update references

CITATIONS.bib

@@ -1,3 +1,18 @@
+@inproceedings{bjorklund2024slipmap,
+  title      = {{{SLIPMAP}}: {{Fast}} and {{Robust Manifold Visualisation}} for {{Explainable AI}}},
+  shorttitle = {{{SLIPMAP}}},
+  booktitle  = {Advances in {{Intelligent Data Analysis XXII}}},
+  author     = {Bj{\"o}rklund, Anton and Sepp{\"a}l{\"a}inen, Lauri and Puolam{\"a}ki, Kai},
+  year       = {2024},
+  series     = {Lecture {{Notes}} in {{Computer Science}}},
+  volume     = {14642},
+  pages      = {223--235},
+  publisher  = {Springer Nature Switzerland},
+  address    = {Cham},
+  doi        = {10.1007/978-3-031-58553-1_18},
+  isbn       = {978-3-031-58553-1}
+}
+
 @article{bjorklund2024slisemap,
   title      = {{SLISEMAP}: Supervised dimensionality reduction through local explanations},
   shorttitle = {{SLISEMAP}},

README.md

@@ -18,6 +18,11 @@ SLIPMAP is largely compatible with SLISEMAP, just change the class name (`Slisem
 
 ## Citations
 
+The new SLIPMAP paper ([supplements](https://github.com/edahelsinki/slisemap/tree/slipmap_experiments) and [slides](https://github.com/edahelsinki/slisemap/blob/data/slides/slipmap_slides.pdf)):
+> *Björklund, A., Seppäläinen, L., & Puolamäki, K. (2024).*  
+> **SLIPMAP: Fast and Robust Manifold Visualisation for Explainable AI**  
+> Advances in Intelligent Data Analysis XXII, IDA 2024, pp. 223-235. Lecture Notes in Computer Science, vol 14642. DOI: [10.1007/978-3-031-58553-1_18](https://doi.org/10.1007/978-3-031-58553-1_18)  
+
 The full SLISEMAP paper ([arXiv](https://arxiv.org/abs/2201.04455), [supplements](https://github.com/edahelsinki/slisemap/tree/slisemap_experiments), and [slides](https://github.com/edahelsinki/slisemap/blob/data/slides/slisemap_slides.pdf)):
 > *Björklund, A., Mäkelä, J., & Puolamäki, K. (2023).*  
 > **SLISEMAP: Supervised dimensionality reduction through local explanations.**  
@@ -26,12 +31,7 @@ The full SLISEMAP paper ([arXiv](https://arxiv.org/abs/2201.04455), [supplements
 The short demo paper ([video](https://youtu.be/zvcFYItwRlQ) and [slides](https://github.com/edahelsinki/slisemap/blob/data/slides/demo_slides.pdf)):
 > *Björklund, A., Mäkelä, J., & Puolamäki, K. (2023).*  
 > **SLISEMAP: Combining Supervised Dimensionality Reduction with Local Explanations.**  
-> Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science, vol 13718. DOI: [10.1007/978-3-031-26422-1_41](https://doi.org/10.1007/978-3-031-26422-1_41).
-
-The new SLIPMAP paper ([supplements](https://github.com/edahelsinki/slisemap/tree/slipmap_experiments)):
-> *Björklund, A., Seppäläinen, L., & Puolamäki, K. (2024).*  
-> **SLIPMAP: Fast and Robust Manifold Visualisation for Explainable AI**  
-> To appear in: Advances in Intelligent Data Analysis XXII. IDA 2024. Lecture Notes in Computer Science.  
+> Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022. Lecture Notes in Computer Science, vol 13718. DOI: [10.1007/978-3-031-26422-1_41](https://doi.org/10.1007/978-3-031-26422-1_41).
 
 
 ## Installation

mkdocs.yml

@@ -47,6 +47,9 @@ nav:
   - Links:
       - GitHub: https://github.com/edahelsinki/slisemap
       - PyPI: https://pypi.org/project/slisemap/
+      - Paper (Slipmap):
+          - Paper: https://doi.org/10.1007/978-3-031-58553-1_18
+          - Slides: https://github.com/edahelsinki/slisemap/blob/data/slides/slipmap_slides.pdf
       - Paper (Slisemap):
           - Paper: https://doi.org/10.1007/s10994-022-06261-1
           - Slides: https://github.com/edahelsinki/slisemap/blob/data/slides/slisemap_slides.pdf

pyproject.toml

@@ -4,11 +4,12 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "slisemap"
-version = "1.6.1"
+version = "1.6.2"
 authors = [
     { name = "Anton Björklund", email = "anton.bjorklund@helsinki.fi" },
-    { name = "Jarmo Mäkelä" },
     { name = "Kai Puolamäki", email = "kai.puolamaki@helsinki.fi" },
+    { name = "Lauri Seppäläinen" },
+    { name = "Jarmo Mäkelä" },
 ]
 description = "SLISEMAP: Combine local explanations with supervised dimensionality reduction"
 readme = "README.md"

slisemap/__init__.py

@@ -16,7 +16,7 @@
 This module also includes the faster SLIPMAP variant, that uses "prototypes" to speed up
 the calculations (linear time and memory complexity instead of quadratic).
 SLIPMAP is largely compatible with SLISEMAP, just change the class name (`Slisemap` to `Slipmap`).
-For more information, see the sources above and the [paper](TODO).
+For more information, see the sources above and the [paper](https://doi.org/10.1007/978-3-031-58553-1_18).
 
 Documentation
 -------------
@@ -27,13 +27,14 @@
 
 Citations
 ---------
-> Björklund, A., Mäkelä, J. & Puolamäki, K. (2023).
-> SLISEMAP: Supervised dimensionality reduction through local explanations.
-> Machine Learning 112, 1-43. DOI: 10.1007/s10994-022-06261-1.
+> Björklund, A., Mäkelä, J. & Puolamäki, K. (2023).  
+> SLISEMAP: Supervised dimensionality reduction through local explanations.  
+> Machine Learning 112, 1-43. DOI: 10.1007/s10994-022-06261-1.  
 
-> Björklund, A., Seppäläinen, L., & Puolamäki, K. (2024).
-> SLIPMAP: Fast and Robust Manifold Visualisation for Explainable AI
-> To appear in: Advances in Intelligent Data Analysis XXII. IDA 2024. Lecture Notes in Computer Science.
+> Björklund, A., Seppäläinen, L., & Puolamäki, K. (2024).  
+> SLIPMAP: Fast and Robust Manifold Visualisation for Explainable AI  
+> Advances in Intelligent Data Analysis XXII, pp. 223-235, LNCS 14642.  
+> DOI: 10.1007/978-3-031-58553-1_18.  
 
 
 Example Usage
@@ -47,7 +48,7 @@
     sm = Slipmap(X, y, radius=2.0, lasso=1e-4, ridge=2e-4)
     sm.optimise()
     sm.plot()
-"""  # noqa: D205
+"""  # noqa: D205, W291
 
 from slisemap.slipmap import Slipmap  # noqa: F401
 from slisemap.slisemap import Slisemap  # noqa: F401