TP2a.1

Efficient Methods for Bilevel Optimization with Polyak-Lojasiewicz Lower-level Problems

Quan Xiao, Rensselaer Polytechinic Institute, United States; Songtao Lu, IBM Research, United States; Tianyi Chen, Rensselaer Polytechinic Institute, United States
Session:
TP2a: Bilevel Optimization (Invited) Lecture
Track:
Adaptive Systems, Machine Learning, Data Analytics
Location:
Oak Shelter
Presentation Time:
Tue, 31 Oct, 13:30 - 13:55 PT (UTC -8)
Session Co-Chairs:
Hoi-To Wai, Chinese University of Hong Kong and Mingyi Hong, University of Minnesota
Presentation
Discussion
Resources
No resources available.
Session TP2a
TP2a.1: Efficient Methods for Bilevel Optimization with Polyak-Lojasiewicz Lower-level Problems
Quan Xiao, Rensselaer Polytechinic Institute, United States; Songtao Lu, IBM Research, United States; Tianyi Chen, Rensselaer Polytechinic Institute, United States
TP2a.2: Stochastic Perturbation Based Smoothing for Linearly Constrained Bilevel Optimization
Ioannis Tsaknakis, University of Minnesota, United States; Prashant Khanduri, Wayne State University, United States; Mingyi Hong, University of Minnesota, United States
TP2a.3: A Conditional Gradient-based Method for Simple Bilevel Optimization with Convex Lower-level Problem
Ruichen Jiang, The University of Texas at Austin, United States; Nazanin Abolfazli, University of Arizona, United States; Aryan Mokhtari, The University of Texas at Austin, United States; Erfan Yazdandoost Hamedani, University of Arizona, United States
TP2a.4: Efficient Bilevel Optimization and Application in Machine Learning
Kaiyi Ji, University at Buffalo, United States
Contacts