Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

[Merged by Bors] - Add acknowledgements #131

Closed
wants to merge 2 commits into from
Closed

[Merged by Bors] - Add acknowledgements #131

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
ac7b088
Add acknowledgements
devmotion Feb 8, 2022
9443b45
Remove linebreaks
devmotion Feb 8, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
9 changes: 6 additions & 3 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -49,10 +49,9 @@ statistical hypothesis tests of calibration.

The slides of the talk are available as [Pluto notebook](https://talks.widmann.dev/2021/07/calibration/).

## References
## Citing

If you use CalibrationErrors.jl as part of your research, teaching, or other activities,
please consider citing the following publications:
If you use CalibrationErrors.jl as part of your research, teaching, or other activities, please consider citing the following publications:

Widmann, D., Lindsten, F., & Zachariah, D. (2019). [Calibration tests in multi-class
classification: A unifying framework](https://proceedings.neurips.cc/paper/2019/hash/1c336b8080f82bcc2cd2499b4c57261d-Abstract.html). In
Expand All @@ -61,3 +60,7 @@ classification: A unifying framework](https://proceedings.neurips.cc/paper/2019/
Widmann, D., Lindsten, F., & Zachariah, D. (2021).
[Calibration tests beyond classification](https://openreview.net/forum?id=-bxf89v3Nx).
*International Conference on Learning Representations (ICLR 2021)*.

## Acknowledgements

This work was financially supported by the Swedish Research Council via the projects *Learning of Large-Scale Probabilistic Dynamical Models* (contract number: 2016-04278), *Counterfactual Prediction Methods for Heterogeneous Populations* (contract number: 2018-05040), and *Handling Uncertainty in Machine Learning Systems* (contract number: 2020-04122), by the Swedish Foundation for Strategic Research via the project *Probabilistic Modeling and Inference for Machine Learning* (contract number: ICA16-0015), by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation, and by ELLIIT.
7 changes: 5 additions & 2 deletions docs/src/index.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -25,8 +25,7 @@ The slides of the talk are available as [Pluto notebook](https://talks.widmann.d

## Citing

If you use CalibrationErrors.jl as part of your research, teaching, or other activities,
please consider citing the following publications:
If you use CalibrationErrors.jl as part of your research, teaching, or other activities, please consider citing the following publications:

Widmann, D., Lindsten, F., & Zachariah, D. (2019). [Calibration tests in multi-class
classification: A unifying framework](https://proceedings.neurips.cc/paper/2019/hash/1c336b8080f82bcc2cd2499b4c57261d-Abstract.html). In
Expand All @@ -35,3 +34,7 @@ classification: A unifying framework](https://proceedings.neurips.cc/paper/2019/
Widmann, D., Lindsten, F., & Zachariah, D. (2021).
[Calibration tests beyond classification](https://openreview.net/forum?id=-bxf89v3Nx).
*International Conference on Learning Representations (ICLR 2021)*.

## Acknowledgements

This work was financially supported by the Swedish Research Council via the projects *Learning of Large-Scale Probabilistic Dynamical Models* (contract number: 2016-04278), *Counterfactual Prediction Methods for Heterogeneous Populations* (contract number: 2018-05040), and *Handling Uncertainty in Machine Learning Systems* (contract number: 2020-04122), by the Swedish Foundation for Strategic Research via the project *Probabilistic Modeling and Inference for Machine Learning* (contract number: ICA16-0015), by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation, and by ELLIIT.