IEEE SPS Distinguished Lecturer Program Twin Cities SP/COM Chapter Seminar 11/14/2023
Lecture by Professor Gesualdo Scutari, Professor with the School of Industrial Engineering and Electrical and Computer Engineering at Purdue University, West Lafayette, IN, USA, an IEEE SPS Distinguished Lecturer, on the topic of "Statistical Inference over Networks: Decentralized Optimization Meets High-dimensional Statistics" to be held at the Keller Hall 3-180, University of Minnesota.
Title: Statistical Inference over Networks: Decentralized Optimization Meets High-dimensional Statistics
There is growing interest in solving large-scale statistical machine learning problems over decentralized networks, where data are distributed across the nodes of the network and no centralized coordination is present (we termed these systems as “mesh” networks). Inference from massive datasets poses a fundamental challenge at the nexus of the computational and statistical sciences: ensuring the quality of statistical inference when computational resources, like time and communication, are constrained. While statistical-computation tradeoffs have been largely explored in the centralized setting, our understanding over mesh networks is limited: (i) distributed schemes, designed and performing well in the classical low-dimensional regime, can break down in the high-dimensional case; and (ii) existing convergence studies may fail to predict algorithmic behaviors, with some findings directly contradicted by empirical tests. This is mainly due to the fact that the majority of distributed algorithms have been designed and studied only from the optimization perspective, lacking the statistical dimension. This talk will discuss some vignettes from high-dimensional statistical inference suggesting new analyses (and designs) aiming at bringing statistical thinking in distributed optimization.
Date and Time
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- Date: 14 Nov 2023
- Time: 11:00 AM to 12:30 PM
- All times are (UTC-06:00) Central Time (US & Canada)
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- 200 Union St SE
- Minneapolis, Minnesota
- United States 55455
- Building: Kenneth H. Keller Hall
- Room Number: 3-180
- Contact Event Host
- Co-sponsored by College of Science and Engineering, University of Minnesota
- Starts 05 October 2023 11:00 AM
- Ends 14 November 2023 09:00 PM
- All times are (UTC-06:00) Central Time (US & Canada)
- No Admission Charge
Speakers
Prof. Gesualdo Scutari of Purdue University
Statistical Inference over Networks: Decentralized Optimization Meets High-dimensional Statistics
There is growing interest in solving large-scale statistical machine learning problems over decentralized networks, where data are distributed across the nodes of the network and no centralized coordination is present (we termed these systems as “mesh” networks). Inference from massive datasets poses a fundamental challenge at the nexus of the computational and statistical sciences: ensuring the quality of statistical inference when computational resources, like time and communication, are constrained. While statistical-computation tradeoffs have been largely explored in the centralized setting, our understanding over mesh networks is limited: (i) distributed schemes, designed and performing well in the classical low-dimensional regime, can break down in the high-dimensional case; and (ii) existing convergence studies may fail to predict algorithmic behaviors, with some findings directly contradicted by empirical tests. This is mainly due to the fact that the majority of distributed algorithms have been designed and studied only from the optimization perspective, lacking the statistical dimension. This talk will discuss some vignettes from high-dimensional statistical inference suggesting new analyses (and designs) aiming at bringing statistical thinking in distributed optimization.
Biography:
Gesualdo Scutari is a Professor with the School of Industrial Engineering and Electrical and Computer Engineering (by courtesy) at Purdue University, West Lafayette, IN, USA, and he is a Purdue Faculty Scholar. His research interests include continuous optimization, equilibrium programming, and their applications to 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 serves as an IEEE Signal Processing Distinguish Lecturer (2023-2024). He served on the editorial broad of several IEEE journals and he is currently an Associate Editor of SIAM Journal on Optimization. He is IEEE Fellow.
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Agenda
11:00 – 11:05 p.m. Welcome Remarks by Dr. Wenyu Jin
11:05 – 12:00 p.m. Statistical Inference over Networks: Decentralized Optimization Meets High-dimensional Statistics by Prof. Gesualdo Scutari , chair by Prof. Mingyi Hong
12:00 – 12:30 p.m. Q&A and Networking, Prof. Mingyi Hong to Moderate