Workshop
Bandits, global optimization, active learning, and Bayesian reinforcement learning -- understanding the common ground
- Marc Toussaint (Stuttgart University, Germany)
Abstract
In active learning and related scenarios the learning agent actively selects which data point should be labeled next. That is, instead of learning passively from a previously selected data set, the learning agent is in control of the data collection itself, with the aim of collecting the data which is most informative to the model. This can greatly reduce the number of necessary data points. In this tutorial I will give a basic introduction to this area, including:
- What is the relation between bandits, optimization, active learning and Bayesian reinforcement learning?
- What would "optimal active learning'' be?
- What are typical and efficient heuristics (including regret bounds)?
- What are modern Monte-Carlo tree search approaches?
