#030 Multi-Armed Bandits and Pure-Exploration (Wouter M. Koolen)

Machine Learning Street Talk (MLST) - En podcast af Machine Learning Street Talk (MLST)

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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... #machinelearning

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