Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Data valuation has wide use cases in machine learning, including improving data quality
and creating economic incentives for data sharing. This paper studies the robustness of data …

In recent years, data has become one of the most important resources in the digital
economy. Unlike traditional resources, the digital nature of data makes it difficult to value …

Quantifying the impact of training data points is crucial for understanding the outputs of
machine learning models and for improving the transparency of the AI pipeline. The …

Abstract Generalized Zero-Shot Learning (GZSL) has emerged as a pivotal research domain
in computer vision, owing to its capability to recognize objects that have not been seen …

Large language models (LLMs) are trained on a vast amount of human-written data, but data
providers often remain uncredited. In response to this issue, data valuation (or data …

Data valuation, a growing field that aims at quantifying the usefulness of individual data
sources for training machine learning (ML) models, faces notable yet often overlooked …

Many recent works on understanding deep learning try to quantify how much individual data
instances influence the optimization and generalization of a model. Such attempts reveal …

Physics-Informed Neural Networks (PINNs), which incorporate PDEs as soft constraints,
train with a composite loss function that contains multiple training point types: different types …

Traditionally, data selection has been studied in settings where all samples from prospective
sources are fully revealed to a machine learning developer. However, in practical data …