Distributed Stochastic Non-Convex Optimization - Optimal Regimes and Tradeoffs

Explore distributed stochastic non-convex optimization, focusing on optimal regimes and tradeoffs in this hour-long webinar presented by Usman A. Khan from Tufts University. Gain insights into this complex topic as part of the Data sciEnce on GrAphS (DEGAS) Webinar Series, organized in collaboration with the IEEE Signal Processing Society Data Science Initiative. Delve into the intricacies of non-convex optimization techniques and their applications in distributed systems. Learn about the challenges and opportunities in this field, and understand how different regimes and tradeoffs impact the optimization process. Enhance your knowledge of advanced optimization methods and their relevance in modern data science and signal processing applications.

Distributed stochastic non-convex optimization: Optimal regimes and tradeoffs