Updated publications

Pseudomanifold · Pseudomanifold · commit 874d2c58b98f · 2025-10-20T10:08:19.000+02:00
diff --git a/content/publications.md b/content/publications.md
diff --git a/static/research/Adamer24a.bib b/static/research/Adamer24a.bib
@@ -30,6 +30,7 @@ @article{Adamer24a
 	doi = {10.1007/s41468-024-00182-9},
 	eprint = {2110.15188},
 	journal = {Journal of Applied and Computational Topology},
+	keywords = {TOP-IF},
 	number = {3},
 	pages = {447--473},
 	primaryclass = {cs.LG},
diff --git a/static/research/Alain24a.bib b/static/research/Alain24a.bib
@@ -1,7 +1,7 @@
 @inproceedings{Alain24a,
 	author = {Mathieu Alain and So Takao and Bastian Rieck and Xiaowen Dong and Emmanuel Noutahi},
 	author+an = {3=highlight},
-	booktitle = {`Bayesian Decision-Making and Uncertainty' Workshop at NeurIPS},
+	booktitle = {``Bayesian Decision-Making and Uncertainty'' Workshop at NeurIPS},
 	title = {Graph Classification Gaussian Processes via Hodgelet Spectral Features},
 	url = {https://openreview.net/forum?id=zgLOn7f2Lp},
 	year = {2024},
diff --git a/static/research/Amboage25a.bib b/static/research/Amboage25a.bib
@@ -0,0 +1,9 @@
+@unpublished{Amboage25a,
+	archiveprefix = {arXiv},
+	author = {Juan Amboage and Ernst Röell and Patrick Schnider and Bastian Rieck},
+	author+an = {4=highlight},
+	eprint = {2510.00757},
+	primaryclass = {cs.LG},
+	title = {LEAP: Local ECT-Based Learnable Positional Encodings for Graphs},
+	year = {2025},
+}
diff --git a/static/research/Bock19b.bib b/static/research/Bock19b.bib
@@ -1,7 +1,7 @@
 @inproceedings{Bock19b,
 	author = {Christian Bock and Matteo Togninalli and Elisabetta Ghisu and Thomas Gumbsch and Bastian Rieck and Karsten Borgwardt},
 	author+an = {1=first; 2=first; 5=highlight},
-	booktitle = {`Optimal Transport \& Machine Learning' Workshop at NeurIPS},
+	booktitle = {``Optimal Transport \& Machine Learning'' Workshop at NeurIPS},
 	repository = {https://github.com/BorgwardtLab/WTK},
 	title = {A {W}asserstein Subsequence Kernel for Time Series},
 	url = {https://bastian.rieck.me/research/OTML2019_WTK.pdf},
diff --git a/static/research/Brueningk20a.bib b/static/research/Brueningk20a.bib
@@ -2,7 +2,7 @@ @inproceedings{Brueningk20a
 	archiveprefix = {arXiv},
 	author = {Sarah C. Brüningk and Felix Hensel and Catherine R. Jutzeler and Bastian Rieck},
 	author+an = {1=first; 2=first; 3=last; 4=last,highlight},
-	booktitle = {`Machine Learning for Health~(ML4H)' Workshop at NeurIPS},
+	booktitle = {``Machine Learning for Health~(ML4H)'' Workshop at NeurIPS},
 	eprint = {2011.06531},
 	primaryclass = {cs.LG},
 	repository = {https://github.com/BorgwardtLab/TDA-CNN-AD},
diff --git a/static/research/Brueningk20b.bib b/static/research/Brueningk20b.bib
@@ -1,7 +1,7 @@
 @inproceedings{Brueningk20b,
 	author = {Sarah C. Brüningk and Felix Hensel and Catherine R. Jutzeler and Bastian Rieck},
 	author+an = {1=first; 2=first; 3=last; 4=last,highlight},
-	booktitle = {`Medical Imaging meets NeurIPS' Workshop at NeurIPS},
+	booktitle = {``Medical Imaging meets NeurIPS'' Workshop at NeurIPS},
 	title = {Scalable Solutions for {MR} Image Classification of {A}lzheimer's Disease},
 	year = {2020},
 }
diff --git a/static/research/Coupette25a.bib b/static/research/Coupette25a.bib
@@ -3,12 +3,16 @@ @inproceedings{Coupette25a
 	author = {Corinna Coupette and Jeremy Wayland and Emily Simons and Bastian Rieck},
 	author+an = {1=first; 2=first; 4=highlight},
