ASA Connect

Dear Colleagues, 

This is a reminder that SLDS February webinar is on next Wednesday.   Prof. Adel Javanmard from University of Southern California will discuss about aggregated learning, and introduce novel loss construction and bagging schemes that enhance model accuracy while maintaining privacy.  Hope to see you there!  

Title:                        Learning from aggregated responses: Improving model utility under privacy constraints.

Speakers:               Prof. Adel Javanmard, Department of Data Sciences and Operations, Marshall School of Business, University of Southern California

Date and Time:      February 26, 2025, 1:00 to 2:30 pm Eastern Time

Registration Link:  ASA SLDS Webinar Registration Link [eventbrite.com] 

Abstract:                In many real-world scenarios, training data is aggregated before being shared with the learner, to protect users' privacy. This talk explores recent advancements in aggregate learning, where datasets are grouped into bags of samples with only summary responses available for each bag. I will discuss novel loss construction and bagging schemes that enhance model accuracy while maintaining privacy. Key topics include using priors to inform bag construction and an iterative boosting algorithm that refines priors through sample splitting. Additionally, I will discuss learning rules in this framework that achieve PAC learning guarantees for classification. While Empirical Proportional Risk Minimization (EPRM) achieves fast rates under realizability, it can falter in agnostic settings. To address this, I will introduce a debiased proportional square loss that achieves "optimistic rates" in both realizable and agnostic scenarios.

Presenter:              Adel Javanmard is a Professor in the department of Data Sciences and Operation, at USC Marshall School of Business, and an executive member of the iORB committee. Previously he was the Dean's Associate chair. He received his PhD in Electrical Engineering from Stanford University in 2014. His research interests are broadly in the area of high-dimensional statistics, machine learning, optimization, and personal decision making. Adel is the recipient of several highly prestigious awards, including the Alfred Sloan research fellow in mathematics, IMS Tweedie New Researcher Award, NSF CAREER award, Faculty Research Awards from Amazon, Adobe, Google and the Thomas Cover dissertation award from IEEE Society. He was the recipient of the Dr. Douglas Basil Award for Junior Business Faculty (2018) and the Deans' award for Research Excellence (2024). He also received the USC Golden Apple Award for teaching in undergraduate program (2022).