This week Dr. Tim Scarfe, Dr. Keith Duggar and Yannic Kilcher discuss multi-arm bandits and pure exploration with Dr. Wouter M. Koolen, Senior Researcher, Machine Learning group, Centrum Wiskunde & Informatica.

Wouter specialises in machine learning theory, game theory, information theory, statistics and optimisation. Wouter is currently interested in pure exploration in multi-armed bandit models, game tree search, and accelerated learning in sequential decision problems. His research has been cited 1000 times, and he has been published in NeurIPS, the number 1 ML conference 14 times as well as lots of other exciting publications.

Today we are going to talk about two of the most studied settings in control, decision theory, and learning in unknown environment which are the multi-armed bandit (MAB) and reinforcement learning (RL) approaches

- when can an agent stop learning and start exploiting using the knowledge it obtained

- which strategy leads to minimal learning time

00:00:00 What are multi-arm bandits/show trailer

00:12:55 Show introduction

00:15:50 Bandits

00:18:58 Taxonomy of decision framework approaches

00:25:46 Exploration vs Exploitation

00:31:43 the sharp divide between modes

00:34:12 bandit measures of success

00:36:44 connections to reinforcement learning

00:44:00 when to apply pure exploration in games

00:45:54 bandit lower bounds, a pure exploration renaissance

00:50:21 pure exploration compiler dreams

00:51:56 what would the PX-compiler DSL look like

00:57:13 the long arms of the bandit

01:00:21 causal models behind the curtain of arms

01:02:43 adversarial bandits, arms trying to beat you

01:05:12 bandits as an optimization problem

01:11:39 asymptotic optimality vs practical performance

01:15:38 pitfalls hiding under asymptotic cover

01:18:50 adding features to bandits

01:27:24 moderate confidence regimes

01:30:33 algorithms choice is highly sensitive to bounds

01:46:09 Post script: Keith interesting piece on n quantum

http://wouterkoolen.info

https://www.cwi.nl/research-groups/ma...

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