 	booktitle = {Proceedings of the 42nd International Conference on Machine Learning},
+	editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry},
 	eprint = {2502.02379},
 	keywords = {CORE-A*},
+	pages = {11405--11434},
 	primaryclass = {cs.LG},
 	publisher = {PMLR},
-	pubstate = {inpress},
+	repository = {https://github.com/aidos-lab/rings},
 	series = {Proceedings of Machine Learning Research},
 	title = {No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets},
+	url = {https://proceedings.mlr.press/v267/coupette25a.html},
+	volume = {267},
 	year = {2025},
 }
diff --git a/static/research/Dyer22a.bib b/static/research/Dyer22a.bib
@@ -1,7 +1,7 @@
 @inproceedings{Dyer22a,
 	author = {Joel Dyer and John Fitzgerald and Bastian Rieck and Sebastian M. Schmon},
 	author+an = {3=highlight},
-	booktitle = {`Temporal Graph Learning' Workshop at NeurIPS},
+	booktitle = {``Temporal Graph Learning'' Workshop at NeurIPS},
 	title = {Approximate {B}ayesian Computation for Panel Data with Signature Maximum Mean Discrepancies},
 	url = {https://openreview.net/pdf?id=Bol45H5FAc},
 	year = {2022},
diff --git a/static/research/Gao21.bib b/static/research/Gao21.bib
@@ -1,7 +1,7 @@
 @inproceedings{Gao21,
 	author = {Feng Gao and Jessica Moore and Bastian Rieck and Valentina Greco and Smita Krishnaswamy},
 	author+an = {3=highlight},
-	booktitle = {`Geometrical and Topological Representation Learning' Workshop at ICLR},
+	booktitle = {``Geometrical and Topological Representation Learning'' Workshop at ICLR},
 	title = {Exploring epithelial-cell calcium signaling with geometric and topological data analysis},
 	url = {https://openreview.net/forum?id=8V6vm_yuwEv},
 	year = {2021},
diff --git a/static/research/Ghalamkari21.bib b/static/research/Ghalamkari21.bib
@@ -4,8 +4,7 @@ @article{Ghalamkari21
 	doi = {10.11517/jjsai.36.4_421},
 	journal = {Journal of the Japanese Society for Artificial Intelligence},
 	language = {langjapanese and langenglish},
-	note = {This article constitutes an abridged translation of our survey
-`Graph Kernels: State-of-the-Art and Future Challenges'},
+	note = {This article constitutes an abridged translation of our survey ``Graph Kernels: State-of-the-Art and Future Challenges''},
 	number = {4},
 	pages = {421--429},
 	repository = {https://github.com/BorgwardtLab/graphkernels-review},
diff --git a/static/research/Horoi22a.bib b/static/research/Horoi22a.bib
@@ -1,30 +1,4 @@
 @inproceedings{Horoi22a,
-	abstract = {Recent work has established clear links between the
-generalization performance of trained neural networks
-and the geometry of their loss landscape near the
-local minima to which they converge. This suggests
-that qualitative and quantitative examination of the
-loss landscape geometry could yield insights about
-neural network generalization performance during
-training. To this end, researchers have proposed
-visualizing the loss landscape through the use of
-simple dimensionality reduction techniques. However,
-such visualization methods have been limited by their
-linear nature and only capture features in one or two
-dimensions, thus restricting sampling of the loss
-landscape to lines or planes. Here, we expand and
-improve upon these in three ways. First, we present
-a novel ``jump and retrain'' procedure for sampling
-relevant portions of the loss landscape. We show that
-the resulting sampled data holds more meaningful
-information about the network's ability to generalize.
