Feb. 12, 2024, 5:43 a.m. | Stefana Anita Gabriel Turinici

cs.LG updates on arXiv.org arxiv.org

We present a self-contained proof of the convergence rate of the Stochastic Gradient Descent (SGD) when the learning rate follows an inverse time decays schedule; we next apply the results to the convergence of a modified form of policy gradient Multi-Armed Bandit (MAB) with $L2$ regularization.

application apply convergence cs.ai cs.ds cs.lg cs.na form gradient math.na next policy rate stat.ml stochastic

Research Scholar (Technical Research)

@ Centre for the Governance of AI | Hybrid; Oxford, UK

HPC Engineer (x/f/m) - DACH

@ Meshcapade GmbH | Remote, Germany

Director of Machine Learning

@ Axelera AI | Hybrid/Remote - Europe (incl. UK)

Senior Data Scientist - Trendyol Milla

@ Trendyol | Istanbul (All)

Data Scientist, Mid

@ Booz Allen Hamilton | USA, CA, San Diego (1615 Murray Canyon Rd)

Systems Development Engineer , Amazon Robotics Business Applications and Solutions Engineering

@ Amazon.com | Boston, Massachusetts, USA