Abstract
The rapid proliferation of decentralized network architectures, such as mesh networks, has sparked growing interest in efficiently solving large-scale statistical learning tasks, especially when data is inherently distributed and lacks centralized oversight. Performing accurate statistical inference in such environments is a nontrivial task, particularly under stringent constraints on computational power, time, and inter-node communication. While statistical-computational trade-offs have been thoroughly characterized for high-dimensional inference in centralized settings, our understanding of these trade-offs within decentralized network environments remains comparatively limited. Indeed, methodologies that demonstrate robustness and accuracy in traditional low-dimensional contexts frequently underperform in high-dimensional regimes, and theoretical convergence results often fail to align with observed empirical behaviors. This divergence is largely attributable to the historical emphasis on optimization-centric design and analysis of decentralized algorithms, often overlooking critical statistical nuances. In this talk, we will introduce new algorithmic frameworks and analytical tools specifically tailored for decentralized high-dimensional inferential tasks. By integrating statistical insights into the design and analysis of decentralized optimization algorithms, we shed new light on existing gaps and misconceptions prevalent in the literature, thereby redefining our understanding of distributed inference methodologies.
Biographical Information
Gesualdo Scutari is the Pedro and Granadillo Professor in the School of Industrial Engineering and Electrical and Computer Engineering at Purdue University, West Lafayette, IN, USA. His research interests focus on continuous optimization--particularly distributed and stochastic methods--equilibrium programming, and their applications in signal processing and statistical learning. Among others, he was a recipient of the 2013 NSF CAREER Award, the 2015 IEEE Signal Processing Society Young Author Best Paper Award, and the 2020 IEEE Signal Processing Society Best Paper Award. He served as an IEEE Signal Processing Distinguish Lecturer (2023-2024) and has been on the editorial broad of several IEEE journals. He is currently an Associate Editor for the SIAM Journal on Optimization. He is a Fellow of IEEE.