3 papers
cs.LG2026
Convex Basins in Single-Index Model Loss Landscapes: Applications to Robust Recovery under Strong Adversarial Corruption
Santanu Das, Sagnik Chatterjee, Jatin Batra
We study the problem of robustly learning Gaussian Single Index Models (SIMs) in the presence of heavy-tailed noise and a constant fraction of adversarially corrupted covariates an…
cs.LG2026
Tractable Gaussian Phase Retrieval with Heavy Tails and Adversarial Corruption with Near-Linear Sample Complexity
Santanu Das, Jatin Batra
Phase retrieval is the classical problem of recovering a signal from its noisy phaseless measurements (where d…
cs.LG2024
A direct proof of a unified law of robustness for Bregman divergence losses
Santanu Das, Jatin Batra, Piyush Srivastava
In contemporary deep learning practice, models are often trained to near zero loss i.e. to nearly interpolate the training data. However, the number of parameters in the model is u…