Clean up the bibliography, add bibtex-tidy, update "Research"

.pre-commit-config.yaml

@@ -12,3 +12,12 @@ repos:
       - id: check-yaml
       - id: end-of-file-fixer
       - id: trailing-whitespace
+
+  - repo: local
+    hooks:
+      - id: bibtex-tidy
+        name: bibtex-tidy
+        language: node
+        additional_dependencies: [[email protected]]
+        entry: bibtex-tidy --modify --curly --numeric --months --space=2 --align=14 --blank-lines --trailing-commas --remove-empty-fields --no-escape --strip-enclosing-braces --duplicates=key,doi
+        files: ^_bibliography/.*\.bib$

CNAME

@@ -1 +1 @@
-www.probabilistic-numerics.org
+www.probabilistic-numerics.org

_data/research_tags.yml

@@ -29,5 +29,5 @@
   parent: ode
 - id: pde
   label: PDEs
-- id: related
-  label: Related
+- id: other
+  label: Other

_includes/teaching.html

@@ -63,4 +63,4 @@
   {% else %}
   <p>No lectures to show.</p>
   {% endif %}
-</div>
+</div>

_layouts/bib.html

@@ -36,8 +36,9 @@
 
       <div class="periodical">
         {%- capture venue -%}
-          {%- if entry.type == "article" -%}<em>{{ entry.journal }}</em>
-          {%- elsif entry.type == "inproceedings" -%}<em>In {{ entry.booktitle }}</em>
+          {%- if entry.type == "article" -%}{{ entry.journal }}
+          {%- elsif entry.type == "inproceedings" or entry.type == "incollection" -%}In {{ entry.booktitle }}
+          {%- elsif entry.type == "book" -%}{{ entry.publisher }}
           {%- endif -%}
         {%- endcapture -%}
         {{ venue }}{% if entry.year %}{% if venue != "" %}, {% endif %}{{ entry.year }}{% endif %}

_meetings/2015_DALI.md

@@ -2,7 +2,7 @@
 layout: meeting
 
 title: Probabilistic Numerics
-event: Workshop on Data, Learning and Inference (DALI) 
+event: Workshop on Data, Learning and Inference (DALI)
 location: La Palma, Canary Islands
 organizers: [["Philipp", "Hennig"], ["Maike", "Osborne"], ["Mark", "Girolami"]]
 
@@ -13,8 +13,8 @@ summary: >
   Session on Probabilistic Numerics at the newly conceived DALI conference.
 
 link: http://isweb.is.tuebingen.mpg.de/dali/
-img: 
-img_caption: 
+img:
+img_caption:
 
 url_code:
 url_pdf:

_meetings/2015_NeurIPS.md

@@ -9,11 +9,11 @@ organizers: [["Philipp", "Hennig"], ["Maike", "Osborne"]]
 date: 2015-12-07
 date_end: 2015-12-12
 
-summary: 
+summary:
 
 link: https://nips.cc/Conferences/2015
-img: 
-img_caption: 
+img:
+img_caption:
 
 url_code:
 url_pdf:
@@ -23,23 +23,23 @@ url_video:
 
 This workshop will be hosted by [NIPS](https://nips.cc/Conferences/2015), a large annual machine learning and computational neuroscience conference.
 
-Integration is the central numerical operation required for Bayesian machine learning (in the form of marginalization and conditioning). Sampling algorithms still abound in this area, although it has long been known that Monte Carlo methods are fundamentally sub-optimal. The challenges for the development of better performing integration methods are mostly algorithmic. Moreover, recent algorithms have begun to outperform MCMC and its siblings, in wall-clock time, on realistic problems from machine learning. 
+Integration is the central numerical operation required for Bayesian machine learning (in the form of marginalization and conditioning). Sampling algorithms still abound in this area, although it has long been known that Monte Carlo methods are fundamentally sub-optimal. The challenges for the development of better performing integration methods are mostly algorithmic. Moreover, recent algorithms have begun to outperform MCMC and its siblings, in wall-clock time, on realistic problems from machine learning.
 
 The workshop will review the existing, by now quite strong, theoretical case against the use of random numbers for integration, discuss recent algorithmic developments, relationships between conceptual approaches, and highlight central research challenges going forward.
 
