{% endif %}
-
\ No newline at end of file
+
diff --git a/_layouts/bib.html b/_layouts/bib.html
index d31f086..e79cbcf 100644
--- a/_layouts/bib.html
+++ b/_layouts/bib.html
@@ -36,8 +36,9 @@
{%- capture venue -%}
- {%- if entry.type == "article" -%}{{ entry.journal }}
- {%- elsif entry.type == "inproceedings" -%}In {{ entry.booktitle }}
+ {%- 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 %}
diff --git a/_meetings/2015_DALI.md b/_meetings/2015_DALI.md
index 6d550bf..eaa64f8 100644
--- a/_meetings/2015_DALI.md
+++ b/_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:
diff --git a/_meetings/2015_NeurIPS.md b/_meetings/2015_NeurIPS.md
index 889b1c4..b17c8a4 100644
--- a/_meetings/2015_NeurIPS.md
+++ b/_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 . -->
diff --git a/_meetings/2015_SciCADE.md b/_meetings/2015_SciCADE.md
index 69c6e89..e2fbacb 100644
--- a/_meetings/2015_SciCADE.md
+++ b/_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:
diff --git a/_meetings/2016_MCMSki.md b/_meetings/2016_MCMSki.md
index 9dc088a..672dac4 100644
--- a/_meetings/2016_MCMSki.md
+++ b/_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:
diff --git a/_meetings/2016_MCQMC.md b/_meetings/2016_MCQMC.md
index f6b1524..80c1b41 100644
--- a/_meetings/2016_MCQMC.md
+++ b/_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:
@@ -24,14 +24,14 @@ 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?**
>
@@ -39,14 +39,14 @@ The workshop will be held on Thursday, 18 August 15:50-17:50, at Stanford Univer
> 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.
> 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?**
>
> *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.
> [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.**
>
@@ -65,13 +65,13 @@ $$
>
> *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.
> 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.
> [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.
diff --git a/_meetings/2016_NeurIPS.md b/_meetings/2016_NeurIPS.md
index 5fe3f68..eb978de 100644
--- a/_meetings/2016_NeurIPS.md
+++ b/_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 . -->
diff --git a/_meetings/2020_SIAMUQ.md b/_meetings/2020_SIAMUQ.md
index 64b70fc..af32ede 100644
--- a/_meetings/2020_SIAMUQ.md
+++ b/_meetings/2020_SIAMUQ.md
@@ -9,11 +9,11 @@ organizers: [["Alex", "Diaz"], ["Chris", "Oates"], ["Toni", "Karvonen"], ["Alexa
date: 2020-03-24
date_end: 2020-03-27
-summary:
+summary:
link: https://siam-uq20.ma.tum.de/
img:
-img_caption:
+img_caption:
url_code:
url_pdf:
@@ -30,22 +30,22 @@ Nevertheless, many speakers provided a recording of their presentation. *Huge th
### [Probabilistic Numerical Methods for Cubature](https://www.events.tum.de/frontend/index.php?page_id=3712&v=List&do=15&day=489&ses=1659#anker_session_1659)
-**The Successes and Challenges of Automatic Bayesian Cubature**
+**The Successes and Challenges of Automatic Bayesian Cubature**
[Fred J. Hickernell (Illinois Institute of Technology, US)](http://mypages.iit.edu/~hickernell/)
-**Gaussian processes and Uncertainty Quantification**
+**Gaussian processes and Uncertainty Quantification**
[Toni Karvonen (Alan Turing Institute, UK)](https://www.turing.ac.uk/people/researchers/toni-karvonen)
-**Integrals of Linearly Constrained Gaussians**
+**Integrals of Linearly Constrained Gaussians**
[Alexandra Gessner (University of Tübingen, Germany)](https://www.is.mpg.de/person/agessner)
-**Adaptivity in Bayesian Cubature**
+**Adaptivity in Bayesian Cubature**
[Matthew Fisher (Newcastle University, UK)](https://www.ncl.ac.uk/bigdata/people/people/fishermatthew.html)
@@ -55,21 +55,21 @@ Nevertheless, many speakers provided a recording of their presentation. *Huge th
### [Probabilistic Numerical Methods: Opportunities and Challenges](https://www.events.tum.de/frontend/index.php?page_id=3712&v=List&do=15&day=489&ses=1660#anker_session_1660)
-**Probabilistic Numerics: History and Recent Trends**
