Assessing the quality and impact of individual data points is critical for improving model
performance and mitigating undesirable biases within the training dataset. Several data …

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 …

Data quality is crucial for robust machine learning algorithms, with the recent interest in data-
centric AI emphasizing the importance of training data characterization. However, current …

Data Shapley provides a principled framework for attributing data's contribution within
machine learning contexts. However, existing approaches require re-training models on …

Data Shapley provides a principled approach to data valuation and plays a crucial role in
data-centric machine learning (ML) research. Data selection is considered a standard …

Massive data is often considered essential for deep learning applications, but it also incurs
significant computational and infrastructural costs. Therefore, dataset pruning (DP) has …

This work focuses on leveraging and selecting from vast, unlabeled, open data to pre-fine-
tune a pre-trained language model. The goal is to minimize the need for costly domain …

Data valuation quantifies the contribution of each data point to the performance of a machine
learning model. Existing works typically define the value of data by its improvement of the …

Federated Learning (FL) enables collaborative model training while preserving the privacy
of raw data. A challenge in this framework is the fair and efficient valuation of data which is …

In this paper, we propose WaterMark Detector (WMD), the first invisible watermark detection
method under a black-box and annotation-free setting. WMD is capable of detecting arbitrary …