3 Search Results for "Dumitras, Tudor"

Document
DOI: 10.4230/LIPIcs.FORC.2026.19
A Machine Learning Theory Perspective on Strategic Litigation

Authors: Melissa Dutz, Han Shao, Avrim Blum, and Aloni Cohen

Published in: LIPIcs, Volume 368, 7th Symposium on Foundations of Responsible Computing (FORC 2026)

Abstract
Strategic litigation involves bringing a case to court with the goal of having an impact beyond resolving the particular dispute at hand. In a common law system, one way a case may have far-reaching impact is by establishing new legal precedent that later courts must follow. In this paper, we explore strategic litigation from the perspective of machine learning theory. We consider an abstract model of a common law legal system where a lower court decides new cases by applying a decision rule learned from a higher court’s past rulings. In this model, we explore the power of a strategic litigator, who strategically brings cases to the higher court to influence the decision rule applied by the lower court in future cases. We explore questions including: What impact can a strategic litigator have? Which cases should a strategic litigator bring to court? Does it ever make sense for a strategic litigator to bring a case when they are sure the court will rule against them? We show that this strategic case selection problem has interesting structure, with even simple settings exhibiting counterintuitive phenomena. When cases are represented by points in one dimension and the lower court’s learning algorithm is nearest neighbor, or as points in d dimensions and the lower court’s learning algorithm is a support vector machine, we characterize the set of inducible decision rules and develop algorithms for selecting an optimal set of cases to bring to the higher court given the strategic litigator’s objectives.
Cite as

Melissa Dutz, Han Shao, Avrim Blum, and Aloni Cohen. A Machine Learning Theory Perspective on Strategic Litigation. In 7th Symposium on Foundations of Responsible Computing (FORC 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 368, pp. 19:1-19:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{dutz_et_al:LIPIcs.FORC.2026.19,
  author =	{Dutz, Melissa and Shao, Han and Blum, Avrim and Cohen, Aloni},
  title =	{{A Machine Learning Theory Perspective on Strategic Litigation}},
  booktitle =	{7th Symposium on Foundations of Responsible Computing (FORC 2026)},
  pages =	{19:1--19:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-419-2},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{368},
  editor =	{Lin, Huijia (Rachel)},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2026.19},
  URN =		{urn:nbn:de:0030-drops-259921},
  doi =		{10.4230/LIPIcs.FORC.2026.19},
  annote =	{Keywords: Strategic Litigation, Machine Learning Theory, Law}
}
Document
DOI: 10.4230/LIPIcs.ECRTS.2025.13
Faster Classification of Time-Series Input Streams

Authors: Kunal Agrawal, Sanjoy Baruah, Zhishan Guo, Jing Li, Federico Reghenzani, Kecheng Yang, and Jinhao Zhao

Published in: LIPIcs, Volume 335, 37th Euromicro Conference on Real-Time Systems (ECRTS 2025)

Abstract
Deep learning–based classifiers are widely used for perception in autonomous Cyber-Physical Systems (CPS’s). However, such classifiers rarely offer guarantees of perfect accuracy while being optimized for efficiency. To support safety-critical perception, ensembles of multiple different classifiers working in concert are typically used. Since CPS’s interact with the physical world continuously, it is not unreasonable to expect dependencies among successive inputs in a stream of sensor data. Prior work introduced a classification technique that leverages these inter-input dependencies to reduce the average time to successful classification using classifier ensembles. In this paper, we propose generalizations to this classification technique, both in the improved generation of classifier cascades and the modeling of temporal dependencies. We demonstrate, through theoretical analysis and numerical evaluation, that our approach achieves further reductions in average classification latency compared to the prior methods.
Cite as

Kunal Agrawal, Sanjoy Baruah, Zhishan Guo, Jing Li, Federico Reghenzani, Kecheng Yang, and Jinhao Zhao. Faster Classification of Time-Series Input Streams. In 37th Euromicro Conference on Real-Time Systems (ECRTS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 335, pp. 13:1-13:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{agrawal_et_al:LIPIcs.ECRTS.2025.13,
  author =	{Agrawal, Kunal and Baruah, Sanjoy and Guo, Zhishan and Li, Jing and Reghenzani, Federico and Yang, Kecheng and Zhao, Jinhao},
  title =	{{Faster Classification of Time-Series Input Streams}},
  booktitle =	{37th Euromicro Conference on Real-Time Systems (ECRTS 2025)},
  pages =	{13:1--13:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-377-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{335},
  editor =	{Mancuso, Renato},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2025.13},
  URN =		{urn:nbn:de:0030-drops-235919},
  doi =		{10.4230/LIPIcs.ECRTS.2025.13},
  annote =	{Keywords: Classification, Deep Learning, Sensor data streams, IDK classifiers}
}
Document
DOI: 10.4230/DagSemProc.09201.6
Testing Run-time Evolving Systems

Authors: Tudor Dumitras, Frank Eliassen, Kurt Geihs, Henry Muccini, Andrea Polini, and Theo Ungerer

Published in: Dagstuhl Seminar Proceedings, Volume 9201, Self-Healing and Self-Adaptive Systems (2009)

Abstract
This document summarizes the results of the Working Group 4 - ``Testing'' - at the Dagstuhl Seminar 09201 ``Self-Healing and Self-Adaptive Systems'' (organized by A. Andrzejak, K. Geihs, O. Shehory and J. Wilkes). The seminar was held from May 10th 2009 to May 15th 2009 in Schloss Dagstuhl~--~Leibniz Center for Informatics.
Cite as

Tudor Dumitras, Frank Eliassen, Kurt Geihs, Henry Muccini, Andrea Polini, and Theo Ungerer. Testing Run-time Evolving Systems. In Self-Healing and Self-Adaptive Systems. Dagstuhl Seminar Proceedings, Volume 9201, pp. 1-7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{dumitras_et_al:DagSemProc.09201.6,
  author =	{Dumitras, Tudor and Eliassen, Frank and Geihs, Kurt and Muccini, Henry and Polini, Andrea and Ungerer, Theo},
  title =	{{Testing Run-time Evolving Systems}},
  booktitle =	{Self-Healing and Self-Adaptive Systems},
  pages =	{1--7},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9201},
  editor =	{Artur Andrzejak and Kurt Geihs and Onn Shehory and John Wilkes},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09201.6},
  URN =		{urn:nbn:de:0030-drops-21065},
  doi =		{10.4230/DagSemProc.09201.6},
  annote =	{Keywords: Software Testing, Dynamically Evolving Systems}
}
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