 Among the questions to be addressed by the workshop are
 
-* How fast can a practical integral estimate on a deterministic function converge (polynomially, super-polynomially, not just “better than $$\sqrt{N}$$”)? 
+* How fast can a practical integral estimate on a deterministic function converge (polynomially, super-polynomially, not just “better than $$\sqrt{N}$$”)?
 * How are these rates related, precisely, to prior assumptions about the integrand, and to the design rules of the integrator?
-* To which degree can the source code of an integration problem be parsed to choose informative priors? 
-* Are random numbers necessary and helpful for efficient multivariate integration, or are they a conceptual crutch that cause inefficiencies? 
+* To which degree can the source code of an integration problem be parsed to choose informative priors?
+* Are random numbers necessary and helpful for efficient multivariate integration, or are they a conceptual crutch that cause inefficiencies?
 * What are the practical challenges in the design of efficient multivariate integration methods that use such prior information?
 
 The workshop builds upon the growing field of probabilistic numerics,for which probabilistic integration is a core component.
 
 ### Schedule
 
-The workshop will be held on Friday, 11 December in room 512a. 
+The workshop will be held on Friday, 11 December in room 512a.
 
 * 09:00-09:10: [Opening Remarks](http://imgc-cn.artprintimages.com/images/P-473-488-90/60/6056/IC9D100Z/posters/matthew-diffee-your-honor-if-it-please-the-court-i-d-like-to-deliver-my-opening-comment-new-yorker-cartoon.jpg)
 * 09:10-09:40: [Christian Robert](https://www.ceremade.dauphine.fr/~xian/) [(slides)]({{site.baseurl}}/assets/pdf/2015_NeurIPS/CPRobert.pdf)
@@ -80,6 +80,6 @@ We welcome contributions from theoretical treatments, empirical studies, and app
 
 ### Submission instructions
 
-Submissions should be in the NIPS 2015 format, with a maximum of 4 pages (excluding references). Accepted papers will be made available online at the workshop website, and will be presented in a spotlight talk at the workshop itself, but the workshop proceedings can be considered non-archival. Explicitly: shorter versions of relevant papers submitted or published elsewhere are encouraged. Submissions need not be anonymous. 
+Submissions should be in the NIPS 2015 format, with a maximum of 4 pages (excluding references). Accepted papers will be made available online at the workshop website, and will be presented in a spotlight talk at the workshop itself, but the workshop proceedings can be considered non-archival. Explicitly: shorter versions of relevant papers submitted or published elsewhere are encouraged. Submissions need not be anonymous.
 
 Please mail pdf submissions to <[email protected]>. -->

_meetings/2015_SciCADE.md

@@ -9,10 +9,10 @@ organizers: [["Ben", "Calderhead"]]
 date: 2015-09-14
 date_end: 2015-09-18
 
-summary:   
+summary:
 
 link: http://scicade2015.math.uni-potsdam.de/scicade2015/index.html
-img: 
+img:
 img_caption:
 
 url_code:

_meetings/2016_MCMSki.md

@@ -9,10 +9,10 @@ organizers: [["Maike", "Osborne"], ["Chris", "Oates"], ["François-Xavier", "Bri
 date: 2016-01-04
 date_end: 2016-01-07
 
-summary: 
+summary:
 
 link: http://www.pages.drexel.edu/~mwl25/mcmskiV/index.html
-img: 
+img:
 img_caption:
 
 url_code:

_meetings/2016_MCQMC.md

@@ -7,12 +7,12 @@ location: Stanford, CA, United States
 organizers: [["Mark", "Girolami"]]
 
 date: 2016-08-18
-date_end: 
+date_end:
 
-summary: 
+summary:
 
 link: https://mcqmc2016.stanford.edu/
-img: 
+img:
 img_caption:
 
 url_code:
@@ -24,29 +24,29 @@ url_video:
 This workshop is organized by [Mark Girolami](http://www2.warwick.ac.uk/fac/sci/statistics/staff/academic-research/girolami/) and will be hosted by [MCQMC](http://mcqmc2016.stanford.edu/), a biennial meeting on Monte Carlo and quasi-Monte Carlo methods.
 
 Probabilistic Numerics (PN) is an emerging theme of research that straddles the disciplines of mathematical analysis, statistical science and numerical computation. This nascent eld takes it's inspiration from early work in the mid 80s by Diaconis and O'Hagan, who posited that numerical computation should be viewed as a problem of statistical inference. The progress in the intervening years has focused on algorithm design for the probabilistic solution of ordinary differential equations and numerical quadrature, suggesting empirically that an enhanced performance of e.g. numerical quadrature routines can be achieved via an appropriate statistical formulation. However there has been little theoretical analysis to understand the mathematical principles underlying PN that deliver these improvements.
- 
+
 This mini-symposium seeks to bring the area of PN to the MCQMC research community, to present recent mathematical analysis which elucidates the relationship between PN schemes, functional regression and QMC.
 
