5 papers · 1 filter
A Semismooth Newton Augmented Lagrangian Method for Sparse Spectral Risk Optimization
Rufeng Xiao, Rujun Jiang, Xudong Li +1
Empirical risk minimization is a standard and effective paradigm for learning predictive models by minimizing average loss. In high-stakes decision-making, however, an average-loss…
Adaptive Algorithms for Nonconvex Bilevel Optimization under PŁ Conditions
Xu Shi, Yinglin Du, Rufeng Xiao +1
Existing methods for nonconvex bilevel optimization (NBO) require prior knowledge of first- and second-order problem-specific parameters (e.g., Lipschitz constants and the Polyak-Ł…
An Alternating Direction Method of Multipliers for Utility-based Shortfall Risk Portfolio Optimization
Rufeng Xiao, Zhiping Li, Rujun Jiang
Utility-based shortfall risk (UBSR), a convex risk measure sensitive to tail losses, has gained popularity in recent years. However, research on computational methods for UBSR opti…
An Adaptive Algorithm for Bilevel Optimization on Riemannian Manifolds
Xu Shi, Rufeng Xiao, Rujun Jiang
Existing methods for solving Riemannian bilevel optimization (RBO) problems require prior knowledge of the problem's first- and second-order information and curvature parameter of…
A Unified Framework for Rank-based Loss Minimization
Rufeng Xiao, Yuze Ge, Rujun Jiang +1
The empirical loss, commonly referred to as the average loss, is extensively utilized for training machine learning models. However, in order to address the diverse performance req…