2 citations · 4 across the 18 of their papers we have counts for
5 papers · 1 filter
Risk-Averse Certification of Bayesian Neural Networks
Xiyue Zhang, Zifan Wang, Yulong Gao +3
In light of the inherently complex and dynamic nature of real-world environments, incorporating risk measures is crucial for the robustness evaluation of deep learning models. In t…
Risk-averse learning with delayed feedback
Siyi Wang, Zifan Wang, Karl Henrik Johansson +1
In real-world scenarios, risk-averse learning is valuable for mitigating potential adverse outcomes. However, the delayed feedback makes it challenging to assess and manage risk ef…
Risk-averse Learning with Non-Stationary Distributions
Siyi Wang, Zifan Wang, Xinlei Yi +3
Considering non-stationary environments in online optimization enables decision-maker to effectively adapt to changes and improve its performance over time. In such cases, it is fa…
Learning of Nash Equilibria in Risk-Averse Games
Zifan Wang, Yi Shen, Michael M. Zavlanos +1
This paper considers risk-averse learning in convex games involving multiple agents that aim to minimize their individual risk of incurring significantly high costs. Specifically,…
Policy Evaluation in Distributional LQR (Extended Version)
Zifan Wang, Yulong Gao, Siyi Wang +3
Distributional reinforcement learning (DRL) enhances the understanding of the effects of the randomness in the environment by letting agents learn the distribution of a random retu…