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

EPISODE · Nov 20, 2020 · 1H 48M

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

from Machine Learning Street Talk (MLST)

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

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

NOW PLAYING

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

0:00 1:48:08

No transcript for this episode yet

We transcribe on demand. Request one and we'll notify you when it's ready — usually under 10 minutes.

French Your Way Jessica: Native French teacher founder of French Your Way Boost your French listening skills and test your comprehension with this one of a kind series of podcasts. Get the chance to listen to a real conversation between native speakers talking at normal speed AND customise your learning experience through carefully designed sets of questions (2 levels of difficulty) available for download at www.frenchvoicespodcast.com. All interviews also come with the transcript. French teacher Jessica interviews native speakers of French from around the world who share a bit of their life and passion. Where else would you meet in one same place a French yoga teacher based in Melbourne, a soap manufacturer from Provence, or a couple cycling around the world? Kaizen Blueprint Aldo Chandra "Kaizen" is a Japanese term for continuous improvement. This podcast provides a blueprint to learn about health, wealth, relationships and everything else in between. Through our podcast, we strive to inspire, educate, and motivate our audience to cultivate a mindset of lifelong learning, productivity, and personal development. By sharing insights, strategies, and practical tips, we aim to guide listeners on their journey towards realizing their fullest potential, fostering success, and creating lasting positive change. One Man Went To Row PepperDawesMedia Follow the journey, from training to finish line, of a man from Derby, UK who is going from having only ever rowed on a machine to rowing 3000 miles solo across the Atlantic...just after his 70th birthday! Humanizing Change Tremendousness Join us each episode as we talk with innovators in their respective fields about their unique journeys and how they humanize change in their own work, right here, on Humanizing Change.

Frequently Asked Questions

How long is this episode of Machine Learning Street Talk (MLST)?

This episode is 1 hour and 48 minutes long.

When was this Machine Learning Street Talk (MLST) episode published?

This episode was published on November 20, 2020.

What is this episode about?

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...

Can I download this Machine Learning Street Talk (MLST) episode?

Yes, you can download this episode by clicking the download button on the episode player, or subscribe to the podcast in your preferred podcast app for automatic downloads.
URL copied to clipboard!