Pratik Jawanpuria

Riemannian geometry can revolutionize low-rank optimal transport, achieving faster convergence and better performance without the need for regularization.

Online federated learning can actually benefit from parallelization, but only if temporal data variation is mild relative to stochastic gradient variance—a condition often overlooked in existing pessimistic analyses.