241 citations · 815 across the 44 of their papers we have counts for
45 papers
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…
First-order Methods for Affinely Constrained Composite Non-convex Non-smooth Problems: Lower Complexity Bound and Near-optimal Methods
Wei Liu, Qihang Lin, Yangyang Xu
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-ca…
Oracle Complexity of Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Functional Constrained Optimization
Yankun Huang, Qihang Lin
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 thi…
Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints
Yao Yao, Qihang Lin, Tianbao Yang
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…
ProtoX: Explaining a Reinforcement Learning Agent via Prototyping
Ronilo J. Ragodos, Tong Wang, Qihang Lin +1
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-…
Federated Learning on Adaptively Weighted Nodes by Bilevel Optimization
Yankun Huang, Qihang Lin, Nick Street +1
We propose a federated learning method with weighted nodes in which the weights can be modified to optimize the model's performance on a separate validation set. The problem is for…
Large-scale Optimization of Partial AUC in a Range of False Positive Rates
Yao Yao, Qihang Lin, Tianbao Yang
The area under the ROC curve (AUC) is one of the most widely used performance measures for classification models in machine learning. However, it summarizes the true positive rates…
Inexact accelerated proximal gradient method with line search and reduced complexity for affine-constrained and bilinear saddle-point structured convex problems
Qihang Lin, Yangyang Xu
The goal of this paper is to reduce the total complexity of gradient-based methods for two classes of problems: affine-constrained composite convex optimization and bilinear saddle…
An Adaptive Parameter-free and Projection-free Restarting Level Set Method for Constrained Convex Optimization Under the Error Bound Condition
Qihang Lin, Negar Soheili, Runchao Ma +1
Recent efforts to accelerate first-order methods have focused on convex optimization problems that satisfy a geometric property known as error-bound condition, which covers a broad…
Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization
Yan Yan, Yi Xu, Qihang Lin +2
Epoch gradient descent method (a.k.a. Epoch-GD) proposed by Hazan and Kale (2011) was deemed a breakthrough for stochastic strongly convex minimization, which achieves the optimal…
Self-guided Approximate Linear Programs
Parshan Pakiman, Selvaprabu Nadarajah, Negar Soheili +1
Approximate linear programs (ALPs) are well-known models based on value function approximations (VFAs) to obtain policies and lower bounds on the optimal policy cost of discounted-…
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…
Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence
Yi Xu, Qi Qi, Qihang Lin +2
Difference of convex (DC) functions cover a broad family of non-convex and possibly non-smooth and non-differentiable functions, and have wide applications in machine learning and…
First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems
Mingrui Liu, Hassan Rafique, Qihang Lin +1
In this paper, we consider first-order convergence theory and algorithms for solving a class of non-convex non-concave min-max saddle-point problems, whose objective function is we…
Weakly-Convex Concave Min-Max Optimization: Provable Algorithms and Applications in Machine Learning
Hassan Rafique, Mingrui Liu, Qihang Lin +1
Min-max problems have broad applications in machine learning, including learning with non-decomposable loss and learning with robustness to data distribution. Convex-concave min-ma…
Comparison-Based Algorithms for One-Dimensional Stochastic Convex Optimization
Xi Chen, Qihang Lin, Zizhuo Wang
Stochastic optimization finds a wide range of applications in operations research and management science. However, existing stochastic optimization techniques usually require the i…
A Unified Analysis of Stochastic Momentum Methods for Deep Learning
Yan Yan, Tianbao Yang, Zhe Li +2
Stochastic momentum methods have been widely adopted in training deep neural networks. However, their theoretical analysis of convergence of the training objective and the generali…
Prophit: Causal inverse classification for multiple continuously valued treatment policies
Michael T. Lash, Qihang Lin, W. Nick Street
Inverse classification uses an induced classifier as a queryable oracle to guide test instances towards a preferred posterior class label. The result produced from the process is a…