Artificial intelligence systems are increasingly being used in industrial applications, security
and military contexts, disaster response complexes, policing and justice practices, finance …

One of the goals of learning algorithms is to complement and reduce the burden on human
decision makers. The expert deferral setting wherein an algorithm can either predict on its …

Creating large-scale high-quality labeled datasets is a major bottleneck in supervised
machine learning workflows. Threshold-based auto-labeling (TBAL), where validation data …

Online active learning is a paradigm in machine learning that aims to select the most
informative data points to label from a data stream. The problem of minimizing the cost …

The idea of combining the active query strategy and the passive-aggressive (PA) update
strategy in online learning can be credited to the PA active (PAA) algorithm, which has …

When machine learning models encounter data which is out of the distribution on which they
were trained they have a tendency to behave poorly, most prominently over-confidence in …

Active learning is a paradigm of machine learning which aims at reducing the amount of
labeled data needed to train a classifier. Its overall principle is to sequentially select the most …

Abstract Language models and other recent machine learning paradigms blur the distinction
between generative and discriminative tasks, in a continuum that is regulated by the degree …

In this paper, we propose deep architectures for learning instance specific abstain (reject
option) binary classifiers. The proposed approach uses double sigmoid loss function as …

Creating large-scale high-quality labeled datasets is a major bottleneck in supervised
machine learning workflows. Auto-labeling systems are a promising way to reduce reliance …