Pascal Esser

Spectral learning unlocks $\mathcal{O}(d\log d)$ covariance estimation for dense Gaussian dependencies, outperforming prior methods in forecasting and prediction tasks.

Structural and magnitude pruning can retain significant functional redundancy, revealing that model compression strategies may overlook critical parameter directions.