Given an unsupervised outlier detection task, how should one select i) a detection algorithm,
and ii) associated hyperparameter values (jointly called a model)? E ective outlier model …

Detecting which nodes in graphs are outliers is a relatively new machine learning task with
numerous applications. Despite the proliferation of algorithms developed in recent years for …

In the context of biomedical data, an anomaly could refer to a rare or new type of disease, an
aberration from normal behavior, or an unexpected observation requiring immediate …

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 …

Unsupervised Outlier Detection (UOD) is an important data mining task. With the advance of
deep learning, deep Outlier Detection (OD) has received broad interest. Most deep UOD …

Capturing similarity among cells is at the core of many tasks in single-cell transcriptomics,
such as the identification of cell types and cell states. This problem can be formulated in a …

We propose a new method, dubbed One Class Signed Distance Function (OCSDF), to
perform One Class Classification (OCC) by provably learning the Signed Distance Function …

Self-supervised learning (SSL) has emerged as a promising alternative to create
supervisory signals to real-world problems, avoiding the extensive cost of manual labeling …

Anomaly detection requires detecting abnormal samples in large unlabeled datasets. While
progress in deep learning and the advent of foundation models has produced powerful …

The astonishing successes of ML have raised growing concern for the fairness of modern
methods when deployed in real world settings. However, studies on fairness have mostly …