Updated publications

Pseudomanifold · Pseudomanifold · commit 1507305f4c99 · 2025-06-18T19:21:19.000+02:00
diff --git a/content/publications.md b/content/publications.md
diff --git a/static/research/Alain25a.bib b/static/research/Alain25a.bib
@@ -0,0 +1,10 @@
+@unpublished{Alain25a,
+	archiveprefix = {arXiv},
+	author = {Mathieu Alain and So Takao and Xiaowen Dong and Bastian Rieck and Emmanuel Noutahi},
+	author+an = {4=highlight},
+	eprint = {2505.10877},
+	primaryclass = {cs.LG},
+	title = {Graph and Simplicial Complex Prediction Gaussian Process via the Hodgelet Representations},
+	type = {Preprint},
+	year = {2025},
+}
diff --git a/static/research/Andreeva23a.bib b/static/research/Andreeva23a.bib
@@ -5,11 +5,11 @@ @inproceedings{Andreeva23a
 	booktitle = {Proceedings of the 2nd Annual Workshop on Topology, Algebra, and Geometry in Machine Learning~(TAG-ML)},
 	editor = {Doster, Timothy and Emerson, Tegan and Kvinge, Henry and Miolane, Nina and Papillon, Mathilde and Rieck, Bastian and Sanborn, Sophia},
 	eprint = {2305.05611},
-	number = {221},
 	pages = {242--253},
 	primaryclass = {cs.LG},
 	publisher = {PMLR},
 	series = {Proceedings of Machine Learning Research},
 	title = {Metric Space Magnitude and Generalisation in Neural Networks},
+	volume = {221},
 	year = {2023},
 }
diff --git a/static/research/Ballester25a.bib b/static/research/Ballester25a.bib
@@ -1,12 +1,11 @@
 @inproceedings{Ballester25a,
 	archiveprefix = {arXiv},
-	author = {Rubén Ballester and Ernst Röell and Daniel Bin Schmid and Mathieu Alain and Sergio Escalera and Carles Casacuberta and Bastian Rieck},
+	author = {Rubén Ballester and Ernst Röell and Daniel Bīn Schmid and Mathieu Alain and Sergio Escalera and Carles Casacuberta and Bastian Rieck},
 	author+an = {1=first; 2=first; 3=first; 4=first; 7=highlight},
 	booktitle = {International Conference on Learning Representations},
 	eprint = {2410.02392},
 	keywords = {CORE-A*},
 	primaryclass = {cs.LG},
-	pubstate = {inpress},
 	title = {MANTRA: The Manifold Triangulations Assemblage},
 	url = {https://openreview.net/forum?id=X6y5CC44HM},
 	year = {2025},
diff --git a/static/research/Bhaskar25a.bib b/static/research/Bhaskar25a.bib
@@ -0,0 +1,11 @@
+@unpublished{Bhaskar25a,
+	author = {Bhaskar, Dhananjay and Zhang, Yanlei and Moore, Jessica and Gao, Feng and Rieck, Bastian and Wolf, Guy and Khasawneh, Firas and Munch, Elizabeth and Noah, J. Adam and Pushkarskaya, Helen and Pittenger, Christopher and Greco, Valentina and Krishnaswamy, Smita},
+	author+an = {1=first; 2=first; 5=highlight},
+	doi = {10.1101/2023.03.22.533807},
+	elocation-id = {2023.03.22.533807},
+	eprint = {10.1101/2023.03.22.533807v3},
+	eprinttype = {bioRxiv},
+	title = {Neurospectrum: A Geometric and Topological Deep Learning Framework for Uncovering Spatiotemporal Signatures in Neural Activity},
+	type = {Preprint},
+	year = {2025},
+}
diff --git a/static/research/Brueningk21a.bib b/static/research/Brueningk21a.bib
@@ -3,11 +3,11 @@ @inproceedings{Brueningk21a
 	author+an = {1=first; 2=first; 5=last; 6=last,highlight},
 	booktitle = {Proceedings of the 6th Machine Learning for Healthcare Conference},
 	editor = {Jung, Ken and Yeung, Serena and Sendak, Mark and Sjoding, Michael and Ranganath, Rajesh},
