Finding the seed set that maximizes the influence spread over a network is a well-known NP-hard problem.
Online display advertising is growing rapidly in recent years thanks to the automation of the ad buying process.
We introduce Bayesian least-squares policy iteration (BLSPI), an off-policy, model-free, policy iteration algorithm that uses the Bayesian least-squares temporal-difference (BLSTD) learning algorithm to evaluate policies.
In text classification, the problem of overfitting arises due to the high dimensionality, making regularization essential.
This has the advantage to establish an informative feature space and modify the task of game playing to a regression analysis problem.
This paper proposes an online tree-based Bayesian approach for reinforcement learning.
This paper introduces a simple, general framework for likelihood-free Bayesian reinforcement learning, through Approximate Bayesian Computation (ABC).