7 papers
Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization
Aleksandar Armacki, Dragana BajoviÄ, DuÅ¡an JakovetiÄ +2
The study of tail behaviour of SGD-induced processes has been attracting a lot of interest, due to offering strong guarantees with respect to individual runs of an algorithm. While…
Decentralized Nonconvex Optimization under Heavy-Tailed Noise: Normalization and Optimal Convergence
Shuhua Yu, Dusan Jakovetic, Soummya Kar
Heavy-tailed noise in nonconvex stochastic optimization has garnered increasing research interest, as empirical studies, including those on training attention models, suggest it is…
Distributed Gradient Clustering: Convergence and the Effect of Initialization
Aleksandar Armacki, Himkant Sharma, Dragana BajoviÄ +3
We study the effects of center initialization on the performance of a family of distributed gradient-based clustering algorithms introduced in [1], that work over connected network…
Sharp High-Probability Rates for Nonlinear SGD under Heavy-Tailed Noise via Symmetrization
Aleksandar Armacki, Dragana Bajovic, Dusan Jakovetic +1
We study convergence in high-probability of SGD-type methods in non-convex optimization and the presence of heavy-tailed noise. To combat the heavy-tailed noise, a general black-bo…
Safe Driving in Occluded Environments
Zhuoyuan Wang, Tongyao Jia, Pharuj Rajborirug +5
Ensuring safe autonomous driving in the presence of occlusions poses a significant challenge in its policy design. While existing model-driven control techniques based on set invar…
Distributed Truncated Predictive Control for Networked Systems under Uncertainty: Stability and Near-Optimality Guarantee
Eric Xu, Soummya Kar, Guannan Qu
We study the problem of distributed online control of networked systems with time-varying cost functions and disturbances, where each node only has local information of the states…