  High-quality data is crucial for accurate machine learning and actionable
analytics, however, mislabeled or noisy data is a common problem in many
domains. Distinguishing low- from high-quality data can be challenging, often
requiring expert knowledge and considerable manual intervention. Data Valuation
algorithms are a class of methods that seek to quantify the value of each
sample in a dataset based on its contribution or importance to a given
predictive task. These data values have shown an impressive ability to identify
mislabeled observations, and filtering low-value data can boost machine
learning performance. In this work, we present a simple alternative to existing
methods, termed Data Valuation with Gradient Similarity (DVGS). This approach
can be easily applied to any gradient descent learning algorithm, scales well
to large datasets, and performs comparably or better than baseline valuation
methods for tasks such as corrupted label discovery and noise quantification.
We evaluate the DVGS method on tabular, image and RNA expression datasets to
show the effectiveness of the method across domains. Our approach has the
ability to rapidly and accurately identify low-quality data, which can reduce
the need for expert knowledge and manual intervention in data cleaning tasks.
