3 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.LG2021
Harnessing Heterogeneity: Learning from Decomposed Feedback in Bayesian Modeling
Kai Wang, Bryan Wilder, Sze-chuan Suen +2
There is significant interest in learning and optimizing a complex system composed of multiple sub-components, where these components may be agents or autonomous sensors. Among the…
cs.LG2021★ 1 cited
Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning
Kai Wang, Sanket Shah, Haipeng Chen +3
In the predict-then-optimize framework, the objective is to train a predictive model, mapping from environment features to parameters of an optimization problem, which maximizes de…
cs.GT2021★ 3 cited
Coordinating Followers to Reach Better Equilibria: End-to-End Gradient Descent for Stackelberg Games
Kai Wang, Lily Xu, Andrew Perrault +2
A growing body of work in game theory extends the traditional Stackelberg game to settings with one leader and multiple followers who play a Nash equilibrium. Standard approaches f…