Speaker
Nicolò Cesa-Bianchi
(University of Milan, Italy)
Description
Online learning explores algorithms that acquire knowledge sequentially, through repeated interactions with an unknown environment. The general goal is to understand how fast an agent can learn based on the information received from the environment. Digital markets, with their complex ecosystems of algorithmic agents, offer a rich landscape of sequential decision-making problems, characterized by diverse decision spaces, utility functions, and feedback mechanisms. This talk will demonstrate how tackling challenges within digital markets has not only advanced our understanding of machine learning capabilities but also revealed novel insights into algorithmic efficiency and decision-making under uncertainty.
