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Statistical and Probabilistic Methods in Algorithmic Data Analysis (Dagstuhl Seminar 24391)

Authors: Aristides Gionis, Matteo Riondato, and Eli Upfal

Published in: Dagstuhl Reports, Volume 14, Issue 9 (2025)

Abstract

This report documents the program and the outcomes of Dagstuhl Seminar "Statistical and Probabilistic Methods in Algorithmic Data Analysis" (24391). Modern algorithms for data analysis require the use of advanced probabilistic methods to achieve the necessary scalability and accuracy guarantees. At the same time, modern tasks of knowledge discovery from data require the use of advanced statistics to handle challenges such as the test of multiple hypotheses or dependency structure of the data points, such as in time series or graphs. Probabilistic methods are also at the core of areas of theoretical computer science such as sub-linear algorithms and average-case analysis. The application of these methods requires careful balancing of theoretical and practical considerations, to obtain efficient algorithms for data analysis. The Dagstuhl Seminar focused on statistical and probabilistic methods to develop and analyze useful, scalable algorithms for knowledge discovery from large, rich datasets. Participants from different countries, at different stages of their careers, and from both industry and academia gave talks on the topics of the seminar, usually presenting their own research, either recently published or soon-to-be. There was ample time for socializing, networking, and starting or continuing collaborations, and new results are expected to be published thanks to these collaborations.

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Aristides Gionis, Matteo Riondato, and Eli Upfal. Statistical and Probabilistic Methods in Algorithmic Data Analysis (Dagstuhl Seminar 24391). In Dagstuhl Reports, Volume 14, Issue 9, pp. 127-144, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@Article{gionis_et_al:DagRep.14.9.127,
  author =	{Gionis, Aristides and Riondato, Matteo and Upfal, Eli},
  title =	{{Statistical and Probabilistic Methods in Algorithmic Data Analysis (Dagstuhl Seminar 24391)}},
  pages =	{127--144},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2025},
  volume =	{14},
  number =	{9},
  editor =	{Gionis, Aristides and Riondato, Matteo and Upfal, Eli},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.9.127},
  URN =		{urn:nbn:de:0030-drops-226050},
  doi =		{10.4230/DagRep.14.9.127},
  annote =	{Keywords: approximation algorithms, data science, online algorithms, randomized algorithms, statistical data analysis}
}

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DOI: 10.4230/LIPIcs.FUN.2021.2

Collaborative Procrastination

Authors: Aris Anagnostopoulos, Aristides Gionis, and Nikos Parotsidis

Published in: LIPIcs, Volume 157, 10th International Conference on Fun with Algorithms (FUN 2021) (2020)

Abstract

The problem of inconsistent planning in decision making, which leads to undesirable effects such as procrastination, has been studied in the behavioral-economics literature, and more recently in the context of computational behavioral models. Individuals, however, do not function in isolation, and successful projects most often rely on team work. Team performance does not depend only on the skills of the individual team members, but also on other collective factors, such as team spirit and cohesion. It is not an uncommon situation (for instance, experienced by the authors while working on this paper) that a hard-working individual has the capacity to give a good example to her team-mates and motivate them to work harder. In this paper we adopt the model of Kleinberg and Oren (EC'14) on time-inconsistent planning, and extend it to account for the influence of procrastination within the members of a team. Our first contribution is to model collaborative work so that the relative progress of the team members, with respect to their respective subtasks, motivates (or discourages) them to work harder. We compare the total cost of completing a team project when the team members communicate with each other about their progress, with the corresponding cost when they work in isolation. Our main result is a tight bound on the ratio of these two costs, under mild assumptions. We also show that communication can either increase or decrease the total cost. We also consider the problem of assigning subtasks to team members, with the objective of minimizing the negative effects of collaborative procrastination. We show that whereas a simple problem of forming teams of two members can be solved in polynomial time, the problem of assigning n tasks to n agents is NP-hard.

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Aris Anagnostopoulos, Aristides Gionis, and Nikos Parotsidis. Collaborative Procrastination. In 10th International Conference on Fun with Algorithms (FUN 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 157, pp. 2:1-2:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{anagnostopoulos_et_al:LIPIcs.FUN.2021.2,
  author =	{Anagnostopoulos, Aris and Gionis, Aristides and Parotsidis, Nikos},
  title =	{{Collaborative Procrastination}},
  booktitle =	{10th International Conference on Fun with Algorithms (FUN 2021)},
  pages =	{2:1--2:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-145-0},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{157},
  editor =	{Farach-Colton, Martin and Prencipe, Giuseppe and Uehara, Ryuhei},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FUN.2021.2},
  URN =		{urn:nbn:de:0030-drops-127634},
  doi =		{10.4230/LIPIcs.FUN.2021.2},
  annote =	{Keywords: time-inconsistent planning, computational behavioral science, collaborative work, collaborative environments}
}

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Documents authored by Gionis, Aristides

Statistical and Probabilistic Methods in Algorithmic Data Analysis (Dagstuhl Seminar 24391)

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Collaborative Procrastination

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