Update to volume for script

README.md

@@ -1,30 +1,22 @@
 # PMLR R6
 
-To suggest fixes to this volume please make a pull request containng the changes requested and a justification for the changes.
+To suggest fixes to this volume please make a pull request containing the changes requested and a justification for the changes.
 
 To edit the details of this conference work edit the [_config.yml](./_config.yml) file and submit a pull request.
 
 To make changes to the individual paper details, edit the associated paper file in the [./_posts](./_posts) subdirectory.
 
-For details of how to publish in PMLR please check http://proceedings.mlr.press/faq.html
+For details of how to publish in PMLR please check https://proceedings.mlr.press/faq.html
 
-For details of what is required to submit a proceedings please check http://proceedings.mlr.press/spec.html
+For details of what is required to submit a proceedings please check https://proceedings.mlr.press/spec.html
 
 
-Published as Reissue R6 by the Proceedings of Machine Learning Research on ?? October 2024.
+
+Published as Reissue R6 by the Proceedings of Machine Learning Research on 09 October 2024.
 
 Volume Edited by:
   * David A. McAllester
   * Petri Myllymäki
 
 Series Editors:
   * Neil D. Lawrence
-  * Mark Reid
-
-
----
-
-
-## TODOs
-
-- [ ] the reissue date is tentative, currently `30 Oct. 2024`, in [`_config.yml`](./_config.yml).

_config.yml

@@ -1,19 +1,20 @@
 ---
 booktitle: Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence
 shortname: UAI
-start: 2008-07-09
+year: '2008'
+start: &1 2008-07-09
 end: 2008-07-12
-url: https://proceedings.mlr.press
+conference_number: '24'
+published: 2024-10-09
 firstpublished: 2008-07-09
-published: 2024-10-30
 layout: proceedings
 series: Proceedings of Machine Learning Research
 publisher: PMLR
 issn: 2640-3498
-id: uai2008
+id: UAI-2008
 month: 0
 cycles: false
-bibtex_editor: McAllester, David A. and Myllymäki, Petri
+bibtex_editor: McAllester, David A. and Myllym{\"a}ki, Petri
 editor:
 - given: David A.
   family: McAllester
@@ -22,17 +23,18 @@ editor:
 title: Proceedings of Machine Learning Research
 description: |
   Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence
-    Held in the University of Helsinki City Centre Campus, Helsinki on 09-12 July 2008
+    Held in The University of Helsinki City Centre Campus, Helsinki on 09-12 July 2008
 
-  Published as Reissue 6 by the Proceedings of Machine Learning Research on 30 October 2024.
+  Published as Reissue 6 by the Proceedings of Machine Learning Research on 09 October 2024.
 
   Volume Edited by:
     David A. McAllester
     Petri Myllymäki
 
   Series Editors:
     Neil D. Lawrence
-date_str: 09--12 Jul
+date_str: '09--12 Jul'
+url: https://proceedings.mlr.press
 author:
   name: PMLR
 baseurl: "/r6"
@@ -64,11 +66,11 @@ volume_dir: r6
 volume: R6
 email: ''
 conference:
-  name: Conference on Uncertainty in Artificial Intelligence
-  url:
+  name: Uncertainty in Artificial Intelligence
+  url: https://www.auai.org/uai2008/
   location: The University of Helsinki City Centre Campus, Helsinki
   dates:
-  - 2008-07-09
+  - *1
   - 2008-07-10
   - 2008-07-11
   - 2008-07-12
@@ -77,4 +79,4 @@ analytics:
     tracking_id: UA-92432422-1
 orig_bibfile: "/Users/neil/mlresearch/r6/uai2008.bib"
 # Site settings
-# Original source:  /Users/neil/mlresearch/r5/aistats2005.bib
+# Original source:  /Users/neil/mlresearch/r6/uai2008.bib

