We present new algorithms for optimizing non-smooth, non-convex stochastic objectives
based on a novel analysis technique. This improves the current best-known complexity for …

During the recent years the interest of optimization and machine learning communities in
high-probability convergence of stochastic optimization methods has been growing. One of …

This work considers the problem of finding a first-order stationary point of a non-convex
function with potentially unbounded smoothness constant using a stochastic gradient oracle …

Due to the non-smoothness of optimization problems in Machine Learning, generalized
smoothness assumptions have been gaining a lot of attention in recent years. One of the …

Both centralized and decentralized approaches have shown excellent performance and
great application value in federated learning (FL). However, current studies do not provide …

We develop new sub-optimality bounds for gradient descent (GD) that depend on the
conditioning of the objective along the path of optimization, rather than on global, worst-case …

We consider the gradient method with variable step size for minimizing functions that are
definable in o-minimal structures on the real field and differentiable with locally Lipschitz …

The presence of non-convexity in smooth optimization problems arising from deep learning
have sparked new smoothness conditions in the literature and corresponding convergence …

This letter proposes a novel force-based task-orientation controller for interaction tasks with
environmental orientation uncertainties. The main aim of the controller is to align the robot …

Methods with adaptive stepsizes, such as AdaGrad and Adam, are essential for training
modern Deep Learning models, especially Large Language Models. Typically, the noise in …