-Next, we show that non-linear dimensionality reduction
-of the jump and retrain trajectories via PHATE,
-a trajectory and manifold-preserving method, allows us
-to visualize differences between networks that are
-generalizing well vs poorly. Finally, we combine PHATE
-trajectories with a computational homology
-characterization to quantify trajectory differences.},
 	address = {Cham, Switzerland},
 	archiveprefix = {arXiv},
 	author = {Stefan Horoi and Jessie Huang and Bastian Rieck and Guillaume Lajoie and Guy Wolf and Smita Krishnaswamy},
diff --git a/static/research/Koke25b.bib b/static/research/Koke25b.bib
@@ -1,7 +1,7 @@
 @inproceedings{Koke25b,
 	author = {Christian Koke and Yuesong Shen and Abhishek Saroha and Marvin Eisenberger and Bastian Rieck and Michael M. Bronstein and Daniel Cremers},
 	author+an = {5=highlight},
-	booktitle = {ICLR Workshop `I Can't Believe It's Not Better: Challenges in Applied Deep Learning'},
+	booktitle = {ICLR Workshop ``I Can't Believe It's Not Better: Challenges in Applied Deep Learning''},
 	title = {Graph Networks Struggle with Variable Scale},
 	url = {https://openreview.net/forum?id=N5n6SAfnU0},
 	year = {2025},
diff --git a/static/research/Kuijs21a.bib b/static/research/Kuijs21a.bib
@@ -2,7 +2,7 @@ @inproceedings{Kuijs21a
 	archiveprefix = {arXiv},
 	author = {Merel Kuijs and Catherine R. Jutzeler and Bastian Rieck and Sarah C. Brüningk},
 	author+an = {3=highlight},
-	booktitle = {`Machine Learning for Health~(ML4H)' Symposium},
+	booktitle = {``Machine Learning for Health~(ML4H)'' Symposium},
 	eprint = {2111.08701},
 	primaryclass = {eess.IV},
 	title = {Interpretability Aware Model Training to Improve Robustness against Out-of-Distribution Magnetic Resonance Images in {A}lzheimer's Disease Classification},
diff --git a/static/research/Limbeck25a.bib b/static/research/Limbeck25a.bib
@@ -1,9 +1,7 @@
-@unpublished{Limbeck25a,
-	archiveprefix = {arXiv},
+@inproceedings{Limbeck25a,
 	author = {Katharina Limbeck and Lydia Mezrag and Guy Wolf and Bastian Rieck},
 	author+an = {1=first; 2=first; 3=last; 4=last,highlight},
-	eprint = {2506.11700},
-	primaryclass = {cs.LG},
+	booktitle = {ECML PKDD Workshop on Mining and Learning with Graphs},
 	title = {Geometry-Aware Edge Pooling for Graph Neural Networks},
 	year = {2025},
 }
diff --git a/static/research/Limbeck25b.bib b/static/research/Limbeck25b.bib
@@ -1,7 +1,14 @@
 @inproceedings{Limbeck25b,
+	archiveprefix = {arXiv},
 	author = {Katharina Limbeck and Lydia Mezrag and Guy Wolf and Bastian Rieck},
 	author+an = {1=first; 2=first; 3=last; 4=last,highlight},
-	booktitle = {ECML PKDD Workshop on Mining and Learning with Graphs},
+	booktitle = {Advances in Neural Information Processing Systems},
+	eprint = {2506.11700},
+	keywords = {CORE-A*},
+	primaryclass = {cs.LG},
+	publisher = {Curran Associates, Inc.},
+	pubstate = {inpress},
 	title = {Geometry-Aware Edge Pooling for Graph Neural Networks},
+	volume = {38},
 	year = {2025},
 }
diff --git a/static/research/Liu21a.bib b/static/research/Liu21a.bib
@@ -2,7 +2,7 @@ @inproceedings{Liu21a
 	archiveprefix = {arXiv},
 	author = {Renming Liu and Semih Cantürk and Frederik Wenkel and Dylan Sandfelder and Devin Kreuzer and Anna Little and Sarah McGuire and Michael Perlmutter and Leslie O'Bray and Bastian Rieck and Matthew Hirn and Guy Wolf and Ladislav Rampášek},
 	author+an = {1=first; 2=first; 10=highlight},
-	booktitle = {`Data-Centric AI' Workshop at NeurIPS},
+	booktitle = {``Data-Centric AI'' Workshop at NeurIPS},
 	eprint = {2110.14809},
 	primaryclass = {cs.LG},
 	title = {Towards a Taxonomy of Graph Learning Datasets},
diff --git a/static/research/MacDonald22a.bib b/static/research/MacDonald22a.bib
