UAI '04 : proceedings of the 20th conference on Uncertainty in artificial intelligence / / Christopher Meek

• Autore: Meek Christopher
• Pubblicazione: Arlington, Virginia : , : AUAI Press, , 2004
• Descrizione fisica: 1 online resource (xii, 645 pages) : illustrations
• Disciplina: 006.3
• Soggetto topico: Artificial intelligence; Uncertainty (Information theory)
• Sommario/riassunto: This year marks the 20th anniversary of the Conference of Uncertainty in Artificial Intelligence (UAI). From its beginnings as a small workshop, UAI has grown to become the leading conference in the field. It is now the primary international forum for presenting new results on the use of principled methods for reasoning under uncertainty within intelligent systems. The scope of UAI is wide, including, but not limited to, representation, automated reasoning, learning, decision making, and knowledge acquisition under uncertainty. This year's conference (UAI 2004) continues the tradition, including contributions that report on advances in these core areas, as well as insights derived from the construction and use of applications involving uncertain reasoning. This volume comprises the papers accepted for presentation at UAI 2004, held at the Banff Park Inn in Banff, Canada, from July 7 through 11, 2004. Papers appearing in this volume were subjected to rigorous review; three Program Committee members (or in some cases, auxiliary reviewers) reviewed each paper under the supervision of an Area Chair, who made recommendations to the Program Chairs. The assignment of Program Committee members to papers was based on their expertise and expressed interests in the papers, with an eye toward coverage of the relevant aspects of each paper. This year a record 253 papers were submitted to UAI, and 76 papers were accepted for plenary or poster presentation at the conference. All accepted papers appear in this volume. We are confident that the proceedings, like past UAI Conference Proceedings, will become an important archival reference for the field. Based on the recommendation of the program committee, we selected one paper for the recipient of the Best Paper Award and one as the recipient of the Best Student Paper Award. These awards were given for outstanding technical contributions. We are pleased to present the UAI 2004 Best Paper Award to David McAllester, Michael Collins, and Fernando Pereira for their paper The Case-Factor Complexity of Markov Random Fields and the 2004 Best Student Paper Award to Mathias Drton and Thomas Richardson for their paper Iterative Conditional Fitting for Gaussian Ancestral Graph Models. The runners-up for the Best Student Paper Award were Gal Elidan, Iftach Nachman, and Nir Friedman for their paper "Ideal Parent" Structure Learning for Continuous Variable Networks. In addition to the presentation of technical papers, we were very pleased to have five distinguished invited speakers: Ed George (University of Pennsylvania), Jon Kleinberg (Cornell University), Lillian Lee (Cornell University), Alon Orlitsky (University of California at San Diego), and Moshe Y. Vardi (Rice University). UAI 2004 also continued the tradition of offering a full-day course on Advanced Topics in Uncertainty in Artificial Intelligence consisting of tutorials by Ronen Brafman (Ben-Gurion University), Rina Dechter (University of California at Irvine), Nir Friedman (Hebrew University), and Martin Wainwright (University of California at Berkeley). The set of papers, invited talks, and full-day course topics illustrate both the depth and breadth of UAI techniques and applications. We are proud of the quality of this year's conference, and are looking forward to continued contributions and growth in the future.
• Titolo autorizzato: UAI '04
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910412074403321
• Lo trovi qui: Univ. Federico II
• Serie: ACM international conference proceeding series.