-	number = {149},
 	pages = {730--754},
 	publisher = {PMLR},
 	series = {Proceedings of Machine Learning Research},
 	title = {Back to the basics with inclusion of clinical domain knowledge --- A simple, scalable, and effective model of {A}lzheimer's Disease classification},
 	url = {https://proceedings.mlr.press/v149/bruningk21a.html},
+	volume = {149},
 	year = {2021},
 }
diff --git a/static/research/Coupette25a.bib b/static/research/Coupette25a.bib
@@ -1,10 +1,14 @@
-@unpublished{Coupette25a,
+@inproceedings{Coupette25a,
 	archiveprefix = {arXiv},
 	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},
 	eprint = {2502.02379},
+	keywords = {CORE-A*},
 	primaryclass = {cs.LG},
+	publisher = {PMLR},
+	pubstate = {inpress},
+	series = {Proceedings of Machine Learning Research},
 	title = {No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets},
-	type = {Preprint},
 	year = {2025},
 }
diff --git a/static/research/Groha20.bib b/static/research/Groha20.bib
@@ -2,7 +2,7 @@ @inproceedings{Groha20
 	archiveprefix = {arXiv},
 	author = {Groha, Stefan and Weis, Caroline and Gusev, Alexander and Rieck, Bastian},
 	author+an = {1=first; 2=first; 4=highlight},
-	booktitle = {`Learning Meaningful Representations of Life' Workshop at NeurIPS},
+	booktitle = {NeurIPS Workshop on Learning Meaningful Representations of Life~(LMRL)},
 	eprint = {2011.11070},
 	primaryclass = {q-bio.GN},
 	title = {Topological Data Analysis of Copy Number Alterations in Cancer},
diff --git a/static/research/Hacker22a.bib b/static/research/Hacker22a.bib
@@ -5,12 +5,12 @@ @inproceedings{Hacker22a
 	booktitle = {Proceedings of Topological, Algebraic, and Geometric Learning Workshops 2022},
 	editor = {Cloninger, Alexander and Doster, Timothy and Emerson, Tegan and Kaul, Manohar and Ktena, Ira and Kvinge, Henry and Miolane, Nina and Rieck, Bastian and Tymochko, Sarah and Wolf, Guy},
 	eprint = {2206.08252},
-	number = {196},
 	pages = {142--151},
 	primaryclass = {cs.LG},
 	publisher = {PMLR},
 	repository = {https://github.com/aidos-lab/node2vec-surprises},
 	series = {Proceedings of Machine Learning Research},
 	title = {On the Surprising Behaviour of \texttt{node2vec}},
+	volume = {196},
 	year = {2022},
 }
diff --git a/static/research/Hetzel25a.bib b/static/research/Hetzel25a.bib
@@ -7,7 +7,6 @@ @inproceedings{Hetzel25a
 	keywords = {CORE-A*},
 	note = {Accepted as a \emph{spotlight} presentation~(\textbf{top 5\%} of all submissions)},
 	primaryclass = {physics.chem-ph},
-	pubstate = {inpress},
 	title = {{MAGNet}: Motif-Agnostic Generation of Molecules from Scaffolds},
 	url = {https://openreview.net/forum?id=5FXKgOxmb2},
 	year = {2025},
diff --git a/static/research/Hofer20a.bib b/static/research/Hofer20a.bib
@@ -6,13 +6,13 @@ @inproceedings{Hofer20a
 	editor = {Daumé~III, Hal and Aarti Singh},
 	eprint = {1905.10996},
 	keywords = {CORE-A*},
-	number = {119},
 	pages = {4314--4323},
 	primaryclass = {cs.LG},
 	publisher = {PMLR},
 	repository = {https://github.com/c-hofer/graph_filtration_learning},
 	series = {Proceedings of Machine Learning Research},
 	title = {Graph Filtration Learning},
 	url = {https://proceedings.mlr.press/v119/hofer20b.html},