@@ -1,7 +1,7 @@
 @inproceedings{MacDonald22a,
 	author = {Kincaid MacDonald and Jennifer Paige and Dawson Thomas and Sarah Zhao and Kisung You and Ian M. Adelstein and Yarif Aizenbud and Bastian Rieck and Dhananjay Bhaskar and Smita Krishnaswamy},
 	author+an = {8=highlight},
-	booktitle = {`Geometrical and Topological Representation Learning' Workshop at ICLR},
+	booktitle = {``Geometrical and Topological Representation Learning'' Workshop at ICLR},
 	title = {Diffusion-Based Methods for Estimating Curvature in Data},
 	url = {https://openreview.net/forum?id=HtLfgBbyagq},
 	year = {2022},
diff --git a/static/research/Moor20c.bib b/static/research/Moor20c.bib
@@ -1,7 +1,7 @@
 @inproceedings{Moor20c,
 	author = {Michael Moor and Max Horn and Karsten Borgwardt and Bastian Rieck},
 	author+an = {4=highlight},
-	booktitle = {`Topological Data Analysis and Beyond' Workshop at NeurIPS},
+	booktitle = {``Topological Data Analysis and Beyond'' Workshop at NeurIPS},
 	repository = {https://github.com/BorgwardtLab/topo-ae-distances},
 	title = {Challenging {E}uclidean Topological Autoencoders},
 	url = {https://openreview.net/forum?id=P3dZuOUnyEY},
diff --git a/static/research/Remme25a.bib b/static/research/Remme25a.bib
@@ -4,7 +4,10 @@ @article{Remme25a
 	doi = {10.1021/jacs.5c06219},
 	eprint = {2503.00443},
 	journal = {Journal of the American Chemical Society},
+	number = {32},
+	pages = {28851--28859},
 	primaryclass = {physics.chem-ph},
 	title = {Stable and Accurate Orbital-Free Density Functional Theory Powered by Machine Learning},
+	volume = {147},
 	year = {2025},
 }
diff --git a/static/research/Rieck25a.bib b/static/research/Rieck25a.bib
@@ -5,6 +5,7 @@ @article{Rieck25a
 	doi = {10.1090/noti3193},
 	eprint = {2410.17760},
 	journal = {Notices of the American Mathematical Society},
+	keywords = {TOP-IF},
 	number = {7},
 	pages = {719--727},
 	primaryclass = {cs.LG},
diff --git a/static/research/Roell25a.bib b/static/research/Roell25a.bib
@@ -1,9 +1,14 @@
-@unpublished{Roell24b,
+@inproceedings{Roell25a,
 	archiveprefix = {arXiv},
 	author = {Ernst R{\"o}ell and Bastian Rieck},
 	author+an = {2=highlight},
+	booktitle = {Advances in Neural Information Processing Systems},
 	eprint = {2410.18987},
+	keywords = {CORE-A*},
 	primaryclass = {cs.CV},
+	publisher = {Curran Associates, Inc.},
+	pubstate = {inpress},
 	title = {Point Cloud Synthesis Using Inner Product Transforms},
-	year = {2024},
+	volume = {38},
+	year = {2025},
 }
diff --git a/static/research/Ruppik25a.bib b/static/research/Ruppik25a.bib
@@ -1,9 +1,14 @@
-@unpublished{Ruppik25a,
+@inproceedings{Ruppik25a,
 	archiveprefix = {arXiv},
 	author = {Benjamin Matthias Ruppik and Julius von Rohrscheidt and Carel van Niekerk and Michael Heck and Renato Vukovic and Shutong Feng and Hsien-chin Lin and Nurul Lubis and Bastian Rieck and Marcus Zibrowius and Milica Gašić},
 	author+an = {9=highlight},
+	booktitle = {Advances in Neural Information Processing Systems},
 	eprint = {2506.01034},
+	keywords = {CORE-A*},
 	primaryclass = {cs.CL},
+	publisher = {Curran Associates, Inc.},
+	pubstate = {inpress},
 	title = {Less is More: Local Intrinsic Dimensions of Contextual Language Models},
+	volume = {38},
 	year = {2025},
 }
diff --git a/static/research/Saidi25a.bib b/static/research/Saidi25a.bib
@@ -0,0 +1,9 @@
+@unpublished{Saidi25a,
+	archiveprefix = {arXiv},
+	author = {Tristan Luca Saidi and Abigail Hickok and Bastian Rieck and Andrew J. Blumberg},
+	author+an = {3=highlight},
+	eprint = {2509.03703},
+	primaryclass = {cs.LG},
+	title = {EmbedOR: Provable Cluster-Preserving Visualizations with Curvature-Based Stochastic Neighbor Embeddings},
