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stat.ML2025
Learning from one graph: transductive learning guarantees via the geometry of small random worlds
Nils Detering, Luca Galimberti, Anastasis Kratsios +2
Since their introduction by Kipf and Welling in , a primary use of graph convolutional networks is transductive node classification, where missing labels are inferred within…
math.CA2025
Reconstruction of frequency-localized functions from pointwise samples via least squares and deep learning
A. Martina Neuman, Andres Felipe Lerma Pineda, Jason J. Bramburger +1
Recovering frequency-localized functions from pointwise data is a fundamental task in signal processing. We examine this problem from an approximation-theoretic perspective, focusi…
cs.LG2025
Consistency of augmentation graph and network approximability in contrastive learning
Chenghui Li, A. Martina Neuman
Contrastive learning leverages data augmentation to develop feature representation without relying on large labeled datasets. However, despite its empirical success, the theoretica…