When trying to gain better visibility into a machine learning model in order to understand and
mitigate the associated risks, a potentially valuable source of evidence is: which training …

Influence functions efficiently estimate the effect of removing a single training data point on a
model's learned parameters. While influence estimates align well with leave-one-out …

The goal of data attribution is to trace model predictions back to training data. Despite a long
line of work towards this goal, existing approaches to data attribution tend to force users to …

Good models require good training data. For overparameterized deep models, the causal
relationship between training data and model predictions is increasingly opaque and poorly …

Although mainstream unsupervised anomaly detection (AD) algorithms perform well in
academic datasets, their performance is limited in practical application due to the ideal …

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 …

Test-time task adaptation in few-shot learning aims to adapt a pre-trained task-agnostic
model for capturing task-specific knowledge of the test task, rely only on few-labeled support …

External data sources are increasingly being used to train machine learning (ML) models as
the data demand increases. However, the integration of external data into training poses …

Facial Expression Recognition (FER) plays a crucial role in the real-world applications.
However, large-scale FER datasets collected in the wild usually contain noises. More …

Noisy labels are unavoidable yet troublesome in the ecosystem of deep learning because
models can easily overfit them. There are many types of label noise, such as symmetric …