+	volume = {119},
 	year = {2020},
 }
diff --git a/static/research/Horn20a.bib b/static/research/Horn20a.bib
@@ -6,13 +6,13 @@ @inproceedings{Horn20a
 	editor = {Daumé~III, Hal and Aarti Singh},
 	eprint = {1909.12064},
 	keywords = {CORE-A*},
-	number = {119},
 	pages = {4353--4363},
 	primaryclass = {cs.LG},
 	publisher = {PMLR},
 	repository = {https://github.com/BorgwardtLab/Set_Functions_for_Time_Series},
 	series = {Proceedings of Machine Learning Research},
 	title = {Set Functions for Time Series},
 	url = {https://proceedings.mlr.press/v119/horn20a},
+	volume = {119},
 	year = {2020},
 }
diff --git a/static/research/John25a.bib b/static/research/John25a.bib
@@ -0,0 +1,11 @@
+@unpublished{John25a,
+	author = {John, Tibin and Zhou, Yajun and Aljishi, Ayman and Rieck, Bastian and Turk-Browne, Nicholas B. and Damisah, Eyiyemisi C.},
+	author+an = {4=highlight},
+	doi = {10.1101/2025.03.04.641300},
+	eprint = {10.1101/2025.03.04.641300v1},
+	eprinttype = {bioRxiv},
+	publisher = {Cold Spring Harbor Laboratory},
+	title = {Representation of visual sequences in the tuning and topology of neuronal activity in the human hippocampus},
+	type = {Preprint},
+	year = {2025},
+}
diff --git a/static/research/Koke25a.bib b/static/research/Koke25a.bib
@@ -0,0 +1,8 @@
+@inproceedings{Koke25a,
+	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 on Machine Learning Multiscale Processes},
+	title = {On the Successful Incorporation of Scale into Graph Neural Networks},
+	url = {https://openreview.net/forum?id=SRCsyJafgP},
+	year = {2025},
+}
diff --git a/static/research/Koke25b.bib b/static/research/Koke25b.bib
@@ -0,0 +1,8 @@
+@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'},
+	title = {Graph Networks Struggle with Variable Scale},
+	url = {https://openreview.net/forum?id=N5n6SAfnU0},
+	year = {2025},
+}
diff --git a/static/research/Koke25c.bib b/static/research/Koke25c.bib
@@ -0,0 +1,8 @@
+@inproceedings{Koke25c,
+	author = {Christian Koke and Dominik Schnaus and Yuesong Shen and Abhishek Saroha and Marvin Eisenberger and Bastian Rieck and Michael M. Bronstein and Daniel Cremers},
+	author+an = {6=highlight},
+	booktitle = {ICLR Workshop on Learning Meaningful Representations of Life~(LMRL)},
+	title = {On Multi-Scale Graph Representation Learning},
+	url = {https://openreview.net/forum?id=X3OMHwfsxk},
+	year = {2025},
+}
diff --git a/static/research/Limbeck24a.bib b/static/research/Limbeck24a.bib
@@ -10,7 +10,7 @@ @inproceedings{Limbeck24a
 	primaryclass = {cs.LG},
 	publisher = {Curran Associates, Inc.},
 	title = {Metric Space Magnitude for Evaluating the Diversity of Latent Representations},
-	url = {https://proceedings.neurips.cc/paper_files/paper/2024/file/dfc24bd3ec5d74960e104268bbb52849-Paper-Conference.pdf},
+	url = {https://papers.nips.cc/paper_files/paper/2024/hash/dfc24bd3ec5d74960e104268bbb52849-Abstract-Conference.html},
 	volume = {37},
 	year = {2024},
 }
diff --git a/static/research/Limbeck25a.bib b/static/research/Limbeck25a.bib
@@ -0,0 +1,10 @@
+@unpublished{Limbeck25a,
+	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},
+	eprint = {2506.11700},
+	primaryclass = {cs.LG},
+	title = {Geometry-Aware Edge Pooling for Graph Neural Networks},
