The Multi-Armed Bandit: Group ABA Training Meets Deep Learning


The Multi-Armed Bandit: Group ABA Training Meets Deep Learning – We propose a joint Bayesian learning framework for the task of multi-armed bandit learning. Our framework uses the assumption that the agents are in close proximity towards one another, i.e. towards a set of bandits, that allows the agents to learn more accurately and effectively. More importantly the framework is efficient over the time dimension, which is a key problem that is addressed in the study of multi-armed bandit learning. The purpose of this article is to address this issue using a novel algorithm that allows to learn from observed behaviour in situations where the agents interact with each other and the environment in which they will be participating. This approach is shown to outperform the multi-armed bandit methods, which have been developed for the task of multi-armed bandit learning, with state-of-the-art machine learning and AI-assisted approaches.

In this work, we investigate the problem of learning an optimal policy if the optimal policy is given by a good policy, or a poor policy. Our main ideas are: 1) We use a regularizer to model the nonconvex norm, and 2) We use a probabilistic optimization to optimize a Gaussian density function to estimate the optimal nonconvex policy. We show that our policy approximation algorithms outperform many state-of-the-art policy estimates in terms of performance and scalability, and that we can obtain a high-dimensional policy that performs well in practice. Our method is more robust to outliers that are present in the data, and can be extended to handle large graphs. We experimentally show that our method is very efficient in several settings (optimal policy, low-hanging fruit, and nonconvex policy), and show that it performs well under both settings, even in real-data scenarios.

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The Multi-Armed Bandit: Group ABA Training Meets Deep Learning

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  • A Hierarchical Clustering Model for Knowledge Base Completion

    A Randomized Nonparametric Bayes Method for Optimal Bayesian RankingIn this work, we investigate the problem of learning an optimal policy if the optimal policy is given by a good policy, or a poor policy. Our main ideas are: 1) We use a regularizer to model the nonconvex norm, and 2) We use a probabilistic optimization to optimize a Gaussian density function to estimate the optimal nonconvex policy. We show that our policy approximation algorithms outperform many state-of-the-art policy estimates in terms of performance and scalability, and that we can obtain a high-dimensional policy that performs well in practice. Our method is more robust to outliers that are present in the data, and can be extended to handle large graphs. We experimentally show that our method is very efficient in several settings (optimal policy, low-hanging fruit, and nonconvex policy), and show that it performs well under both settings, even in real-data scenarios.


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