Learning by Exploration: New Challenges in Real-World Environments

Qingyun Wu, Huazheng Wang, Hongning Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Learning is a predominant theme for any intelligent system, humans, or machines. Moving beyond the classical paradigm of learning from past experience, e.g., offline supervised learning from given labels, a learner needs to actively collect exploratory feedback to learn from the unknowns, i.e., learning through exploration. This tutorial will introduce the learning by exploration paradigm, which is the key ingredient in many interactive online learning problems, including the multi-armed bandit and, more generally, reinforcement learning problems. In this tutorial, we will first motivate the need for exploration in machine learning algorithms and highlight its importance in many real-world problems where online sequential decision making is involved. In real-world application scenarios, considerable challenges arise in such a learning problem, including sample complexity, costly and even outdated feedback, and ethical considerations of exploration (such as fairness and privacy). We will introduce several classical exploration strategies and then highlight the aforementioned three fundamental challenges in the learning from exploration paradigm and introduce the recent research development on addressing them, respectively.

Original languageEnglish (US)
Title of host publicationKDD 2020 - Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages3575-3576
Number of pages2
ISBN (Electronic)9781450379984
DOIs
StatePublished - Aug 23 2020
Event26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020 - Virtual, Online, United States
Duration: Aug 23 2020Aug 27 2020

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020
Country/TerritoryUnited States
CityVirtual, Online
Period8/23/208/27/20

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

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