The 27th Uncertainty in Artificial Intelligence Conference: Preface

Fabio Cozman, Avi Pfeffer
Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:1-17, 2011.

Abstract

This year’s Conference on Uncertainty in Artificial Intelligence (UAI) is the twenty-seventh of a series of meetings that began as a small workshop in 1985. Over the years, the UAI community has developed techniques that have been widely integrated into general Artificial Intelligence curricula and adopted in many fields outside of Artificial Intelligence. The meeting is now the premier international conference on issues relating to representation and management of uncertainty within the field of Artificial Intelligence. UAI has a wide scope that includes, but is not limited to, modeling, inference, learning and decision making under uncertainty, with an interest both in theory and in applied work. This year UAI also hosted the 8th Bayesian Modelling Applications Workshop, a focused forum for interchange among those interested in real world applications of graphical models and Bayesian networks. This volume contains all papers presented at UAI 2011, held at the Campus Roger de Lluria of the Universitat Pompeu Fabra, in Barcelona, Spain, July 14–17, 2011. Papers appearing in this volume were subjected to a rigorous review process; 285 regular papers were submitted to UAI this year, and 96 were accepted (24 for plenary presentation and 72 for poster presentation). We are confident that the proceedings, like past UAI Conference Proceedings, will become an important archival reference for the field. Based on the recommendations of the Program Committee and our own overview of the accepted papers, we selected one paper as recipient of the UAI 2011 Microsoft Best Paper Award and another paper as the recip- ient of the UAI 2011 Google Best Student Paper Award. These awards were given for outstanding technical contributions. The UAI 2011 Microsoft Best Paper Award was given to the paper Sum-Product Networks: A New Deep Architecture by Hoifung Poon and Pedro Domingos, and the UAI 2011 Google Best Student Paper Award was given to the paper Generalised Wishart Processes by Andrew Wilson and Zoubin Ghahramani. Two other papers deserve recognition. The runner up for the Best Paper Award was A Sequence of Relaxations Constraining Hidden Variable Models by Greg Ver Steeg and Aram Galstyan, and the runner up for the Best Student Paper Award was Graph Cuts is a Max-Product Algorithm by Daniel Tarlow, Inmar Givoni, Richard Zemel and Brendan Frey. The journal Artificial Intelligence (AIJ) has invited authors of best papers to submit journal versions of their papers and will work to e