_posts/2008-07-09-acar08a.md

@@ -1,25 +1,29 @@
 ---
-title: 'Adaptive inference on general graphical models'
-abstract: 'Many algorithms and applications involve repeatedly solving variations of the same inference problem; for example we may want to introduce new evidence to the model or perform updates to conditional dependencies. The goal of adaptive inference is to take advantage of what is preserved in the model and perform inference more rapidly than from scratch. In this paper, we describe techniques for adaptive inference on general graphs that support marginal computation and updates to the conditional probabilities and dependencies in logarithmic time. We give experimental results for an implementation of our algorithm, and demonstrate its potential performance benefit in the study of protein structure.'
+abstract: Many algorithms and applications involve repeatedly solving variations of
+  the same inference problem; for example we may want to introduce new evidence to
+  the model or perform updates to conditional dependencies. The goal of adaptive inference
+  is to take advantage of what is preserved in the model and perform inference more
+  rapidly than from scratch. In this paper, we describe techniques for adaptive inference
+  on general graphs that support marginal computation and updates to the conditional
+  probabilities and dependencies in logarithmic time. We give experimental results
+  for an implementation of our algorithm, and demonstrate its potential performance
+  benefit in the study of protein structure.
+title: Adaptive inference on general graphical models
+year: '2008'
 layout: inproceedings
 series: Proceedings of Machine Learning Research
 publisher: PMLR
 issn: 2640-3498
 id: acar08a
 month: 0
-tex_title: "Adaptive inference on general graphical models"
+tex_title: Adaptive inference on general graphical models
 firstpage: 1
 lastpage: 8
 page: 1-8
 order: 1
 cycles: false
-bibtex_editor: McAllester, David A. and Myllym{"a}ki, Petri
-editor:
-- given: David A.
-  family: McAllester
-- given: Petri
-  family: Myllymäki
-bibtex_author: Acar, Umut A. and Ihler, Alexander T. and Mettu, Ramgopal R. and S\"{u}mer, \"{O}zg\"{u}r
+bibtex_author: Acar, Umut A. and Ihler, Alexander T. and Mettu, Ramgopal R. and S\"{u}mer,
+  \"{O}zg\"{u}r
 author:
 - given: Umut A.
   family: Acar
@@ -28,9 +32,9 @@ author:
 - given: Ramgopal R.
   family: Mettu
 - given: Özgür
-  family: Sümer 
+  family: Sümer
 date: 2008-07-09
-note: Reissued by PMLR on 30 October 2024.
+note: Reissued by PMLR on 09 October 2024.
 address:
 container-title: Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence
 volume: R6
@@ -40,7 +44,7 @@ issued:
   - 2008
   - 7
   - 9
-pdf: http://proceedings.mlr.press/r6/acar08a/acar08a.pdf
+pdf: https://raw.githubusercontent.com/mlresearch/r6/main/assets/acar08a/acar08a.pdf
 extras: []
-# Format based on citeproc: http://blog.martinfenner.org/2013/07/30/citeproc-yaml-for-bibliographies/
+# Format based on Martin Fenner's citeproc: https://blog.front-matter.io/posts/citeproc-yaml-for-bibliographies/
 ---

_posts/2008-07-09-antos08a.md

@@ -1,32 +1,34 @@
 ---
-title: 'Identifying reasoning patterns in games'
-abstract: 'We present an algorithm that identifies the reasoning patterns of agents in a game, by iteratively examining the graph structure of its Multi-Agent Influence Diagram (MAID) representation. If the decision of an agent participates in no reasoning patterns, then we can effectively ignore that decision for the purpose of calculating a Nash equilibrium for the game. In some cases, this can lead to exponential time savings in the process of equilibrium calculation. Moreover, our algorithm can be used to enumerate the reasoning patterns in a game, which can be useful for constructing more effective computerized agents interacting with humans.'
+abstract: We present an algorithm that identifies the reasoning patterns of agents
+  in a game, by iteratively examining the graph structure of its Multi-Agent Influence
+  Diagram (MAID) representation. If the decision of an agent participates in no reasoning
+  patterns, then we can effectively ignore that decision for the purpose of calculating
+  a Nash equilibrium for the game. In some cases, this can lead to exponential time
+  savings in the process of equilibrium calculation. Moreover, our algorithm can be
+  used to enumerate the reasoning patterns in a game, which can be useful for constructing
+  more effective computerized agents interacting with humans.
+title: Identifying reasoning patterns in games
+year: '2008'
 layout: inproceedings
 series: Proceedings of Machine Learning Research
 publisher: PMLR
 issn: 2640-3498
 id: antos08a
 month: 0
-tex_title: "Identifying reasoning patterns in games"
+tex_title: Identifying reasoning patterns in games
 firstpage: 9
 lastpage: 18
 page: 9-18
 order: 9
 cycles: false
-bibtex_editor: McAllester, David A. and Myllym{"a}ki, Petri
-editor:
-- given: David A.
-  family: McAllester
-- given: Petri
-  family: Myllymäki
 bibtex_author: Antos, Dimitrios and Pfeffer, Avi
 author:
 - given: Dimitrios
   family: Antos
 - given: Avi
-  family: Pfeffer 
+  family: Pfeffer
 date: 2008-07-09
-note: Reissued by PMLR on 30 October 2024.
+note: Reissued by PMLR on 09 October 2024.
 address:
 container-title: Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence
 volume: R6
@@ -36,7 +38,7 @@ issued:
   - 2008
   - 7
   - 9
-pdf: http://proceedings.mlr.press/r6/antos08a/antos08a.pdf
+pdf: https://raw.githubusercontent.com/mlresearch/r6/main/assets/antos08a/antos08a.pdf
 extras: []
-# Format based on citeproc: http://blog.martinfenner.org/2013/07/30/citeproc-yaml-for-bibliographies/
+# Format based on Martin Fenner's citeproc: https://blog.front-matter.io/posts/citeproc-yaml-for-bibliographies/
 ---

