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cs.LG2026
Practical Non-Stationary Graph Gaussian Processes
K. S. Sesh Kumar, Viacheslav Borovitskiy
Gaussian processes on graphs provide a principled framework for prediction and uncertainty estimation, but modelling heterogeneous networks often requires balancing flexibility aga…
cs.LG2026
Geometry-Constrained Kolmogorov-Arnold Networks: Learning Edge Geometry via Banach Duality
K S Sesh Kumar
Kolmogorov-Arnold Networks (KANs) learn univariate functions on edges, making the choice of edge response central to their design. Existing variants use basis expansions such as sp…