scalable-algorithms

This paper presents two randomized SVD approximations for spectral co-clustering of word-document matrices, showing that the random-projection method is more reliable across settings, while the sampling-based method is better for denser matrices.

CLS introduces a scalable framework for simultaneous causal network inference and forecasting in dynamical systems, achieving high-fidelity reconstruction and accurate predictions in benchmarks.