_posts/2008-07-09-auvray08a.md

@@ -1,32 +1,35 @@
 ---
-title: 'Learning inclusion-optimal chordal graphs'
-abstract: 'Chordal graphs can be used to encode dependency models that are representable by both directed acyclic and undirected graphs. This paper discusses a very simple and efficient algorithm to learn the chordal structure of a probabilistic model from data. The algorithm is a greedy hill-climbing search algorithm that uses the inclusion boundary neighborhood over chordal graphs. In the limit of a large sample size and under appropriate hypotheses on the scoring criterion, we prove that the algorithm will find a structure that is inclusion-optimal when the dependency model of the data-generating distribution can be represented exactly by an undirected graph. The algorithm is evaluated on simulated datasets.'
+abstract: Chordal graphs can be used to encode dependency models that are representable
+  by both directed acyclic and undirected graphs. This paper discusses a very simple
+  and efficient algorithm to learn the chordal structure of a probabilistic model
+  from data. The algorithm is a greedy hill-climbing search algorithm that uses the
+  inclusion boundary neighborhood over chordal graphs. In the limit of a large sample
+  size and under appropriate hypotheses on the scoring criterion, we prove that the
+  algorithm will find a structure that is inclusion-optimal when the dependency model
+  of the data-generating distribution can be represented exactly by an undirected
+  graph. The algorithm is evaluated on simulated datasets.
+title: Learning inclusion-optimal chordal graphs
+year: '2008'
 layout: inproceedings
 series: Proceedings of Machine Learning Research
 publisher: PMLR
 issn: 2640-3498
 id: auvray08a
 month: 0
-tex_title: "Learning inclusion-optimal chordal graphs"
+tex_title: Learning inclusion-optimal chordal graphs
 firstpage: 18
 lastpage: 25
 page: 18-25
 order: 18
 cycles: false
-bibtex_editor: McAllester, David A. and Myllym{"a}ki, Petri
-editor:
-- given: David A.
-  family: McAllester
-- given: Petri
-  family: Myllymäki
 bibtex_author: Auvray, Vincent and Wehenkel, Louis
 author:
 - given: Vincent
   family: Auvray
 - given: Louis
-  family: Wehenkel 
+  family: Wehenkel
 date: 2008-07-09
-note: Reissued by PMLR on 30 October 2024.
+note: Reissued by PMLR on 09 October 2024.
 address:
 container-title: Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence
 volume: R6
@@ -36,7 +39,7 @@ issued:
   - 2008
   - 7
   - 9
-pdf: http://proceedings.mlr.press/r6/auvray08a/auvray08a.pdf
+pdf: https://raw.githubusercontent.com/mlresearch/r6/main/assets/auvray08a/auvray08a.pdf
 extras: []
-# Format based on citeproc: http://blog.martinfenner.org/2013/07/30/citeproc-yaml-for-bibliographies/
+# Format based on Martin Fenner's citeproc: https://blog.front-matter.io/posts/citeproc-yaml-for-bibliographies/
 ---

