32 citations · 226 across the 30 of their papers we have counts for
30 papers
Non-Convex Bilevel Optimization with Time-Varying Objective Functions
Sen Lin, Daouda Sow, Kaiyi Ji +2
Bilevel optimization has become a powerful tool in a wide variety of machine learning problems. However, the current nonconvex bilevel optimization considers an offline dataset and…
SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning
Yifan Yang, Peiyao Xiao, Kaiyi Ji
Federated bilevel optimization (FBO) has shown great potential recently in machine learning and edge computing due to the emerging nested optimization structure in meta-learning, f…
Direction-oriented Multi-objective Learning: Simple and Provable Stochastic Algorithms
Peiyao Xiao, Hao Ban, Kaiyi Ji
Multi-objective optimization (MOO) has become an influential framework in many machine learning problems with multiple objectives such as learning with multiple criteria and multi-…
Achieving Linear Speedup in Non-IID Federated Bilevel Learning
Minhui Huang, Dewei Zhang, Kaiyi Ji
Federated bilevel optimization has received increasing attention in various emerging machine learning and communication applications. Recently, several Hessian-vector-based algorit…
Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation
Peiyao Xiao, Kaiyi Ji
Federated bilevel optimization has attracted increasing attention due to emerging machine learning and communication applications. The biggest challenge lies in computing the gradi…
Network Utility Maximization with Unknown Utility Functions: A Distributed, Data-Driven Bilevel Optimization Approach
Kaiyi Ji, Lei Ying
Fair resource allocation is one of the most important topics in communication networks. Existing solutions almost exclusively assume each user utility function is known and concave…