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cs.LG2023
Provably Efficient Iterated CVaR Reinforcement Learning with Function Approximation and Human Feedback
Yu Chen, Yihan Du, Pihe Hu +3
Risk-sensitive reinforcement learning (RL) aims to optimize policies that balance the expected reward and risk. In this paper, we present a novel risk-sensitive RL framework that e…
cs.LG2023
A Game-theoretic Framework for Privacy-preserving Federated Learning
Xiaojin Zhang, Lixin Fan, Siwei Wang +3
In federated learning, benign participants aim to optimize a global model collaboratively. However, the risk of \textit{privacy leakage} cannot be ignored in the presence of \texti…