_posts/2008-07-09-barber08a.md

@@ -1,30 +1,36 @@
 ---
-title: 'Clique matrices for statistical graph decomposition and parameterising restricted positive definite matrices'
-abstract: 'We introduce Clique Matrices as an alternative representation of undirected graphs, being a generalisation of the incidence matrix representation. Here we use clique matrices to decompose a graph into a set of possibly overlapping clusters, defined as well-connected subsets of vertices. The decomposition is based on a statistical description which encourages clusters to be well connected and few in number. Inference is carried out using a variational approximation. Clique matrices also play a natural role in parameterising positive definite matrices under zero constraints on elements of the matrix. We show that clique matrices can parameterise all positive definite matrices restricted according to a decomposable graph and form a structured Factor Analysis approximation in the non-decomposable case.'
+abstract: We introduce Clique Matrices as an alternative representation of undirected
+  graphs, being a generalisation of the incidence matrix representation. Here we use
+  clique matrices to decompose a graph into a set of possibly overlapping clusters,
+  defined as well-connected subsets of vertices. The decomposition is based on a statistical
+  description which encourages clusters to be well connected and few in number. Inference
+  is carried out using a variational approximation. Clique matrices also play a natural
+  role in parameterising positive definite matrices under zero constraints on elements
+  of the matrix. We show that clique matrices can parameterise all positive definite
+  matrices restricted according to a decomposable graph and form a structured Factor
+  Analysis approximation in the non-decomposable case.
+title: Clique matrices for statistical graph decomposition and parameterising restricted
+  positive definite matrices
+year: '2008'
 layout: inproceedings
 series: Proceedings of Machine Learning Research
 publisher: PMLR
 issn: 2640-3498
 id: barber08a
 month: 0
-tex_title: "Clique matrices for statistical graph decomposition and parameterising restricted positive definite matrices"
+tex_title: Clique matrices for statistical graph decomposition and parameterising
+  restricted positive definite matrices
 firstpage: 26
 lastpage: 33
 page: 26-33
 order: 26
 cycles: false
-bibtex_editor: McAllester, David A. and Myllym{"a}ki, Petri
-editor:
-- given: David A.
-  family: McAllester
-- given: Petri
-  family: Myllymäki
 bibtex_author: Barber, David
 author:
 - given: David
-  family: Barber 
+  family: Barber
 date: 2008-07-09
-note: Reissued by PMLR on 30 October 2024.
+note: Reissued by PMLR on 09 October 2024.
 address:
 container-title: Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence
 volume: R6
@@ -34,7 +40,7 @@ issued:
   - 2008
   - 7
   - 9
-pdf: http://proceedings.mlr.press/r6/barber08a/barber08a.pdf
+pdf: https://raw.githubusercontent.com/mlresearch/r6/main/assets/barber08a/barber08a.pdf
 extras: []
-# Format based on citeproc: http://blog.martinfenner.org/2013/07/30/citeproc-yaml-for-bibliographies/
+# Format based on Martin Fenner's citeproc: https://blog.front-matter.io/posts/citeproc-yaml-for-bibliographies/
 ---

_posts/2008-07-09-bhattacharjya08a.md

@@ -1,32 +1,36 @@
 ---
-title: 'Sensitivity analysis in decision circuits'
-abstract: 'Decision circuits have been developed to perform efficient evaluation of influence diagrams [Bhattacharjya and Shachter, 2007], building on the advances in arithmetic circuits for belief network inference [Darwiche, 2003]. In the process of model building and analysis, we perform sensitivity analysis to understand how the optimal solution changes in response to changes in the model. When sequential decision problems under uncertainty are represented as decision circuits, we can exploit the efficient solution process embodied in the decision circuit and the wealth of derivative information available to compute the value of information for the uncertainties in the problem and the effects of changes to model parameters on the value and the optimal strategy.'
+abstract: Decision circuits have been developed to perform efficient evaluation of
+  influence diagrams [Bhattacharjya and Shachter, 2007], building on the advances
+  in arithmetic circuits for belief network inference [Darwiche, 2003]. In the process
+  of model building and analysis, we perform sensitivity analysis to understand how
+  the optimal solution changes in response to changes in the model. When sequential
+  decision problems under uncertainty are represented as decision circuits, we can
+  exploit the efficient solution process embodied in the decision circuit and the
+  wealth of derivative information available to compute the value of information for
+  the uncertainties in the problem and the effects of changes to model parameters
+  on the value and the optimal strategy.
+title: Sensitivity analysis in decision circuits
+year: '2008'
 layout: inproceedings
 series: Proceedings of Machine Learning Research
 publisher: PMLR
 issn: 2640-3498
 id: bhattacharjya08a
 month: 0
-tex_title: "Sensitivity analysis in decision circuits"
+tex_title: Sensitivity analysis in decision circuits
 firstpage: 34
 lastpage: 42
 page: 34-42
 order: 34
 cycles: false
-bibtex_editor: McAllester, David A. and Myllym{"a}ki, Petri
-editor:
-- given: David A.
-  family: McAllester
-- given: Petri
-  family: Myllymäki
 bibtex_author: Bhattacharjya, Debarun and Shachter, Ross D.
 author:
 - given: Debarun
   family: Bhattacharjya
 - given: Ross D.
-  family: Shachter 
+  family: Shachter
 date: 2008-07-09
-note: Reissued by PMLR on 30 October 2024.
+note: Reissued by PMLR on 09 October 2024.
 address:
 container-title: Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence
 volume: R6
@@ -36,7 +40,7 @@ issued:
   - 2008
   - 7
   - 9
-pdf: http://proceedings.mlr.press/r6/bhattacharjya08a/bhattacharjya08a.pdf
+pdf: https://raw.githubusercontent.com/mlresearch/r6/main/assets/bhattacharjya08a/bhattacharjya08a.pdf
 extras: []
-# Format based on citeproc: http://blog.martinfenner.org/2013/07/30/citeproc-yaml-for-bibliographies/
+# Format based on Martin Fenner's citeproc: https://blog.front-matter.io/posts/citeproc-yaml-for-bibliographies/
 ---