+	type = {Preprint},
+	year = {2025},
+}
diff --git a/static/research/Liu22a.bib b/static/research/Liu22a.bib
@@ -6,12 +6,12 @@ @inproceedings{Liu22a
 	editor = {Rieck, Bastian and Pascanu, Razvan},
 	eprint = {2206.07729},
 	note = {Accepted as an \emph{oral} presentation at LoG~(\textbf{top 5\%} of all submissions)},
-	number = {198},
 	pages = {6:1--6:25},
 	primaryclass = {cs.LG},
 	publisher = {PMLR},
 	series = {Proceedings of Machine Learning Research},
 	title = {Taxonomy of Benchmarks in Graph Representation Learning},
 	url = {https://proceedings.mlr.press/v198/liu22a.html},
+	volume = {198},
 	year = {2022},
 }
diff --git a/static/research/Moor19a.bib b/static/research/Moor19a.bib
@@ -1,19 +1,17 @@
 @inproceedings{Moor19a,
-	abstract = {Sepsis is a life-threatening host response to infection that is associated with high mortality, morbidity, and health costs. Its management is highly time-sensitive because each hour of delayed treatment increases mortality due to irreversible organ damage. Meanwhile, despite decades of clinical research, robust biomarkers for sepsis are missing. Therefore, detecting sepsis early by utilizing the affluence of high-resolution intensive care records has become a challenging machine learning problem. Recent advances in deep learning and data mining promise to deliver a powerful set of tools to efficiently address this task. This empirical study proposes two novel approaches for the early detection of sepsis: a deep learning model and a lazy learner that is based on time series distances. Our deep learning model employs a temporal convolutional network that is embedded in a multi-task Gaussian Process adapter framework, making it directly applicable to irregularly-spaced time series data. In contrast, our lazy learner is an ensemble approach that employs dynamic time warping. We frame the timely detection of sepsis as a supervised time series classification task. Consequently, we derive the most recent sepsis definition in an hourly resolution to provide the first fully accessible early sepsis detection environment. Seven hours before sepsis onset, our methods improve area under the precision–recall curve from 0.25 to 0.35 and 0.40, respectively, over the state of the art. This demonstrates that they are well-suited for detecting sepsis in the crucial earlier stages when management is most effective.},
 	archiveprefix = {arXiv},
 	author = {Moor, Michael and Horn, Max and Rieck, Bastian and Roqueiro, Damian and Borgwardt, Karsten},
 	author+an = {3=highlight},
 	booktitle = {Proceedings of the 4th Machine Learning for Healthcare Conference},
 	editor = {Doshi-Velez, Finale and Fackler, Jim and Jung, Ken and Kale, David and Ranganath, Rajesh and Wallace, Byron and Wiens, Jenna},
 	eprint = {1902.01659},
-	month = {August},
-	number = {106},
 	pages = {2--26},
 	primaryclass = {cs.LG},
 	publisher = {PMLR},
 	repository = {https://github.com/BorgwardtLab/mgp-tcn},
 	series = {Proceedings of Machine Learning Research},
 	title = {Early Recognition of Sepsis with {G}aussian Process Temporal Convolutional Networks and Dynamic Time Warping},
 	url = {http://proceedings.mlr.press/v106/moor19a.html},
+	volume = {106},
 	year = {2019},
 }