 ## Schedule
 
-The workshop will be held on Thursday, 18 August 15:50-17:50, at Stanford University, Li Ka Shing Center. 
+The workshop will be held on Thursday, 18 August 15:50-17:50, at Stanford University, Li Ka Shing Center.
 
-#### (*15:50-16:20:*) [Mark Girolami](http://www2.warwick.ac.uk/fac/sci/statistics/staff/academic-research/girolami/) - University of Warwick & Alan Turing Institute. 
+#### (*15:50-16:20:*) [Mark Girolami](http://www2.warwick.ac.uk/fac/sci/statistics/staff/academic-research/girolami/) - University of Warwick & Alan Turing Institute.
 
 > **Probabilistic Numerical Computation: A New Concept?**<br />
 > <br />
 > *Abstract:* The vast amounts of data in many different forms becoming available to politicians, policy makers, technologists, and scientists of every hue presents tantalising opportunities for making advances never before considered feasible.<br />
 > Yet with these apparent opportunities has come an increase in the complexity of the mathematics required to exploit this data. These sophisticated mathematical representations are much more challenging to analyse, and more and more computationally expensive to evaluate. This is a particularly acute problem for many tasks of interest such as making predictions since these will require the extensive use of numerical solvers for linear algebra, optimization, integration or differential equations. These methods will tend to be slow, due to the complexity of the models, and this will potentially lead to solutions with high levels of uncertainty.<br />
 > This talk will introduce our contributions to an emerging area of research defining a nexus of applied mathematics, statistical science and computer science, called “probabilistic numerics”. The aim is to consider numerical problems from a statistical viewpoint, and as such provide numerical methods for which numerical error can be quantified and controlled in a probabilistic manner. This philosophy will be illustrated on problems ranging from predictive policing via crime modelling to computer vision, where probabilistic numerical methods provide a rich and essential quantification of the uncertainty associated with such models and their computation.
 
-#### (*16:20-16:50:*) [François-Xavier Briol](http://www2.warwick.ac.uk/fac/sci/statistics/staff/research_students/briol/) - University of Warwick & University of Oxford. 
+#### (*16:20-16:50:*) [François-Xavier Briol](http://www2.warwick.ac.uk/fac/sci/statistics/staff/research_students/briol/) - University of Warwick & University of Oxford.
 
 > **Probabilistic Integration: A Role for Statisticians in Numerical Analysis?**<br />
 > <br />
 > *Abstract:* One of the current active branches of the field of probabilistic numerics focuses on numerical integration. We will study such a [probabilistic integration](http://www2.warwick.ac.uk/fac/sci/statistics/staff/research_students/briol/probabilistic_integration/) method called Bayesian quadrature, which provides solutions in the form of probability distributions (rather than point estimates) whose structure can provide additional insight into the uncertainty emanating from the finite number of evaluations of the integrand [1]. Strong mathematical guarantees will then be provided in the form of convergence and contraction rates in the number of function evaluations. Finally, we will compare Bayesian quadrature with non-probabilistic methods including Monte Carlo and Quasi-Monte Carlo methods, and illustrate its performance on applications ranging from computer graphics to oil field simulation.<br />
 > [1] F-X. Briol, C.J. Oates, M. Girolami, M. A. Osborne and D. Sejdinovic. Probabilistic Integration: A Role for Statisticians in Numerical Analysis? [arXiv:1512.00933](http://arxiv.org/abs/1512.00933), 2015.
 
-#### (*16:50-17:20:*) [Chris Oates](http://oates.work/) - University of Technology Sydney. 
+#### (*16:50-17:20:*) [Chris Oates](http://oates.work/) - University of Technology Sydney.
 
