. Our main result is a combinatorial construction of a choice rule that is monotonic, pairwise nonmanipulable, and onto the set of alternatives, for any number of alternatives besides three.
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Alon Altman, Ariel D. Procaccia, and Moshe Tennenholtz. Nonmanipulable Selections from a Tournament. In Computational Foundations of Social Choice. Dagstuhl Seminar Proceedings, Volume 10101, pp. 1-6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{altman_et_al:DagSemProc.10101.5,
  author =	{Altman, Alon and Procaccia, Ariel D. and Tennenholtz, Moshe},
  title =	{{Nonmanipulable Selections from a Tournament}},
  booktitle =	{Computational Foundations of Social Choice},
  pages =	{1--6},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10101},
  editor =	{Felix Brandt and Vincent Conitzer and Lane A. Hemaspaandra and Jean-Francois Laslier and William S. Zwicker},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.10101.5},
  URN =		{urn:nbn:de:0030-drops-25607},
  doi =		{10.4230/DagSemProc.10101.5},
  annote =	{Keywords: Tournament, manipulation}
}
Document
DOI: 10.4230/DagSemProc.07271.6
Incentive Compatible Regression Learning

Authors: Ofer Dekel, Felix Fischer, and Ariel D. Procaccia

Published in: Dagstuhl Seminar Proceedings, Volume 7271, Computational Social Systems and the Internet (2007)

Abstract
We initiate the study of incentives in a general machine learning framework. We focus on a game theoretic regression learning setting where private information is elicited from multiple agents, which are interested in different distributions over the sample space. This conflict potentially gives rise to untruthfulness on the part of the agents. In the restricted but important case when distributions are degenerate, and under mild assumptions, we show that agents are motivated to tell the truth. In a more general setting, we study the power and limitations of mechanisms without payments. We finally establish that, in the general setting, the VCG mechanism goes a long way in guaranteeing truthfulness and efficiency.
Cite as

Ofer Dekel, Felix Fischer, and Ariel D. Procaccia. Incentive Compatible Regression Learning. In Computational Social Systems and the Internet. Dagstuhl Seminar Proceedings, Volume 7271, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{dekel_et_al:DagSemProc.07271.6,
  author =	{Dekel, Ofer and Fischer, Felix and Procaccia, Ariel D.},
  title =	{{Incentive Compatible Regression Learning}},
  booktitle =	{Computational Social Systems and the Internet},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7271},
  editor =	{Peter Cramton and Rudolf M\"{u}ller and Eva Tardos and Moshe Tennenholtz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07271.6},
  URN =		{urn:nbn:de:0030-drops-11622},
  doi =		{10.4230/DagSemProc.07271.6},
  annote =	{Keywords: Machine learning, regression, mechanism design}
}
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