Gradient descent finds a global minimum in training deep neural networks despite the
objective function being non-convex. The current paper proves gradient descent achieves zero …

Policy gradient (PG) methods are among the most effective methods in challenging reinforcement
learning problems with large state and/or action spaces. However, little is known about …

The Lancet Countdown is an international collaboration that independently monitors the health
consequences of a changing climate. Publishing updated, new, and improved indicators …

The 2022 report of the Lancet Countdown is published as the world confronts profound and
concurrent systemic shocks. Countries and health systems continue to contend with the …

Abdominal aortic aneurysms represent a life-threatening condition characterized by chronic
inflammation, destructive remodeling of the extracellular matrix, and increased local …

We develop a general approach to valid inference after model selection. At the core of our
framework is a result that characterizes the distribution of a post-selection estimator …

The term microbiologically influenced corrosion (MIC) is used to designate corrosion due to
the presence and activities of microorganisms, that is, those organisms that cannot be seen …

This paper examines from the motivational perspective of the Theory of Reasoned Action (TRA)
how individual values and attitudes in a developing country influence purchase intention …

Matrix completion is a basic machine learning problem that has wide applications, especially
in collaborative filtering and recommender systems. Simple non-convex optimization …

We show that gradient descent converges to a local minimizer, almost surely with random
initial-ization. This is proved by applying the Stable Manifold Theorem from dynamical …