In supervised learning, acquiring labeled training data for a predictive model can be very
costly, but acquiring a large amount of unlabeled data is often quite easy. Active learning is …

Abstract Self-Supervised Learning (SSL) has emerged as the solution of choice to learn
transferable representations from unlabeled data. However, SSL requires to build samples …

We investigate the problem of active learning in the streaming setting in non-parametric
regimes, where the labels are stochastically generated from a class of functions on which we …

We present a novel method for classification of Synthetic Aperture Radar (SAR) data by
combining ideas from graph-based learning and neural network methods within an active …

As annotations of data can be scarce in large-scale practical problems, leveraging
unlabelled examples is one of the most important aspects of machine learning. This is the …

Active learning in semi-supervised classification involves introducing additional labels for
unlabelled data to improve the accuracy of the underlying classifier. A challenge is to identify …

We present a novel adaptation of active learning to graph-based semi-supervised learning
(SSL) under non-Gaussian Bayesian models. We present an approximation of non …

We show that uncertainty sampling is sufficient to achieve exploration versus exploitation in
graph-based active learning, as long as the measure of uncertainty properly aligns with the …

Deep neural networks have great representation power, but typically require large numbers
of training examples. This motivates deep active learning methods that can significantly …

The impressive performance exhibited by modern machine learning models hinges on the
ability to train such models on a very large amounts of labeled data. However, since access …