Gitta Kutyniok

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

This work formalizes causal regions as polyhedral regions with fixed causal sets and derive upper and lower bounds on the maximal number of causal regions in both shallow and multilayer feedforward spiking networks.

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

Multimodal behavioral cloning fails in surprising ways: latent-variable policies can't balance regularization, while action-space generative policies are fundamentally limited by the smoothness of their action mappings.

Sparsity could be the key to unlocking efficient neural networks for learning operators on infinite-dimensional function spaces, sidestepping the curse of dimensionality.