Search Results

Documents authored by Gordon, Andrew D.

Found 2 Possible Name Variants:

Gordon, Andrew D.

Document
DOI: 10.4230/DagRep.5.4.123
Challenges and Trends in Probabilistic Programming (Dagstuhl Seminar 15181)

Authors: Gilles Barthe, Andrew D. Gordon, Joost-Pieter Katoen, and Annabelle McIver

Published in: Dagstuhl Reports, Volume 5, Issue 4 (2015)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 15181 "Challenges and Trends in Probabilistic Programming". Probabilistic programming is at the heart of machine learning for describing distribution functions; Bayesian inference is pivotal in their analysis. Probabilistic programs are used in security for describing both cryptographic constructions (such as randomised encryption) and security experiments. In addition, probabilistic models are an active research topic in quantitative information now. Quantum programs are inherently probabilistic due to the random outcomes of quantum measurements. Finally, there is a rapidly growing interest in program analysis of probabilistic programs, whether it be using model checking, theorem proving, static analysis, or similar. Dagstuhl Seminar 15181 brought researchers from these various research communities together so as to exploit synergies and realize cross-fertilisation.
Cite as

Gilles Barthe, Andrew D. Gordon, Joost-Pieter Katoen, and Annabelle McIver. Challenges and Trends in Probabilistic Programming (Dagstuhl Seminar 15181). In Dagstuhl Reports, Volume 5, Issue 4, pp. 123-141, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)

Copy BibTex To Clipboard

@Article{barthe_et_al:DagRep.5.4.123,
  author =	{Barthe, Gilles and Gordon, Andrew D. and Katoen, Joost-Pieter and McIver, Annabelle},
  title =	{{Challenges and Trends in Probabilistic Programming (Dagstuhl Seminar 15181)}},
  pages =	{123--141},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2015},
  volume =	{5},
  number =	{4},
  editor =	{Barthe, Gilles and Gordon, Andrew D. and Katoen, Joost-Pieter and McIver, Annabelle},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.5.4.123},
  URN =		{urn:nbn:de:0030-drops-53536},
  doi =		{10.4230/DagRep.5.4.123},
  annote =	{Keywords: Bayesian networks, differential privacy, machine learning, probabilistic programs, security, semantics, static analysis, verification}
}

Gordon, Andrew S.

Document
DOI: 10.4230/OASIcs.CMN.2013.198
A Data-Driven Approach for Classification of Subjectivity in Personal Narratives

Authors: Kenji Sagae, Andrew S. Gordon, Morteza Dehghani, Mike Metke, Jackie S. Kim, Sarah I. Gimbel, Christine Tipper, Jonas Kaplan, and Mary Helen Immordino-Yang

Published in: OASIcs, Volume 32, 2013 Workshop on Computational Models of Narrative

Abstract
Personal narratives typically involve a narrator who participates in a sequence of events in the past. The narrator is therefore present at two narrative levels: (1) the extradiegetic level, where the act of narration takes place, with the narrator addressing an audience directly; and (2) the diegetic level, where the events in the story take place, with the narrator as a participant (usually the protagonist). Although story understanding is commonly associated with semantics of the diegetic level (i.e., understanding the events that take place within the story), personal narratives may also contain important information at the extradiegetic level that frames the narrated events and is crucial for capturing the narrator’s intent. We present a data-driven modeling approach that learns to identify subjective passages that express mental and emotional states of the narrator, placing them at either the diegetic or extradiegetic level. We describe an experiment where we used narratives from personal weblog posts to measure the effectiveness of our approach across various topics in this narrative genre.
Cite as

Kenji Sagae, Andrew S. Gordon, Morteza Dehghani, Mike Metke, Jackie S. Kim, Sarah I. Gimbel, Christine Tipper, Jonas Kaplan, and Mary Helen Immordino-Yang. A Data-Driven Approach for Classification of Subjectivity in Personal Narratives. In 2013 Workshop on Computational Models of Narrative. Open Access Series in Informatics (OASIcs), Volume 32, pp. 198-213, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

Copy BibTex To Clipboard

@InProceedings{sagae_et_al:OASIcs.CMN.2013.198,
  author =	{Sagae, Kenji and Gordon, Andrew S. and Dehghani, Morteza and Metke, Mike and Kim, Jackie S. and Gimbel, Sarah I. and Tipper, Christine and Kaplan, Jonas and Immordino-Yang, Mary Helen},
  title =	{{A Data-Driven Approach for Classification of Subjectivity in Personal Narratives}},
  booktitle =	{2013 Workshop on Computational Models of Narrative},
  pages =	{198--213},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-57-6},
  ISSN =	{2190-6807},
  year =	{2013},
  volume =	{32},
  editor =	{Finlayson, Mark A. and Fisseni, Bernhard and L\"{o}we, Benedikt and Meister, Jan Christoph},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.CMN.2013.198},
  URN =		{urn:nbn:de:0030-drops-41454},
  doi =		{10.4230/OASIcs.CMN.2013.198},
  annote =	{Keywords: personal narrative, subjectivity, diegetic levels, discourse}
}
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH Imprint Privacy Contact