2 citations · 4 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
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…
The Graph Cut Kernel for Ranked Data
Michelangelo Conserva, Marc Peter Deisenroth, K S Sesh Kumar
Many algorithms for ranked data become computationally intractable as the number of objects grows due to the complex geometric structure induced by rankings. An additional challeng…
Maximizing submodular functions using probabilistic graphical models
K. S. Sesh Kumar, Francis Bach
We consider the problem of maximizing submodular functions; while this problem is known to be NP-hard, several numerically efficient local search techniques with approximation guar…