 > **Probabilistic Integration for Intractable Distributions.**<br />
 > <br />
@@ -65,13 +65,13 @@ $$<br />
 > <br />
 > *Abstract:* Partial differential equations (PDEs) are a challenging class of problems which rarely admit closed-form solutions, forcing us to resort to numerical methods which discretise the problem to construct an approximate solution. We seek to phrase solution of PDEs as a statistical inference problem, and construct probability measures which quantify the epistemic uncertainty in the solution resulting from the discretisation [1]. We explore construction of probability measures for the strong formulation of elliptic PDEs, and the connection between this and "meshless" methods.<br />
 > We seek to apply these probability measures in Bayesian inverse problems,
-parameter inference problems whose dynamics are often constrained 
-by a system of PDEs. Sampling from parameter posteriors in such 
-problems often involves replacing an exact likelihood involving the 
-unknown solution of the system with an approximate one, in which a 
-numerical approximation is used. Such approximations have been 
-shown to produce biased and overconfident posteriors when error 
-in the forward solver is not tightly controlled. We show how the 
-uncertainty from a probabilistic forward solver can be propagated into the parameter posteriors, thus permitting the use of coarser 
+parameter inference problems whose dynamics are often constrained
+by a system of PDEs. Sampling from parameter posteriors in such
+problems often involves replacing an exact likelihood involving the
+unknown solution of the system with an approximate one, in which a
+numerical approximation is used. Such approximations have been
+shown to produce biased and overconfident posteriors when error
+in the forward solver is not tightly controlled. We show how the
+uncertainty from a probabilistic forward solver can be propagated into the parameter posteriors, thus permitting the use of coarser
 discretisations which still produce valid statistical inferences.<br />
 > [1] J. Cockayne, C. J. Oates, T. Sullivan, and M. Girolami. Probabilistic meshless methods for partial differential equations and Bayesian inverse problems. [arXiv:1605.07811](https://arxiv.org/abs/1605.07811), 2016.

_meetings/2016_NeurIPS.md

@@ -7,13 +7,13 @@ location: Barcelona, Spain
 organizers: [["Maren", "Mahsereci"], ["Alex", "Davies"], ["Philipp", "Hennig"]]
 
 date: 2016-12-10
-date_end: 
+date_end:
 
-summary: 
+summary:
 
 link: https://nips.cc/Conferences/2016
-img: 
-img_caption: 
+img:
+img_caption:
 
 url_code:
 url_pdf:
@@ -28,9 +28,9 @@ Recent work has increasingly tried to eliminate such fiddle factors, typically b
 Among the questions to be addressed by the workshop are:
 
 * Is the prominence of tuning parameters a fundamental feature of stochastic optimization problems? Why do classic optimization methods manage to do well with virtually no free parameters?
-* In which precise sense can the "optimization of optimization algorithms" be phrased as an inference / learning problem? 
+* In which precise sense can the "optimization of optimization algorithms" be phrased as an inference / learning problem?
 * Should, and can, parameters be inferred at design-time (by a human), at compile-time (by an external compiler with access to a meta-description of the problem) or run-time (by the algorithm itself)?
-* What are generic ways to learn parameters of algorithms, and inherent difficulties for doing so? Is the goal to *specialize* to a particular problem, or to *generalize* over many problems? 
+* What are generic ways to learn parameters of algorithms, and inherent difficulties for doing so? Is the goal to *specialize* to a particular problem, or to *generalize* over many problems?
 
 
 ### Schedule
@@ -56,7 +56,7 @@ The workshop will be held on Saturday, 10 December, in *Area 2*
 | **16:20-17:00** | --- |  *Panel Discussion* | |
 
 
-## Accepted Papers 
+## Accepted Papers
 (in alphabetical order, by first author's surname)
 
 * Ömer Deniz Akyildiz, Víctor Elvira, Jesus Fernandez-Bes, Joaquín Miguez. [*On the Relationship between Online Optimizers and Recursive Filters*]({{ site.baseurl }}/assets/pdf/meetings/2016_NeurIPS/Akyildiz_Elvira_Fernandez-Bes_Miguez.pdf)
@@ -79,7 +79,7 @@ The workshop will be held on Saturday, 10 December, in *Area 2*
 ### Topics of interest
 
 * Parameter adaptation for optimization algorithms
-* Stochastic optimization methods 
+* Stochastic optimization methods
 * Optimization methods adapted for specific applications
 * Batch selection methods
 * Convergence diagnostics for optimization algorithms
@@ -88,6 +88,6 @@ We welcome contributions from theoretical treatments, empirical studies, and app
 
 ### Submission instructions
 
-Submissions should be in the (new!) NIPS 2016 format, with a maximum of 4 pages (excluding references). Accepted papers will be made available online at the workshop website, and will be presented in a spotlight talk at the workshop itself, but the workshop proceedings can be considered non-archival. Explicitly: shorter versions of relevant papers submitted or published elsewhere are encouraged. Submissions need not be anonymous. 
+Submissions should be in the (new!) NIPS 2016 format, with a maximum of 4 pages (excluding references). Accepted papers will be made available online at the workshop website, and will be presented in a spotlight talk at the workshop itself, but the workshop proceedings can be considered non-archival. Explicitly: shorter versions of relevant papers submitted or published elsewhere are encouraged. Submissions need not be anonymous.
 
 Please mail pdf submissions to <[email protected]>. -->