6 papers
Stochastic Momentum Methods for Non-smooth Non-Convex Finite-Sum Coupled Compositional Optimization
Xingyu Chen, Bokun Wang, Ming Yang +2
Finite-sum Coupled Compositional Optimization (FCCO), characterized by its coupled compositional objective structure, emerges as an important optimization paradigm for addressing a…
Single-loop Algorithms for Stochastic Non-convex Optimization with Weakly-Convex Constraints
Ming Yang, Gang Li, Quanqi Hu +2
Constrained optimization with multiple functional inequality constraints has significant applications in machine learning. This paper examines a crucial subset of such problems whe…
A Note on Complexity for Two Classes of Structured Non-Smooth Non-Convex Compositional Optimization
Yao Yao, Qihang Lin, Tianbao Yang
This note studies numerical methods for solving compositional optimization problems, where the inner function is smooth, and the outer function is Lipschitz continuous, non-smooth,…
A Retention-Centric Framework for Continual Learning with Guaranteed Model Developmental Safety
Gang Li, Wendi Yu, Yao Yao +4
In real-world applications, learning-enabled systems often undergo iterative model development to address challenging or emerging tasks, which involve collecting new data, training…
Multi-Output Distributional Fairness via Post-Processing
Gang Li, Qihang Lin, Ayush Ghosh +1
The post-processing approaches are becoming prominent techniques to enhance machine learning models' fairness because of their intuitiveness, low computational cost, and excellent…
Deterministic and Stochastic Accelerated Gradient Method for Convex Semi-Infinite Optimization
Yao Yao, Qihang Lin, Tianbao Yang
This paper explores numerical methods for solving a convex differentiable semi-infinite program. We introduce a primal-dual gradient method which performs three updates iteratively…