Traditionally, the hinge loss is used to construct support vector machine (SVM) classifiers.
The hinge loss is related to the shortest distance between sets and the corresponding …

Twin support-vector machine (TSVM), which generates two nonparallel hyperplanes by
solving a pair of smaller-sized quadratic programming problems (QPPs) instead of a single …

The so-called pinball loss for estimating conditional quantiles is a well-known tool in both
statistics and machine learning. So far, however, only little work has been done to quantify …

The standard twin support vector machine (TSVM) uses the hinge loss function which leads
to noise sensitivity and instability. In this paper, we propose a novel general twin support …

Feature noise, namely noise on inputs is a long-standing plague to support vector machine
(SVM). Conventional SVM with the hinge loss (C-SVM) is sparse but sensitive to feature …

We report on an empirical study of the main strategies for quantile regression in the context
of stochastic computer experiments. To ensure adequate diversity, six metamodels are …

Untitled Page 1 Page 2 Information Science and Statistics Series Editors: M. Jordan J.
Kleinberg B. Schölkopf Page 3 Information Science and Statistics For other titles published in …

Multi-calibration is a powerful and evolving concept originating in the field of algorithmic
fairness. For a predictor $ f $ that estimates the outcome $ y $ given covariates $ x $, and for …

Uncertainty quantification is at the core of the reliability and robustness of machine learning.
In this paper, we provide a theoretical framework to dissect the uncertainty, especially …

A new robust loss function (called L q-loss) is proposed based on the concept of quantile
and correntropy, which can be seen as an improved version of quantile loss function. The …