241 citations · 815 across the 44 of their papers we have counts for
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Model-Agnostic Linear Competitors -- When Interpretable Models Compete and Collaborate with Black-Box Models
Hassan Rafique, Tong Wang, Qihang Lin
Driven by an increasing need for model interpretability, interpretable models have become strong competitors for black-box models in many real applications. In this paper, we propo…
Inexact Proximal-Point Penalty Methods for Constrained Non-Convex Optimization
Qihang Lin, Runchao Ma, Yangyang Xu
In this paper, an inexact proximal-point penalty method is studied for constrained optimization problems, where the objective function is non-convex, and the constraint functions c…
A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints
Qihang Lin, Selvaprabu Nadarajah, Negar Soheili +1
Stochastic convex optimization problems with expectation constraints (SOECs) are encountered in statistics and machine learning, business, and engineering. In data-rich environment…
Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints
Runchao Ma, Qihang Lin, Tianbao Yang
Optimization models with non-convex constraints arise in many tasks in machine learning, e.g., learning with fairness constraints or Neyman-Pearson classification with non-convex l…
Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model
Tong Wang, Qihang Lin
Interpretable machine learning has become a strong competitor for traditional black-box models. However, the possible loss of the predictive performance for gaining interpretabilit…
Stochastic Primal-Dual Algorithms with Faster Convergence than for Problems without Bilinear Structure
Yan Yan, Yi Xu, Qihang Lin +2
Previous studies on stochastic primal-dual algorithms for solving min-max problems with faster convergence heavily rely on the bilinear structure of the problem, which restricts th…
Distributionally Robust Optimization with Confidence Bands for Probability Density Functions
Xi Chen, Qihang Lin, Guanglin Xu
Distributionally robust optimization (DRO) has been introduced for solving stochastic programs where the distribution of the random parameters is unknown and must be estimated by s…