Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

The histological assessment of human tissue has emerged as the key challenge for
detection and treatment of cancer. A plethora of different data sources ranging from tissue …

Online learning is a well established learning paradigm which has both theoretical and
practical appeals. The goal of online learning is to make a sequence of accurate predictions …

One of the most important obstacles to deploying predictive models is the fact that humans
do not understand and trust them. Knowing which variables are important in a model's …

Algorithms in varied fields use the idea of maintaining a distribution over a certain set and
use the multiplicative update rule to iteratively change these weights. Their analyses are …

The compressive sensing (CS) framework aims to ease the burden on analog-to-digital
converters (ADCs) by reducing the sampling rate required to acquire and stably recover …

In an online decision problem, one makes a sequence of decisions without knowledge of the
future. Each period, one pays a cost based on the decision and observed state. We give a …

Documents are stored in a digital form across several organizations. Printing this amount of
data and placing it into folders instead of storing digitally is against the practical, economical …

The advancements in smart power grid and the advocation of “green communications” have
inspired the wireless communication networks to harness energy from ambient …

This paper consists of an overview on universal prediction from an information-theoretic
perspective. Special attention is given to the notion of probability assignment under the self …