+**Probabilistic Numerics: History and Recent Trends**
[Tim Sullivan (Free University of Berlin, Germany)](http://www.tjsullivan.org.uk/)
-**It is Time to Take Uncertainty Seriously**
+**It is Time to Take Uncertainty Seriously**
[Philipp Hennig (University of Tübingen, Germany)](https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/methoden-des-maschinellen-lernens/personen/philipp-hennig/)
-**Fast Bayesian Inference for Differential Equations Using Probabilistic Numerical Methods**
+**Fast Bayesian Inference for Differential Equations Using Probabilistic Numerical Methods**
[Chris Oates (Newcastle Univeristy, UK)](http://oates.work/)
-**Active Multi-Source Bayesian Quadrature for Expensive Simulations**
+**Active Multi-Source Bayesian Quadrature for Expensive Simulations**
[Maren Mahsereci (Amazon Research, UK)]()
@@ -78,23 +78,23 @@ Nevertheless, many speakers provided a recording of their presentation. *Huge th
### [Probabilistic Numerical Methods for Differential Equations and Linear Algebra, Part I](https://www.events.tum.de/frontend/index.php?page_id=3712&v=List&do=15&day=490&ses=1662#anker_session_1662)
-**Prior Distributions and Test Statistics for the Bayesian Conjugate Gradient Method**
+**Prior Distributions and Test Statistics for the Bayesian Conjugate Gradient Method**
[Tim Reid (North Carolina State University, US)](https://math.sciences.ncsu.edu/people/twreid/)
-**A Probabilistic View on Sparse Cholesky Factorization**
+**A Probabilistic View on Sparse Cholesky Factorization**
[Florian Schäfer ( California Institute of Technology, US)](https://f-t-s.github.io/)
-**Estimation of Ordinary Differential Equation Models with Discretization Error Quantification**
+**Estimation of Ordinary Differential Equation Models with Discretization Error Quantification**
[Takeru Matsuda (University of Tokyo, Japan)](http://www.stat.t.u-tokyo.ac.jp/~t-matsuda/index_en.html)
@@ -103,21 +103,21 @@ Nevertheless, many speakers provided a recording of their presentation. *Huge th
### [Probabilistic Numerical Methods for Differential Equations and Linear Algebra, Part II](https://www.events.tum.de/frontend/index.php?page_id=3712&v=List&do=15&day=490&ses=1661#anker_session_1661)
-**On the Role of Exponential Integrability of Probabilistic Integrators for Approximate Bayesian Inference**
+**On the Role of Exponential Integrability of Probabilistic Integrators for Approximate Bayesian Inference**
[Han Cheng Lie ( University of Potsdam, Germany)](https://sites.google.com/site/hanchenglie)
-**BVPs, Computational Pipelines and a Probabilistic Numerics GOODE**
+**BVPs, Computational Pipelines and a Probabilistic Numerics GOODE**
[Michael Schober (Bosch, Germany)](http://michaelschober.net/)
-**Probabilistic Solutions to Ordinary Differential Equations as Non-Linear Bayesian Filtering and Smoothing: Gaussian Approximation**
+**Probabilistic Solutions to Ordinary Differential Equations as Non-Linear Bayesian Filtering and Smoothing: Gaussian Approximation**
[Filip Tronarp (University of Tübingen, Germany)]()
-**Approximate Bayesian Solutions to Nonlinear Differential Equations**
+**Approximate Bayesian Solutions to Nonlinear Differential Equations**
[Junyang Wang (Newcastle Univeristy, UK)](https://www.ncl.ac.uk/bigdata/people/people/wangjunyang.html)
diff --git a/_meetings/2021_Dagstuhl.md b/_meetings/2021_Dagstuhl.md
index d4ec74e..2e54768 100644
--- a/_meetings/2021_Dagstuhl.md
+++ b/_meetings/2021_Dagstuhl.md
@@ -33,4 +33,4 @@ The goals of this Dagstuhl Seminar are thus two-fold. First, we want to rekindle
## Schedule
-
\ No newline at end of file
+
diff --git a/_meetings/2023_SpringSchool.md b/_meetings/2023_SpringSchool.md
index 7fd8d5c..859c00b 100644
--- a/_meetings/2023_SpringSchool.md
+++ b/_meetings/2023_SpringSchool.md
@@ -12,8 +12,8 @@ date_end: 2023-03-29
summary: The first ever probabilistic numerics spring school and research workshop aimed at graduate students, researchers, and professionals interested in probabilistic numerical methods.
