Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

Collaborative Top Distribution Identifications with Limited Interaction

IEEE via YouTube

Overview

Explore a 25-minute IEEE conference talk on collaborative learning strategies for Multi-Armed Bandits (MAB) problems, focusing on top-m distribution identifications with limited interaction. Delve into the intricacies of the Top-m problem in MAB, examining the collaborative learning approach and its algorithm. Understand the key differences between Top-1 and Top-m scenarios, and learn about the reduction technique and error amplification in this context. Presented by Nikolai Karpov, Qin Zhang, and Yuan Zhou from Indiana University at Bloomington and the University of Illinois at Urbana-Champaign, this talk provides valuable insights into advanced MAB problem-solving techniques.

Syllabus

Intro
Top-m problem in Multi-Armed Bandits (MAB)
Collaborative Learning for MAB
Algorithm
Difference between Top-1 and Top-m Top-m
Reduction
Error amplification

Taught by

IEEE FOCS: Foundations of Computer Science

Reviews

Start your review of Collaborative Top Distribution Identifications with Limited Interaction

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.