11 citations · 27 across the 6 of their papers we have counts for
15 papers
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-…
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…
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…
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…