Linear classifiers and leaky ReLU networks trained by gradient flow on the logistic loss have
an implicit bias towards solutions which satisfy the Karush–Kuhn–Tucker (KKT) conditions …

The phenomenon of benign overfitting, where a predictor perfectly fits noisy training data
while attaining low expected loss, has received much attention in recent years, but still …

We study indiscriminate poisoning for linear learners where an adversary injects a few
crafted examples into the training data with the goal of forcing the induced model to incur …

Although the optimization objectives for learning neural networks are highly non-convex,
gradient-based methods have been wildly successful at learning neural networks in …

We study the fundamental problem of learning a single neuron, ie, a function of the form
$\x\mapsto\sigma (\vec w\cdot\x) $ for monotone activations $\sigma:\R\mapsto\R $, with …

We consider a binary classification problem when the data comes from a mixture of two
rotationally symmetric distributions satisfying concentration and anti-concentration …

We consider a one-hidden-layer leaky ReLU network of arbitrary width trained by stochastic
gradient descent (SGD) following an arbitrary initialization. We prove that SGD produces …

We investigate approximation guarantees provided by logistic regression for the
fundamental problem of agnostic learning of homogeneous halfspaces. Previously, for a …

We study indiscriminate poisoning for linear learners where an adversary injects a few
crafted examples into the training data with the goal of forcing the induced model to incur …

The phenomenon of benign overfitting, where a predictor perfectly fits noisy training data
while attaining near-optimal expected loss, has received much attention in recent years, but …