The label noise transition matrix, denoting the transition probabilities from clean labels to
noisy labels, is crucial for designing statistically robust solutions. Existing estimators for …

In the presence of noisy labels, designing robust loss functions is critical for securing the
generalization performance of deep neural networks. Cross Entropy (CE) loss has been …

Language models have shown promise in various tasks but can be affected by undesired
data during training, fine-tuning, or alignment. For example, if some unsafe conversations …

Existing works on anomaly detection (AD) rely on clean labels from human annotators that
are expensive to acquire in practice. In this work, we propose a method to leverage …

As the size of the datasets getting larger, accurately annotating such datasets is becoming
more impractical due to the expensiveness on both time and economy. Therefore, crowd …

The rawly collected training data often comes with separate noisy labels collected from
multiple imperfect annotators (eg, via crowdsourcing). A typical way of using these separate …

Evaluating fairness can be challenging in practice because the sensitive attributes of data
are often inaccessible due to privacy constraints. The go-to approach that the industry …

Using noisy crowdsourced labels from multiple annotators, a deep learning-based end-to-
end (E2E) system aims to learn the label correction mechanism and the neural classifier …

Learning from crowds describes that the annotations of training data are obtained with
crowd-sourcing services. Multiple annotators each complete their own small part of the …

Crowdsourcing provides a practical approach for obtaining annotated data to train
supervised learning models. However, since the crowd annotators may have different …