diff --git a/static/research/Moor20a.bib b/static/research/Moor20a.bib
@@ -7,13 +7,13 @@ @inproceedings{Moor20a
 	editor = {Daumé~III, Hal and Aarti Singh},
 	eprint = {1906.00722},
 	keywords = {CORE-A*},
-	number = {119},
 	pages = {7045--7054},
 	primaryclass = {cs.LG},
 	publisher = {PMLR},
 	repository = {https://github.com/BorgwardtLab/topological-autoencoders},
 	series = {Proceedings of Machine Learning Research},
 	title = {Topological Autoencoders},
 	url = {https://proceedings.mlr.press/v119/moor20a.html},
+	volume = {119},
 	year = {2020},
 }
diff --git a/static/research/Papamarkou24a.bib b/static/research/Papamarkou24a.bib
@@ -6,12 +6,12 @@ @inproceedings{Papamarkou24a
 	editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix},
 	eprint = {2402.08871},
 	keywords = {CORE-A*},
-	number = {235},
 	pages = {39529--39555},
 	primaryclass = {cs.LG},
 	publisher = {PMLR},
 	series = {Proceedings of Machine Learning Research},
 	title = {Position: Topological Deep Learning is the New Frontier for Relational Learning},
 	url = {https://proceedings.mlr.press/v235/papamarkou24a.html},
+	volume = {235},
 	year = {2024},
 }
diff --git a/static/research/Rieck19b.bib b/static/research/Rieck19b.bib
@@ -4,12 +4,12 @@ @inproceedings{Rieck19b
 	booktitle = {Proceedings of the 36th International Conference on Machine Learning},
 	editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
 	keywords = {CORE-A*},
-	number = {97},
 	pages = {5448--5458},
 	publisher = {PMLR},
 	repository = {https://github.com/BorgwardtLab/P-WL},
 	series = {Proceedings of Machine Learning Research},
 	title = {A Persistent {W}eisfeiler--{L}ehman Procedure for Graph Classification},
 	url = {https://proceedings.mlr.press/v97/rieck19a.html},
+	volume = {97},
 	year = {2019},
 }
diff --git a/static/research/Ruppik25a.bib b/static/research/Ruppik25a.bib
@@ -0,0 +1,10 @@
+@unpublished{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},
+	eprint = {2506.01034},
+	primaryclass = {cs.CL},
+	title = {Less is More: Local Intrinsic Dimensions of Contextual Language Models},
+	type = {Preprint},
+	year = {2025},
+}
diff --git a/static/research/Southern23a.bib b/static/research/Southern23a.bib
@@ -10,6 +10,7 @@ @inproceedings{Southern23a
 	primaryclass = {cs.LG},
 	publisher = {Curran Associates, Inc.},
 	title = {Curvature Filtrations for Graph Generative Model Evaluation},
+	url = {https://papers.nips.cc/paper_files/paper/2023/hash/c710d6b4507e70c6332bee871b8d1ca5-Abstract-Conference.html},
 	volume = {36},
 	year = {2023},
 }
diff --git a/static/research/Wayland24a.bib b/static/research/Wayland24a.bib
@@ -6,12 +6,12 @@ @inproceedings{Wayland24a
 	editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix},
 	eprint = {2402.01514},
 	keywords = {CORE-A*},
-	number = {235},
 	pages = {52372--52402},
 	primaryclass = {cs.LG},
 	repository = {https://github.com/aidos-lab/Presto},
 	series = {Proceedings of Machine Learning Research},
 	title = {Mapping the Multiverse of Latent Representations},
 	url = {https://proceedings.mlr.press/v235/wayland24a.html},
+	volume = {235},
 	year = {2024},
 }
diff --git a/static/research/Wayland25a.bib b/static/research/Wayland25a.bib
@@ -0,0 +1,15 @@
