3 accepted papers
This paper introduces a new method for minimizing matrix-smooth non-convex objectives through the use of novel Compressed Gradient Descent (CGD) algorithms enhanced with a matrix-valued stepsize. The proposed algorithms are theoretically analyzed first in the single-node and subsequently in the dis…
This letter presents a robust multi-sensor fusion framework, Leg-KILO (Kinematic-Inertial-Lidar Odometry). When lidar-based SLAM is applied to legged robots, high-dynamic motion (e.g., trot gait) introduces frequent foot impacts, leading to IMU degradation and lidar motion distortion. Direct use of
We propose and study several server-extrapolation strategies for enhancing the theoretical and empirical convergence properties of the popular federated learning optimizer FedProx [Li et al., 2020]. While it has long been known that some form of extrapolation can help in the practice of FL, only a h…