How can we explain the predictions of a black-box model? In this paper, we use influence
functions—a classic technique from robust statistics—to trace a model's prediction through …

Along with the rapid development and wide application of information technology, human
society has entered the information era. In this era, people live and work in cyberspace …

The great success of deep learning heavily relies on increasingly larger training data, which
comes at a price of huge computational and infrastructural costs. This poses crucial …

Influence functions estimate the effect of removing a training point on a model without the
need to retrain. They are based on a first-order Taylor approximation that is guaranteed to …

With the fast development of algorithmic governance, fairness has become a compulsory
property for machine learning models to suppress unintentional discrimination. In this paper …

Influence functions approximate the" influences" of training data-points for test predictions
and have a wide variety of applications. Despite the popularity, their computational cost …

Background Entropy is a nonlinear index that can reflect the degree of chaos within a
system. It is often used to analyze epileptic electroencephalograms (EEG) to detect whether …

To deal with ambiguities in partial label learning (PLL), state-of-the-art strategies implement
disambiguations by identifying the ground-truth label directly from the candidate label set …

Kernel $ k $-means is one of the most popular approaches to clustering and its theoretical
properties have been investigated for decades. However, the existing state-of-the-art risk …

In this paper, we study the generalization performance of multi-class classification and
obtain a shaper data-dependent generalization error bound with fast convergence rate …