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Found 4 papers, 1 papers with code
Achieving ${O}(ε^{-1.5})$ Complexity in Hessian/Jacobian-free Stochastic Bilevel Optimization
In this paper, we revisit the bilevel optimization problem, in which the upper-level objective function is generally nonconvex and the lower-level objective function is strongly convex.
SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning
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, fine-tuning, hyperparameter tuning, etc.
Direction-oriented Multi-objective Learning: Simple and Provable Stochastic Algorithms
In this paper, we propose a new direction-oriented multi-objective problem by regularizing the common descent direction within a neighborhood of a direction that optimizes a linear combination of objectives such as the average loss in MTL.
Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation
Federated bilevel optimization has attracted increasing attention due to emerging machine learning and communication applications.
