In today's transparent markets, e-commerce providers often have to adjust their prices within
short time intervals, eg, to take frequently changing prices of competitors into account …

In several e-commerce scenarios, pricing long-tail products effectively is a central task for
the companies, and there is broad agreement that Artificial Intelligence (AI) will play a …

We study, to the best of our knowledge, the first Bayesian algorithm for unimodal Multi-
Armed Bandit (MAB) problems with graph structure. In this setting, each arm corresponds to …

The price demand relation is a fundamental concept that models how price affects the sale
of a product. It is critical to have an accurate estimate of its parameters, as it will impact the …

According to the main international reports, more pervasive industrial and business-process
automation, thanks to machine learning and advanced analytic tools, will unlock more than …

We introduce a novel framework called combinatorial logistic bandits (CLogB), where in
each round, a subset of base arms (called the super arm) is selected, with the outcome of …

Bandit online multiclass prediction plays an important role in many real-world applications.
In this paper, we propose a unified framework for it in the fully adversarial setting. This …

The accurate estimation of how future demand will react to prices is central to the
optimization of pricing decisions. The systems responsible for demand prediction and …

We investigate using a multi-armed bandit (MAB) setting for modeling repeated Cournot
oligopoly games. Agents interact with separate bandit problems. An agent can choose from …

This paper addresses the question of whether or not uncoupled online learning algorithms
converge to the Nash equilibrium in pricing competition or whether they can learn to collude …