link: https://www.probnumschool.org/
-img:
-img_caption:
+img:
+img_caption:
url_code:
url_pdf:
diff --git a/_meetings/2024_ProbNum.md b/_meetings/2024_ProbNum.md
index e2461af..085ce72 100644
--- a/_meetings/2024_ProbNum.md
+++ b/_meetings/2024_ProbNum.md
@@ -13,7 +13,7 @@ summary: The Probabilistic Numerics Community Meeting 2024, held at St Pancras M
link: https://probnum24.github.io
# img: https://probnum25.github.io/EURECOM-building-updated.jpg
-img_caption:
+img_caption:
url_code:
url_pdf:
@@ -23,4 +23,4 @@ url_video:
# More Information
-Find more information at [https://probnum24.github.io](https://probnum24.github.io).
\ No newline at end of file
+Find more information at [https://probnum24.github.io](https://probnum24.github.io).
diff --git a/_meetings/2024_ProbNumSchool.md b/_meetings/2024_ProbNumSchool.md
index 443806d..78713c6 100644
--- a/_meetings/2024_ProbNumSchool.md
+++ b/_meetings/2024_ProbNumSchool.md
@@ -12,8 +12,8 @@ date_end: 2024-04-10
summary: The Probabilistic Numerics Spring School 2024, held at Southampton University's Highfield Campus.
link: https://probnumschool.org/pages/home.html
-img:
-img_caption:
+img:
+img_caption:
url_code:
url_pdf:
diff --git a/_meetings/2025_ProbNum.md b/_meetings/2025_ProbNum.md
index 90df7a8..6b82336 100644
--- a/_meetings/2025_ProbNum.md
+++ b/_meetings/2025_ProbNum.md
@@ -12,8 +12,8 @@ date_end: 2025-09-03
summary: The first annual International Conference on Probabilistic Numerics.
link: https://probnum25.github.io/
-img:
-img_caption:
+img:
+img_caption:
url_code:
url_pdf:
diff --git a/_pages/research/other.md b/_pages/research/other.md
index 096abd9..b50942c 100644
--- a/_pages/research/other.md
+++ b/_pages/research/other.md
@@ -1,6 +1,6 @@
---
layout: redirect
permalink: /research/other/
-redirect_to: /research/?tags=related
+redirect_to: /research/?tags=other
sitemap: false
---
diff --git a/_sass/_base.scss b/_sass/_base.scss
index a11b006..c166485 100644
--- a/_sass/_base.scss
+++ b/_sass/_base.scss
@@ -521,4 +521,4 @@ div, iframe {
iframe {
display: block;
border-style:none;
-}
\ No newline at end of file
+}
diff --git a/_sass/_layout.scss b/_sass/_layout.scss
index cb01d3b..cb9023a 100644
--- a/_sass/_layout.scss
+++ b/_sass/_layout.scss
@@ -45,5 +45,3 @@
*****************************************************************************/
// TODO: redefine projects layout.
