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Found 10 papers, 1 papers with code
Variance-reduced Clipping for Non-convex Optimization
This is in clear contrast to the well-established assumption in folklore non-convex optimization, a. k. a.
Tight Analysis of Extra-gradient and Optimistic Gradient Methods For Nonconvex Minimax Problems
Despite the established convergence theory of Optimistic Gradient Descent Ascent (OGDA) and Extragradient (EG) methods for the convex-concave minimax problems, little is known about the theoretical guarantees of these methods in nonconvex settings.
On Convergence of Gradient Descent Ascent: A Tight Local Analysis
In this paper, we aim to bridge this gap by analyzing the \emph{local convergence} of general \emph{nonconvex-nonconcave} minimax problems.
Byzantine-Robust Federated Linear Bandits
In this paper, we study a linear bandit optimization problem in a federated setting where a large collection of distributed agents collaboratively learn a common linear bandit model.
Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective
This work examines the deep disconnect between existing theoretical analyses of gradient-based algorithms and the practice of training deep neural networks.
Complexity Lower Bounds for Nonconvex-Strongly-Concave Min-Max Optimization
We provide a first-order oracle complexity lower bound for finding stationary points of min-max optimization problems where the objective function is smooth, nonconvex in the minimization variable, and strongly concave in the maximization variable.
The Stellar Distribution Function and Local Vertical Potential from Gaia DR2
We develop a novel method to simultaneously determine the vertical potential, force and stellar $z-v_z$ phase space distribution function (DF) in our local patch of the Galaxy.
Astrophysics of Galaxies
Convergence of Adversarial Training in Overparametrized Neural Networks
Neural networks are vulnerable to adversarial examples, i. e. inputs that are imperceptibly perturbed from natural data and yet incorrectly classified by the network.
Gradient Descent Finds Global Minima of Deep Neural Networks
Gradient descent finds a global minimum in training deep neural networks despite the objective function being non-convex.
Randomness in Deconvolutional Networks for Visual Representation
Toward a deeper understanding on the inner work of deep neural networks, we investigate CNN (convolutional neural network) using DCN (deconvolutional network) and randomization technique, and gain new insights for the intrinsic property of this network architecture.
