Conference paper
Domain Adaptation meets Individual Fairness. And they get along.
Debarghya Mukherjee, Felix Petersen, et al.
NeurIPS 2022
We consider a new family of stochastic operators for reinforcement learning that seek to alleviate negative effects and become more robust to approximation or estimation errors. Theoretical results are established, showing that our family of operators preserve optimality and increase the action gap in a stochastic sense. Empirical results illustrate the strong benefits of our robust stochastic operators, significantly outperforming the classical Bellman and recently proposed operators.
Debarghya Mukherjee, Felix Petersen, et al.
NeurIPS 2022
Wojciech Ozga, Do Le Quoc , et al.
IFIP DBSec 2021
Fearghal O'Donncha, Yihao Hu, et al.
Ecol. Inform.
Advait Parulekar, Karthikeyan Shanmugam, et al.
ICML 2023