+@inproceedings{Wayland25a,
+	address = {Cham, Switzerland},
+	archiveprefix = {arXiv},
+	author = {Jeremy Wayland and Russel J. Funk and Bastian Rieck},
+	author+an = {3=highlight},
+	booktitle = {Pediatric and Lifespan Data Science},
+	editor = {Ehwerhemuepha, Louis and Hoffman, Mark and Kalawi, Adam and Sanger, Terence},
+	eprint = {2408.16022},
+	isbn = {978-3-031-88346-0},
+	pages = {1--16},
+	primaryclass = {cs.SI},
+	publisher = {Springer},
+	title = {Characterizing Physician Referral Networks with Ricci Curvature},
+	year = {2025},
+}
diff --git a/static/research/Zhang25a.bib b/static/research/Zhang25a.bib
@@ -3,9 +3,10 @@ @inproceedings{Zhang25a
 	author = {Yanlei Zhang and Lydia Mezrag and Xingzhi Sun and Charles Xu and Kincaid Macdonald and Dhananjay Bhaskar and Smita Krishnaswamy and Guy Wolf and Bastian Rieck},
 	author+an = {7=last; 8=last; 9=last,highlight},
 	booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing~(ICASSP)},
+	doi = {10.1109/ICASSP49660.2025.10888433},
 	eprint = {2502.03750},
+	pages = {1--5},
 	primaryclass = {cs.LG},
-	pubstate = {inpress},
 	title = {Principal Curvatures Estimation with Applications to Single Cell Data},
 	year = {2025},
 }
diff --git a/static/research/vonRohrscheidt23a.bib b/static/research/vonRohrscheidt23a.bib
@@ -7,12 +7,13 @@ @inproceedings{vonRohrscheidt23a
 	editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan},
 	eprint = {2210.00069},
 	keywords = {CORE-A*},
-	number = {202},
 	pages = {35175--35197},
 	primaryclass = {cs.LG},
 	publisher = {PMLR},
 	repository = {https://github.com/aidos-lab/TARDIS},
 	series = {Proceedings of Machine Learning Research},
 	title = {Topological Singularity Detection at Multiple Scales},
+	url = {https://proceedings.mlr.press/v202/von-rohrscheidt23a.html},
+	volume = {202},
 	year = {2023},
 }
diff --git a/static/research/vonRohrscheidt24a.bib b/static/research/vonRohrscheidt24a.bib
@@ -1,10 +1,9 @@
-@unpublished{vonRohrscheidt24a,
-	archiveprefix = {arXiv},
-	author = {von Rohrscheidt, Julius and Rieck, Bastian},
-	author+an = {2=highlight},
-	eprint = {2410.02622},
-	primaryclass = {cs.LG},
-	title = {Diss-l-ECT: Dissecting Graph Data with local Euler Characteristic Transforms},
-	type = {Preprint},
+@article{vonRohrscheidt24a,
+	author = {von Rohrscheidt, Julius and Rieck, Bastian and Schmon, Sebastian M.},
+	author+an = {2=last,highlight; 3=last},
+	issn = {2835-8856},
+	journal = {Transactions on Machine Learning Research},
+	title = {Bayesian Computation Meets Topology},
+	url = {https://openreview.net/forum?id=0h1DtRK6dA},
 	year = {2024},
 }
diff --git a/static/research/vonRohrscheidt25a.bib b/static/research/vonRohrscheidt25a.bib
@@ -0,0 +1,14 @@
+@inproceedings{vonRohrscheidt25a,
+	archiveprefix = {arXiv},
+	author = {von Rohrscheidt, Julius and Rieck, Bastian},
+	author+an = {2=highlight},
+	booktitle = {Proceedings of the 42nd International Conference on Machine Learning},
+	eprint = {2410.02622},
+	keywords = {CORE-A*},
+	primaryclass = {cs.LG},
+	publisher = {PMLR},
+	pubstate = {inpress},
+	series = {Proceedings of Machine Learning Research},
+	title = {{Diss-l-ECT}: Dissecting Graph Data with Local {E}uler Characteristic Transforms},
+	year = {2025},
+}
