Proceedings of the 20th conference on Uncertainty in artificial intelligence
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,010 | 0,005 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,066 | 0,016 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».