563 citations · 891 across the 25 of their papers we have counts for
5 papers · 1 filter
The perils of being unhinged: On the accuracy of classifiers minimizing a noise-robust convex loss
Philip M. Long, Rocco A. Servedio
Van Rooyen et al. introduced a notion of convex loss functions being robust to random classification noise, and established that the "unhinged" loss function is robust in this sens…
Foolish Crowds Support Benign Overfitting
Niladri S. Chatterji, Philip M. Long
We prove a lower bound on the excess risk of sparse interpolating procedures for linear regression with Gaussian data in the overparameterized regime. We apply this result to obtai…
The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks
Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett
The recent success of neural network models has shone light on a rather surprising statistical phenomenon: statistical models that perfectly fit noisy data can generalize well to u…
Properties of the After Kernel
Philip M. Long
The Neural Tangent Kernel (NTK) is the wide-network limit of a kernel defined using neural networks at initialization, whose embedding is the gradient of the output of the network…
When does gradient descent with logistic loss interpolate using deep networks with smoothed ReLU activations?
Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett
We establish conditions under which gradient descent applied to fixed-width deep networks drives the logistic loss to zero, and prove bounds on the rate of convergence. Our analysi…