Measuring the value of individual samples is critical for many data-driven
tasks, e.g., the training of a deep learning model. Recent literature witnesses
the substantial efforts in developing data valuation methods. The primary data
valuation methodology is based on the Shapley value from game theory, and
various methods are proposed along this path. {Even though Shapley value-based
valuation has solid theoretical basis, it is entirely an experiment-based
approach and no valuation model has been constructed so far.} In addition,
current data valuation methods ignore the interpretability of the output
values, despite an interptable data valuation method is of great helpful for
applications such as data pricing. This study aims to answer an important
question: is data valuation learnable and interpretable? A learned valuation
model have several desirable merits such as fixed number of parameters and
knowledge reusability. An intrepretable data valuation model can explain why a
sample is valuable or invaluable. To this end, two new data value modeling
frameworks are proposed, in which a multi-layer perception~(MLP) and a new
regression tree are utilized as specific base models for model training and
interpretability, respectively. Extensive experiments are conducted on
benchmark datasets. {The experimental results provide a positive answer for the
question.} Our study opens up a new technical path for the assessing of data
values. Large data valuation models can be built across many different
data-driven tasks, which can promote the widespread application of data
valuation.

通过提出两种新的数据价值建模框架，利用多层感知器和新的回归树作为基础模型进行模型训练和解释性，开展了大量基准数据集上的实验。实验结果积极地回答了数据估值可学习和可解释的重要问题，并为数据价值评估打开了一条新的技术路径。

数据估值是否可学习和可解释的？

Is Data Valuation Learnable and Interpretable?

Contextual dynamic pricing aims to set personalized prices based on
sequential interactions with customers. At each time period, a customer who is
interested in purchasing a product comes to the platform. The customer's
valuation for the product is a linear function of contexts, including product
and customer features, plus some random market noise. The seller does not
observe the customer's true valuation, but instead needs to learn the valuation
by leveraging contextual information and historical binary purchase feedbacks.
Existing models typically assume full or partial knowledge of the random noise
distribution. In this paper, we consider contextual dynamic pricing with
unknown random noise in the valuation model. Our distribution-free pricing
policy learns both the contextual function and the market noise simultaneously.
A key ingredient of our method is a novel perturbed linear bandit framework,
where a modified linear upper confidence bound algorithm is proposed to balance
the exploration of market noise and the exploitation of the current knowledge
for better pricing. We establish the regret upper bound and a matching lower
bound of our policy in the perturbed linear bandit framework and prove a
sub-linear regret bound in the considered pricing problem. Finally, we
demonstrate the superior performance of our policy on simulations and a
real-life auto-loan dataset.

本研究旨在探讨基于上下文的动态定价，采用一种新颖的扰动线性 bandit 框架来平衡市场噪音与当前知识的利用，学习上下文函数和市场噪音，证明了所述策略在扰动线性 bandit 框架下的遗憾上界和匹配下界，并且在模拟和现实场景中表现出卓越的性能。