96 citations · 298 across the 54 of their papers we have counts for
6 papers · 1 filter
Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks
Pascal Mattia Esser, Leena Chennuru Vankadara, Debarghya Ghoshdastidar
In recent years, several results in the supervised learning setting suggested that classical statistical learning-theoretic measures, such as VC dimension, do not adequately explai…
Graphon based Clustering and Testing of Networks: Algorithms and Theory
Mahalakshmi Sabanayagam, Leena Chennuru Vankadara, Debarghya Ghoshdastidar
Network-valued data are encountered in a wide range of applications and pose challenges in learning due to their complex structure and absence of vertex correspondence. Typical exa…
Causal Forecasting:Generalization Bounds for Autoregressive Models
Leena Chennuru Vankadara, Philipp Michael Faller, Michaela Hardt +3
Despite the increasing relevance of forecasting methods, causal implications of these algorithms remain largely unexplored. This is concerning considering that, even under simplify…
Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models
Leena Chennuru Vankadara, Sebastian Bordt, Ulrike von Luxburg +1
Despite the ubiquity of kernel-based clustering, surprisingly few statistical guarantees exist beyond settings that consider strong structural assumptions on the data generation pr…
New Insights into Graph Convolutional Networks using Neural Tangent Kernels
Mahalakshmi Sabanayagam, Pascal Esser, Debarghya Ghoshdastidar
Graph Convolutional Networks (GCNs) have emerged as powerful tools for learning on network structured data. Although empirically successful, GCNs exhibit certain behaviour that has…
HOLISMOKES -- VII. Time-delay measurement of strongly lensed Type Ia supernovae using machine learning
S. Huber, S. H. Suyu, D. Ghoshdastidar +7
The Hubble constant () is one of the fundamental parameters in cosmology, but there is a heated debate around the 4 tension between the local Cepheid distance ladder and…