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Volume 321: Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), 1-2 December 2025, San Diego, California, USA
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Editors: Guillermo Bernardez Gil, Mitchell Black, Alexander Cloninger, Timothy Doster, Tegan Emerson, Ińes Garcı́a-Rodondo, Chester Holtz, Mit Kotak, Henry Kvinge, Gal Mishne, Mathilde Papillon, Alison Pouplin, Katie Rainey, Bastian Rieck, Lev Telyatnikov, Eric Yeats, Qingsong Wang, Yusu Wang, Jeremy Wayland
1st Conference on Topology, Algebra, and Geometry in Data Science (TAG-DS 2025): Preface
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:1-3
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Topological Deep Learning Challenge 2025: Expanding the Data Landscape
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:4-14
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LR-RaNN: Lipschitz Regularized Randomized Neural Networks for System Identification
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:15-29
Peeling metric spaces of strict negative type
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:30-44
Bilevel Optimization for Hyperparameter Learning in Supporting Vector Machines
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:45-55
Topological Preservation in Temporal Link Prediction
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:56-78
Neural Local Wasserstein Regression
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:79-89
Learning Polynomial Activation Functions for Deep Neural Networks
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:90-99
Kernel Mean Embeddings of \texttt[CLS] Tokens in ViTs
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:100-113
Looping back: Circular nodes revisited with novel applications in the radio frequency domain
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:114-125
Advancing Local Clustering on Graphs via Compressive Sensing: Semi-supervised and Unsupervised Methods
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:126-146
Quasi Zigzag Persistence: A Topological Framework for Analyzing Time-Varying Data
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:147-165
Scratching the Surface: Reflections of Training Data Properties in Early CNN Filters
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:166-175
Looping back: Circular nodes revisited with novel applications in the radio frequency domain
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:176-190
HAGGLE: Get a better deal using a Hierarchical Autoencoder for Graph Generation and Latent-space Expressivity
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:191-202
Multi-View Graph Learning with Graph-Tuple
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:203-216
Symmetry-Aware Graph Metanetwork Autoencoders: Model Merging through Parameter Canonicalization
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:217-235
DYMAG: Rethinking Message Passing Using Dynamical-systems-based Waveforms
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:236-268
LINSCAN - A Linearity Based Clustering Algorithm
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:269-286
Topological Signatures of ReLU Neural Network Activation Patterns
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:287-301
Can Neural Networks Learn Small Algebraic Worlds? An Investigation Into the Group-theoretic Structures Learned By Narrow Models Trained To Predict Group Operations
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:302-312
A Model of Flocking Using Sheaves
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:313-337
Robust Hyperspectral Anomaly Detection via Bootstrap Sampling-based Subspace Modeling in the Signed Cumulative Distribution Transform Domain
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:338-348
Precision Matrix based Feature Learning Mechanism for Subspace Clustering Task
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:349-361
Self-Organizing Maps for the Reconstruction of Images in Pixel Permuted Image Stacks
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:362-374
On Predicting Material Fracture from Persistence Homology: Or, Which Topological Features Are Informative Covariates?
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:375-388
Interpreting deep neural networks trained on elementary $p$ groups reveals algorithmic structure
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:389-402
Comparative Analysis in Pre-image Algorithms of Kernel PCA
; Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science(TAG-DS 2025), PMLR 321:403-413
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