Neural Dynamic Data Valuation: A Stochastic Optimal Control Approach
This work addresses data valuation for applications in the data economy, such as model training and market transactions, but appears incremental as it builds on existing valuation methods with a new dynamic approach.
The paper tackles the problem of data valuation by introducing Neural Dynamic Data Valuation (NDDV), a framework that formulates it as a stochastic optimal control problem to capture dynamic data utility over time, addressing limitations like high computational cost and weak fairness in existing methods.
Data valuation has become a cornerstone of the modern data economy, where datasets function as tradable intellectual assets that drive decision-making, model training, and market transactions. Despite substantial progress, existing valuation methods remain limited by high computational cost, weak fairness guarantees, and poor interpretability, which hinder their deployment in large-scale, high-stakes applications. This paper introduces Neural Dynamic Data Valuation (NDDV), a new framework that formulates data valuation as a stochastic optimal control problem to capture the dynamic evolution of data utility over time. Unlike static combinatorial approaches, NDDV models data interactions through continuous trajectories that reflect both individual and collective learning dynamics.