Zobrazeno 1 - 10
of 182
pro vyhledávání: '"Lin, Qihang"'
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, and non-convex but exhibits one of two special structures that enable
Externí odkaz:
http://arxiv.org/abs/2411.14342
Federated learning (FL) enables multiple clients with distributed data sources to collaboratively train a shared model without compromising data privacy. However, existing FL paradigms face challenges due to heterogeneity in client data distributions
Externí odkaz:
http://arxiv.org/abs/2410.14075
In the real world, a learning-enabled system usually undergoes multiple cycles of model development to enhance the system's ability to handle difficult or emerging tasks. This continual model development process raises a significant issue that the mo
Externí odkaz:
http://arxiv.org/abs/2410.03955
The post-processing approaches are becoming prominent techniques to enhance machine learning models' fairness because of their intuitiveness, low computational cost, and excellent scalability. However, most existing post-processing methods are design
Externí odkaz:
http://arxiv.org/abs/2409.00553
This paper studies learning fair encoders in a self-supervised learning (SSL) setting, in which all data are unlabeled and only a small portion of them are annotated with sensitive attribute. Adversarial fair representation learning is well suited fo
Externí odkaz:
http://arxiv.org/abs/2406.05686
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: a momentum gradient ascend step to update the constraint parameters,
Externí odkaz:
http://arxiv.org/abs/2310.10993
Many recent studies on first-order methods (FOMs) focus on \emph{composite non-convex non-smooth} optimization with linear and/or nonlinear function constraints. Upper (or worst-case) complexity bounds have been established for these methods. However
Externí odkaz:
http://arxiv.org/abs/2307.07605
Autor:
Huang, Yankun, Lin, Qihang
We consider a non-convex constrained optimization problem, where the objective function is weakly convex and the constraint function is either convex or weakly convex. To solve this problem, we consider the classical switching subgradient method, whi
Externí odkaz:
http://arxiv.org/abs/2301.13314
As machine learning being used increasingly in making high-stakes decisions, an arising challenge is to avoid unfair AI systems that lead to discriminatory decisions for protected population. A direct approach for obtaining a fair predictive model is
Externí odkaz:
http://arxiv.org/abs/2212.12603
While deep reinforcement learning has proven to be successful in solving control tasks, the "black-box" nature of an agent has received increasing concerns. We propose a prototype-based post-hoc policy explainer, ProtoX, that explains a blackbox agen
Externí odkaz:
http://arxiv.org/abs/2211.03162