-
-
diff --git a/_sass/_publications.scss b/_sass/_publications.scss
index 8976a22..332f030 100644
--- a/_sass/_publications.scss
+++ b/_sass/_publications.scss
@@ -212,7 +212,7 @@ $rule: color-mix(in srgb, var(--global-text-color) 12%, transparent);
.author {
font-size: 0.9rem;
- color: var(--global-text-color-light);
+ color: #666;
a {
color: inherit;
@@ -230,7 +230,7 @@ $rule: color-mix(in srgb, var(--global-text-color) 12%, transparent);
.periodical {
font-size: 0.85rem;
- color: var(--global-text-color-light);
+ color: #666;
}
// Links and topic tags share one line below the venue.
diff --git a/_teaching/2022_Numerics_of_Machine_Learning.md b/_teaching/2022_Numerics_of_Machine_Learning.md
index 532d433..58ec459 100644
--- a/_teaching/2022_Numerics_of_Machine_Learning.md
+++ b/_teaching/2022_Numerics_of_Machine_Learning.md
@@ -10,14 +10,14 @@ date_end: 2023-03-30
summary: A master class on Numerics of Machine Learning at the University of Tübingen in the Winter Term of 2022/23.
-link:
+link:
img: /assets/img/teaching/2022_numerics_of_ml.png
-img_caption:
+img_caption:
url_video: https://youtube.com/playlist?list=PL05umP7R6ij2lwDdj7IkuHoP9vHlEcH0s
url_slides: https://github.com/philipphennig/NumericsOfML
url_pdf:
-url_code:
+url_code:
---
## Summary
@@ -34,9 +34,9 @@ This class discusses both practical and theoretically interesting aspects of the
@techreport{NoML22,
title = {Numerics of Machine Learning},
- author = {N. Bosch and J. Grosse and P. Hennig and A. Kristiadi and M. Pförtner
- and J. Schmidt and F. Schneider and L. Tatzel and J. Wenger},
- series = {Lecture Notes in Machine Learning},
+ author = {N. Bosch and J. Grosse and P. Hennig and A. Kristiadi and M. Pförtner
+ and J. Schmidt and F. Schneider and L. Tatzel and J. Wenger},
+ series = {Lecture Notes in Machine Learning},
year = {2022},
institution = {Tübingen AI Center},
}
diff --git a/_textbooks/probabilistic_numerics_pnmo.md b/_textbooks/probabilistic_numerics_pnmo.md
index 3989d37..af43a43 100644
--- a/_textbooks/probabilistic_numerics_pnmo.md
+++ b/_textbooks/probabilistic_numerics_pnmo.md
@@ -6,10 +6,10 @@ authors: Philipp Hennig, Maike Osborne, Hans Kersting
description: >
Probabilistic numerical computation formalises the connection between machine learning and applied mathematics. Numerical algorithms approximate intractable quantities from computable ones. They estimate integrals from evaluations of the integrand, or the path of a dynamical system described by differential equations from evaluations of the vector field. In other words, they infer a latent quantity from data. This book shows that it is thus formally possible to think of computational routines as learning machines, and to use the notion of Bayesian inference to build more flexible, efficient, or customised algorithms for computation. The text caters for Masters' and PhD students, as well as postgraduate researchers in artificial intelligence, computer science, statistics, and applied mathematics. Extensive background material is provided along with a wealth of figures, worked examples, and exercises (with solutions) to develop intuition.
- Available for order at your local bookseller, at Amazon, or at CUP.
+ Available for order at your local bookseller, at Amazon, or at CUP.
A free electronic version for personal use only is available here, or by clicking on the image on the right.
-
+
img: /assets/img/textbooks/ProbabilisticNumercs_cover_small.png
img-link: ../assets/ProbabilisticNumerics.pdf
---