167 citations · 167 across the 2 of their papers we have counts for
4 papers
Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales
Jonathan Kelner, Annie Marsden, Vatsal Sharan +3
We provide new gradient-based methods for efficiently solving a broad class of ill-conditioned optimization problems. We consider the problem of minimizing a function $f : \mathbb{…
A Field Guide to Federated Optimization
Jianyu Wang, Zachary Charles, Zheng Xu +50
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy prote…
Federated Composite Optimization
Honglin Yuan, Manzil Zaheer, Sashank Reddi
Federated Learning (FL) is a distributed learning paradigm that scales on-device learning collaboratively and privately. Standard FL algorithms such as FedAvg are primarily geared…
SHREC 2020 track: 6D Object Pose Estimation
Honglin Yuan, Remco C. Veltkamp, Georgios Albanis +3
6D pose estimation is crucial for augmented reality, virtual reality, robotic manipulation and visual navigation. However, the problem is challenging due to the variety of objects…