Recently, extensive literature has shown that the relationship between SC and FC is complex and not a simple one-to-one mapping.

We consider a novel dynamic pricing and learning setting where in addition to setting prices of products in sequential rounds, the seller also ex-ante commits to'advertising schemes'. That is, in the beginning of each round the seller can decide what kind of signal they will provide to the buyer about the product's quality upon realization. Using the popular Bayesian persuasion framework to model the effect of these signals on the buyers' valuation and purchase responses, we formulate the problem of finding an optimal design of the advertising scheme along with a pricing scheme that maximizes the seller's expected revenue. Without any apriori knowledge of the buyers' demand function, our goal is to design an online algorithm that can use past purchase responses to adaptively learn the optimal pricing and advertising strategy. We study the regret of the algorithm when compared to the optimal clairvoyant price and advertising scheme.

Designing and deriving effective model-based reinforcement learning (MBRL) algorithms with a performance improvement guarantee is challenging, mainly attributed to the high coupling between model learning and policy optimization. Many prior methods that rely on return discrepancy to guide model learning ignore the impacts of model shift, which can lead to performance deterioration due to excessive model updates. Other methods use performance difference bound to explicitly consider model shift. However, these methods rely on a fixed threshold to constrain model shift, resulting in a heavy dependence on the threshold and a lack of adaptability during the training process. In this paper, we theoretically derive an optimization objective that can unify model shift and model bias and then formulate a fine-tuning process. This process adaptively adjusts the model updates to get a performance improvement guarantee while avoiding model over-fitting.

Fan et al. [2021] incorporates high-frequency views into contrastive learning, leading to the transfer However, there are also several works that challenge the validity of this assumption. Yin et al. [2019] proposes a robustness analysis strategy based on Fourier Heatmaps, which utilizes a model's sensitivity to frequency-bases. Maiya et al. [2021] believes that model robustness does not have an intrinsic connection In addition to the perspective on frequency components, Chen et al. [2021] has shown that the CNN model should be consistent with the Human Visual System, with To show the power law distribution of natural images, we select CIFAR-10 Krizhevsky et al. [2009], Tiny-ImageNet Le and Y ang [2015] and ImageNet Deng et al. [2009] to conduct experiments. We show an example of division on ImageNet, as shown in Fig.2, in which the high-and low-frequency components of the image obtained according to the division radius are also in line with our We conduct experiments on naturally trained models. We conduct experiments on test set of CIFAR10, Tiny-ImageNet, ImageNet-1k datasets.

A widely discussed hypothesis regarding the cause of visual models' lack of robustness is that they can exploit human-imperceptible high-frequency components