Cite this Paper

BibTeX
@InProceedings{pmlr-vR9-cozman11a, title = {The 27th Uncertainty in Artificial Intelligence Conference: Preface}, author = {Cozman, Fabio and Pfeffer, Avi}, booktitle = {Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence}, pages = {1--17}, year = {2011}, editor = {Cozman, Fabio and Pfeffer, Avi}, volume = {R9}, series = {Proceedings of Machine Learning Research}, month = {14--17 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r9/main/assets/cozman11a/cozman11a.pdf}, url = {https://proceedings.mlr.press/r9/cozman11a.html}, abstract = {This year’s Conference on Uncertainty in Artificial Intelligence (UAI) is the twenty-seventh of a series of meetings that began as a small workshop in 1985. Over the years, the UAI community has developed techniques that have been widely integrated into general Artificial Intelligence curricula and adopted in many fields outside of Artificial Intelligence. The meeting is now the premier international conference on issues relating to representation and management of uncertainty within the field of Artificial Intelligence. UAI has a wide scope that includes, but is not limited to, modeling, inference, learning and decision making under uncertainty, with an interest both in theory and in applied work. This year UAI also hosted the 8th Bayesian Modelling Applications Workshop, a focused forum for interchange among those interested in real world applications of graphical models and Bayesian networks. This volume contains all papers presented at UAI 2011, held at the Campus Roger de Lluria of the Universitat Pompeu Fabra, in Barcelona, Spain, July 14–17, 2011. Papers appearing in this volume were subjected to a rigorous review process; 285 regular papers were submitted to UAI this year, and 96 were accepted (24 for plenary presentation and 72 for poster presentation). We are confident that the proceedings, like past UAI Conference Proceedings, will become an important archival reference for the field. Based on the recommendations of the Program Committee and our own overview of the accepted papers, we selected one paper as recipient of the UAI 2011 Microsoft Best Paper Award and another paper as the recip- ient of the UAI 2011 Google Best Student Paper Award. These awards were given for outstanding technical contributions. The UAI 2011 Microsoft Best Paper Award was given to the paper Sum-Product Networks: A New Deep Architecture by Hoifung Poon and Pedro Domingos, and the UAI 2011 Google Best Student Paper Award was given to the paper Generalised Wishart Processes by Andrew Wilson and Zoubin Ghahramani. Two other papers deserve recognition. The runner up for the Best Paper Award was A Sequence of Relaxations Constraining Hidden Variable Models by Greg Ver Steeg and Aram Galstyan, and the runner up for the Best Student Paper Award was Graph Cuts is a Max-Product Algorithm by Daniel Tarlow, Inmar Givoni, Richard Zemel and Brendan Frey. The journal Artificial Intelligence (AIJ) has invited authors of best papers to submit journal versions of their papers and will work to e}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T The 27th Uncertainty in Artificial Intelligence Conference: Preface %A Fabio Cozman %A Avi Pfeffer %B Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2011 %E Fabio Cozman %E Avi Pfeffer %F pmlr-vR9-cozman11a %I PMLR %P 1--17 %U https://proceedings.mlr.press/r9/cozman11a.html %V R9 %X This year’s Conference on Uncertainty in Artificial Intelligence (UAI) is the twenty-seventh of a series of meetings that began as a small workshop in 1985. Over the years, the UAI community has developed techniques that have been widely integrated into general Artificial Intelligence curricula and adopted in many fields outside of Artificial Intelligence. The meeting is now the premier international conference on issues relating to representation and management of uncertainty within the field of Artificial Intelligence. UAI has a wide scope that includes, but is not limited to, modeling, inference, learning and decision making under uncertainty, with an interest both in theory and in applied work. This year UAI also hosted the 8th Bayesian Modelling Applications Workshop, a focused forum for interchange among those interested in real world applications of graphical models and Bayesian networks. This volume contains all papers presented at UAI 2011, held at the Campus Roger de Lluria of the Universitat Pompeu Fabra, in Barcelona, Spain, July 14–17, 2011. Papers appearing in this volume were subjected to a rigorous review process; 285 regular papers were submitted to UAI this year, and 96 were accepted (24 for plenary presentation and 72 for poster presentation). We are confident that the proceedings, like past UAI Conference Proceedings, will become an important archival reference for the field. Based on the recommendations of the Program Committee and our own overview of the accepted papers, we selected one paper as recipient of the UAI 2011 Microsoft Best Paper Award and another paper as the recip- ient of the UAI 2011 Google Best Student Paper Award. These awards were given for outstanding technical contributions. The UAI 2011 Microsoft Best Paper Award was given to the paper Sum-Product Networks: A New Deep Architecture by Hoifung Poon and Pedro Domingos, and the UAI 2011 Google Best Student Paper Award was given to the paper Generalised Wishart Processes by Andrew Wilson and Zoubin Ghahramani. Two other papers deserve recognition. The runner up for the Best Paper Award was A Sequence of Relaxations Constraining Hidden Variable Models by Greg Ver Steeg and Aram Galstyan, and the runner up for the Best Student Paper Award was Graph Cuts is a Max-Product Algorithm by Daniel Tarlow, Inmar Givoni, Richard Zemel and Brendan Frey. The journal Artificial Intelligence (AIJ) has invited authors of best papers to submit journal versions of their papers and will work to e %Z Reissued by PMLR on 04 October 2026.
APA
Cozman, F. & Pfeffer, A.. (2011). The 27th Uncertainty in Artificial Intelligence Conference: Preface. Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R9:1-17 Available from https://proceedings.mlr.press/r9/cozman11a.html. Reissued by PMLR on 04 October 2026.

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