2024
Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise
NeurIPS 2024poster
We study the problem of learning a single neuron with respect to the $L_2^2$-loss in the presence of adversarial distribution shifts, where the labels can be arbitrary, and the goal is to find a "best-fit" function. More precisely, given training samples from a reference distribution $p_0$, the goa…