11 citations · 27 across the 7 of their papers we have counts for
6 papers · 1 filter
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-…
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…
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…
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…