Transfer learning is a critical part of real-world machine learning deployments and has been
extensively studied in experimental works with overparameterized neural networks …

Noisy recovery from random linear observations: Sharp minimax rates under elliptical
constraints Page 1 The Annals of Statistics 2024, Vol. 52, No. 6, 2816–2850 https://doi.org/10.1214/24-AOS2446 …

Random ordinary differential equations (RODEs) describe numerous physical and biological
systems whose dynamics contain some level of inherent randomness. These sources of …

Training deep learning models is an incredibly effective method for finding function
approximators. However, understanding the behavior of these trained models from a first …

This work starts with the intention of using mathematics to understand the intriguing
vulnerability observed by Szegedy et al.(2014) within artificial neural networks. Along the …

This paper proposes a generalized exact path kernel gEPK which naturally decomposes
model predictions into localized input gradients or parameter gradients. Many cutting edge …