Towards Generalizable Data Valuation: Learning an End-to-end Deep Model to Compute Shapley Values

Data Shapley has emerged as a premier framework for data valuation, yet its practical utility is severely hampered by exponential computational complexity and the need for costly model retraining. Unlike existing methods that compute values independently for each dataset, we propose GenSHAP, a novel graph-based paradigm that reformulates data valuation as a transferable learning task. GenSHAP learns an amortised deep model capable of predicting Data Shapley values for any unseen classification dataset in a single forward pass. Our framework utilises a relation-based dataset representation method and a hybrid GIN-Transformer architecture to capture local and global inter-sample dependencies. We further introduce GenSHAP-S, which augments these structural representations with low-budget marginal contribution probes to achieve high-fidelity valuation. Extensive experiments demonstrate that GenSHAP closely approximates high-budget permutation-Shapley reference estimates and excels in downstream tasks such as data removal and addition while reducing valuation time from hours to seconds. The source code is available in the anonymous repository~\footnote{\url{https://anonymous.4open.science/r/GenSHAP/}}.