General unsupervised learning is a long-standing conceptual problem in machine learning.
Supervised learning is successful because it can be solved by the minimization of the …

Unsupervised learning methods, as one of the important machine learning methods, have
been developing rapidly, receiving more and more attention since they can automatically …

Machine learning (ML) is a data-driven strategy in which computers learn from data without
human intervention. The outstanding ML applications are used in a variety of areas. In ML …

We give a novel formal theoretical framework for unsupervised learning with two distinctive
characteristics. First, it does not assume any generative model and based on a worst-case …

The phenomenon of double descent has recently gained attention in supervised learning. It
challenges the conventional wisdom of the bias-variance trade-off by showcasing a …

Autoencoders are widely used for unsupervised learning and as a regularization scheme in
semi-supervised learning. However, theoretical understanding of their generalization …

We present a novel strategy for unsupervised feature learning in image applications inspired
by the Spike-Timing-Dependent-Plasticity (STDP) biological learning rule. We show …

In this thesis, we explore the use of complex systems to study learning and adaptation in
natural and artificial systems. The goal is to develop autonomous systems that can learn …

Semi-supervised Learning (SSL) has shown remarkable success in applications with limited
supervision. However, due to the scarcity of labels in the training process, SSL algorithms …

Self-supervised learning is an increasingly popular approach to unsupervised learning,
achieving state-of-the-art results. A prevalent approach consists in contrasting data points …