Publication

Stochasticity helps to navigate rough landscapes: comparing gradient-descent-based algorithms in the phase retrieval problem

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Stochasticity helps to navigate rough landscapes: comparing gradient-descent-based algorithms in the phase retrieval problem

Chat with Graph Search

The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Updates

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

On the choice of metric in gradient-based theories of brain function

3D nanometrology of transparent objects by phase calibration of a basic bright-field microscope for multiple illumination apertures

Multi-Frequency Tracking via Group-Sparse Optimal Transport

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

Second-order guarantees in centralized, federated and decentralized nonconvex optimization

Adaptive Gradient Descent without Descent

Stochastic Gradient Descent for Spectral Embedding with Implicit Orthogonality Constraint

Learning Non-Parametric Models with Guarantees: A Smooth Lipschitz Interpolation Approach