While the inverse sensitivity mechanism was shown to be instance optimal, it was only with respect to a class of unbiased mechanisms such that the most likely outcome matches the underlying data.

We derive a tighter inequality than Cauchy-Schwarz Inequality, leverage it to refine Pearson's

Recentstudieshaveshownthat the transferability of adversarial examples exists for CNNs, and the same holds true for ViTs.

Inference via knowledge compilation has also been used for many applications in neuro-symbolic AI,suchasconstrained generation [2,54]andneural logic programming [34,28].

The learner's goal is to identify the optimal arm

Risk-averse reinforcement learning (RL) seeks to provide a risk-averse policy for high-stakes real-world decision problems. These high-stake domains include autonomous driving (Jin et al., 2019; Sharma et al., 2020), robot collision avoidance (Ahmadi et al., 2021; Hakobyan and Y ang, 2021),