Training deep networks on large-scale datasets is computationally challenging. In this work,
we explore the problem of “how to accelerate adaptive gradient algorithms in a general …

Federated learning (FL) is a distributed machine learning framework where the global model
of a central server is trained via multiple collaborative steps by participating clients without …

Federated learning (FL) is a subfield of machine learning where multiple clients try to
collaboratively learn a model over a network under communication constraints. We consider …

Training deep networks on large-scale datasets is computationally challenging. This work
explores the problem of “how to accelerate adaptive gradient algorithms in a general …

Estimating sequences introduced by Nesterov is an efficient trick to accelerate gradient
descent (GD). The stochastic version of estimating sequences is also successfully used to …

In the context of finite sums minimization, variance reduction techniques are widely used to
improve the performance of state-of-the-art stochastic gradient methods. Their practical …

Meta-Reinforcement Learning (MRL) is a promising framework for training agents that can
quickly adapt to new environments and tasks. In this work, we study the MRL problem under …

Solving systems of linear equations is a fundamental problem, but it can be computationally
intensive for classical algorithms in high dimensions. Existing quantum algorithms can …

The impact that data science has on everyday life is incommensurable and continuously
expanding. Imaging, language translation, self-driving cars, ChatGPT are some examples …

In this work, we investigate two aspects of large-scale optimization for convex functions
defined on an infinite-dimensional separable Hilbert space: parallelized methods and …