Real-world classification problems typically exhibit an imbalanced or long-tailed label
distribution, wherein many labels are associated with only a few samples. This poses a …

What does it mean for an algorithm to be biased? In US law, unintentional bias is encoded
via disparate impact, which occurs when a selection process has widely different outcomes …

Data imbalance is frequently encountered in biomedical applications. Resampling
techniques can be used in binary classification to tackle this issue. However such solutions …

Learning discriminative image representations plays a vital role in long-tailed image
classification because it can ease the classifier learning in imbalanced cases. Given the …

Real-world datasets are typically imbalanced in the sense that only a few classes have
numerous samples, while many classes are associated with only a few samples. As a result …

Imbalanced datasets are commonplace in modern machine learning problems. The
presence of under-represented classes or groups with sensitive attributes results in …

Many supervised learning problems involve learning from samples whose labels are
corrupted in some way. For example, each sample may have some constant probability of …

Ensuring that classifiers are non-discriminatory or fair with respect to a sensitive feature (eg,
race or gender) is a topical problem. Progress in this task requires fixing a definition of …

Test-time adaptation (TTA) adapts a source model to the distribution shift in testing data
without using any source data. There have been plenty of algorithms concentrated on …

Performance metrics for binary classification are designed to capture tradeoffs between four
fundamental population quantities: true positives, false positives, true negatives and false …