Update citation files with preprint (#715)

* Add preprint as `preferred-citation`

* Update citations in README

* Better Zenodo URL

* Add tip to sparse AD docs

* Address review

Author: adrhill

CITATION.cff

@@ -56,4 +56,20 @@ references:
         description: Arxiv
     repository-code: 'https://github.com/JuliaDiff/AbstractDifferentiation.jl'
     abstract: An abstract interface for automatic differentiation.
-    license: MIT
+    license: MIT
+preferred-citation:
+  type: generic
+  authors:
+  - family-names: "Hill"
+    given-names: "Adrian"
+    orcid: "https://orcid.org/0009-0009-5977-301X"
+  - family-names: "Dalle"
+    given-names: "Guillaume"
+    orcid: "https://orcid.org/0000-0003-4866-1687"
+  title: "Sparser, Better, Faster, Stronger: Efficient Automatic Differentiation for Sparse Jacobians and Hessians"
+  year: 2025
+  eprint: "2501.17737"
+  archivePrefix: "arXiv"
+  primaryClass: "cs.LG"
+  url: "https://arxiv.org/abs/2501.17737"
+  doi: "10.48550/arXiv.2501.17737"

DifferentiationInterface/README.md

@@ -90,4 +90,40 @@ To improve your performance by up to several orders of magnitude compared to thi
 
 ## Citation
 
-Please cite both DifferentiationInterface.jl and its inspiration [AbstractDifferentiation.jl](https://github.com/JuliaDiff/AbstractDifferentiation.jl), using the provided `CITATION.cff` file.
+Whenever you refer to this package or the ideas it contains, please cite our preprint [*Sparser, Better, Faster, Stronger: Efficient Automatic Differentiation for Sparse Jacobians and Hessians*](https://arxiv.org/abs/2501.17737) and DifferentiationInterface.jl's inspiration [AbstractDifferentiation.jl](https://github.com/JuliaDiff/AbstractDifferentiation.jl).
+You can use the provided [`CITATION.cff`](https://github.com/JuliaDiff/DifferentiationInterface.jl/blob/main/CITATION.cff) file or the following BibTeX entries:
+
+```bibtex   
+@misc{hill2025sparserbetterfasterstronger,
+      title={Sparser, Better, Faster, Stronger: Efficient Automatic Differentiation for Sparse Jacobians and Hessians}, 
+      author={Adrian Hill and Guillaume Dalle},
+      year={2025},
+      eprint={2501.17737},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2501.17737}, 
+}
+
+@misc{schäfer2022abstractdifferentiationjlbackendagnosticdifferentiableprogramming,
+      title={AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming in Julia}, 
+      author={Frank Schäfer and Mohamed Tarek and Lyndon White and Chris Rackauckas},
+      year={2022},
+      eprint={2109.12449},
+      archivePrefix={arXiv},
+      primaryClass={cs.MS},
+      url={https://arxiv.org/abs/2109.12449}, 
+}
+```
+
+If you use the software, additionally cite us using the precise [Zenodo DOI](https://zenodo.org/records/11092033) of the package version you used, or the BibTeX entry below:
+
+```bibtex
+@software{dalleDifferentiationInterface2025,
+      author={Dalle, Guillaume and Hill, Adrian},
+      title={Differentiation{I}nterface.jl},
+      year={2024},
+      publisher={Zenodo},
+      doi={10.5281/zenodo.11092033},
+      url={https://doi.org/10.5281/zenodo.11092033},
+}
+```

DifferentiationInterface/docs/src/tutorials/advanced.md

@@ -55,6 +55,10 @@ For additional arguments which act as mutated buffers, the [`Cache`](@ref) wrapp
 
 ## Sparsity
 
+!!! tip
+    If you use DifferentiationInterface's Sparse AD functionality in your research,
+    please cite our preprint [*Sparser, Better, Faster, Stronger: Efficient Automatic Differentiation for Sparse Jacobians and Hessians*](https://arxiv.org/abs/2501.17737).
+
 Sparse AD is very useful when Jacobian or Hessian matrices have a lot of zeros.
 So let us write functions that satisfy this property.