2 Search Results for "Riedl, Mark"

Document
DOI: 10.4230/LIPIcs.FORC.2025.15
Differentially Private High-Dimensional Approximate Range Counting, Revisited

Authors: Martin Aumüller, Fabrizio Boninsegna, and Francesco Silvestri

Published in: LIPIcs, Volume 329, 6th Symposium on Foundations of Responsible Computing (FORC 2025)

Abstract
Locality Sensitive Filters are known for offering a quasi-linear space data structure with rigorous guarantees for the Approximate Near Neighbor search (ANN) problem. Building on Locality Sensitive Filters, we derive a simple data structure for the Approximate Near Neighbor Counting (ANNC) problem under differential privacy (DP). Moreover, we provide a simple analysis leveraging a connection with concomitant statistics and extreme value theory. Our approach produces a simple data structure with a tunable parameter that regulates a trade-off between space-time and utility. Through this trade-off, our data structure achieves the same performance as the recent findings of Andoni et al. (NeurIPS 2023) while offering better utility at the cost of higher space and query time. In addition, we provide a more efficient algorithm under pure ε-DP and elucidate the connection between ANN and differentially private ANNC. As a side result, the paper provides a more compact description and analysis of Locality Sensitive Filters for Fair Near Neighbor Search, improving a previous result in Aumüller et al. (TODS 2022).
Cite as

Martin Aumüller, Fabrizio Boninsegna, and Francesco Silvestri. Differentially Private High-Dimensional Approximate Range Counting, Revisited. In 6th Symposium on Foundations of Responsible Computing (FORC 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 329, pp. 15:1-15:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{aumuller_et_al:LIPIcs.FORC.2025.15,
  author =	{Aum\"{u}ller, Martin and Boninsegna, Fabrizio and Silvestri, Francesco},
  title =	{{Differentially Private High-Dimensional Approximate Range Counting, Revisited}},
  booktitle =	{6th Symposium on Foundations of Responsible Computing (FORC 2025)},
  pages =	{15:1--15:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-367-6},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{329},
  editor =	{Bun, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2025.15},
  URN =		{urn:nbn:de:0030-drops-231426},
  doi =		{10.4230/LIPIcs.FORC.2025.15},
  annote =	{Keywords: Differential Privacy, Locality Sensitive Filters, Approximate Range Counting, Concominant Statistics}
}
Document
DOI: 10.4230/OASIcs.CMN.2014.139
Applying Qualitative Research Methods to Narrative Knowledge Engineering

Authors: Brian O'Neill and Mark Riedl

Published in: OASIcs, Volume 41, 2014 Workshop on Computational Models of Narrative

Abstract
We propose a methodology for knowledge engineering for narrative intelligence systems, based on techniques used to elicit themes in qualitative methods research. Our methodology uses coding techniques to identify actions in natural language corpora, and uses these actions to create planning operators and procedural knowledge, such as scripts. In an iterative process, coders create a taxonomy of codes relevant to the corpus, and apply those codes to each element of that corpus. These codes can then be combined into operators or other narrative knowledge structures. We also describe the use of this methodology in the context of Dramatis, a narrative intelligence system that required STRIPS operators and scripts in order to calculate human suspense responses to stories.
Cite as

Brian O'Neill and Mark Riedl. Applying Qualitative Research Methods to Narrative Knowledge Engineering. In 2014 Workshop on Computational Models of Narrative. Open Access Series in Informatics (OASIcs), Volume 41, pp. 139-153, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014)

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@InProceedings{oneill_et_al:OASIcs.CMN.2014.139,
  author =	{O'Neill, Brian and Riedl, Mark},
  title =	{{Applying Qualitative Research Methods to Narrative Knowledge Engineering}},
  booktitle =	{2014 Workshop on Computational Models of Narrative},
  pages =	{139--153},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-71-2},
  ISSN =	{2190-6807},
  year =	{2014},
  volume =	{41},
  editor =	{Finlayson, Mark A. and Meister, Jan Christoph and Bruneau, Emile G.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.CMN.2014.139},
  URN =		{urn:nbn:de:0030-drops-46528},
  doi =		{10.4230/OASIcs.CMN.2014.139},
  annote =	{Keywords: narrative intelligence, qualitative methods, coding, knowledge engineering}
}
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