Lecture

Multi-arm Bandits: Regret and Exploration

Description

This lecture delves into the concept of regret in multi-arm bandit problems, exploring the trade-off between exploration and exploitation. The instructor explains how to calculate the expected regret over time steps, emphasizing the importance of the gap between optimal choices. The lecture covers the impact of time horizon on decision-making and introduces concentration bounds for tail probabilities. The discussion extends to Gaussian random variables, moment-generating functions, and the turn-off bound. The instructor highlights the challenges of balancing exploration and exploitation, showcasing the implications for real-world applications like internet advertising. The lecture concludes by hinting at future topics, including information-theoretic concepts and practical extensions of bandit algorithms.

About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.