+	year = {2025},
+}
diff --git a/static/research/Thomas22a.bib b/static/research/Thomas22a.bib
@@ -2,7 +2,7 @@ @inproceedings{Thomas22a
 	abstract = {Proton-proton collisions at the large hadron collider result in the creation of unstable particles. The decays of many of these particles produce collimated sprays of particles referred to as jets. To better understand the physics processes occurring in the collisions, one needs to classify the jets, a process known as jet tagging. Given the enormous amount of data generated during such experiments, and the subtleties between different signatures, jet tagging is of vital importance and allows us to discard events which are not of interest --- a critical part of dealing with such high-throughput data. We present a new approach to jet tagging that leverages topological properties of jets to capture their inherent shape. Our method respects underlying physical symmetries, is robust to noise, and exhibits predictive performance on par with more complex, heavily-parametrized approaches.},
 	author = {Dawson Thomas and Sarah Demers and Smita Krishnaswamy and Bastian Rieck},
 	author+an = {3=last; 4=highlight,last},
-	booktitle = {`Machine Learning and the Physical Sciences' Workshop at NeurIPS},
+	booktitle = {``Machine Learning and the Physical Sciences'' Workshop at NeurIPS},
 	title = {Topological Jet Tagging},
 	url = {https://ml4physicalsciences.github.io/2022/files/NeurIPS_ML4PS_2022_176.pdf},
 	year = {2022},
diff --git a/static/research/deBodt25a.bib b/static/research/deBodt25a.bib
@@ -0,0 +1,9 @@
+@unpublished{deBodt25a,
+	archiveprefix = {arXiv},
+	author = {Cyril de Bodt and Alex Diaz-Papkovich and Michael Bleher and Kerstin Bunte and Corinna Coupette and Sebastian Damrich and Enrique Fita Sanmartin and Fred A. Hamprecht and Emőke-Ágnes Horvát and Dhruv Kohli and Smita Krishnaswamy and John A. Lee and Boudewijn P. F. Lelieveldt and Leland McInnes and Ian T. Nabney and Maximilian Noichl and Pavlin G. Poličar and Bastian Rieck and Guy Wolf and Gal Mishne and Dmitry Kobak},
+	author+an = {1=first; 2=first; 18=highlight; 20=last; 21=last},
+	eprint = {2508.15929},
+	primaryclass = {cs.LG},
+	title = {Low-dimensional embeddings of high-dimensional data},
+	year = {2025},
+}
diff --git a/static/research/vonRohrscheidt25a.bib b/static/research/vonRohrscheidt25a.bib
@@ -3,12 +3,16 @@ @inproceedings{vonRohrscheidt25a
 	author = {von Rohrscheidt, Julius and Rieck, Bastian},
 	author+an = {2=highlight},
 	booktitle = {Proceedings of the 42nd International Conference on Machine Learning},
+	editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry},
 	eprint = {2410.02622},
 	keywords = {CORE-A*},
+	pages = {61790--61809},
 	primaryclass = {cs.LG},
 	publisher = {PMLR},
-	pubstate = {inpress},
+	repository = {https://github.com/aidos-lab/Diss-l-ECT},
 	series = {Proceedings of Machine Learning Research},
 	title = {{Diss-l-ECT}: Dissecting Graph Data with Local {E}uler Characteristic Transforms},
+	url = {https://proceedings.mlr.press/v267/von-rohrscheidt25a.html},
+	volume = {267},
 	year = {2025},
 }

Original file line number	Diff line number	Diff line change
`@@ -1,7 +1,7 @@`
`1`	`1`	`@inproceedings{Brueningk20b,`
`2`	`2`	`author = {Sarah C. Brüningk and Felix Hensel and Catherine R. Jutzeler and Bastian Rieck},`
`3`	`3`	`author+an = {1=first; 2=first; 3=last; 4=last,highlight},`
`4`		- booktitle = {`Medical Imaging meets NeurIPS' Workshop at NeurIPS},
	`4`	+ booktitle = {``Medical Imaging meets NeurIPS'' Workshop at NeurIPS},
`5`	`5`	`title = {Scalable Solutions for {MR} Image Classification of {A}lzheimer's Disease},`
`6`	`6`	`year = {2020},`
`7`	`7`	`}`