Online Policy Adaptation for Ensemble Classifiers

Ensemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper we attempt to train and combine the base classifiers using an adaptive policy. This policy is learnt through a $Q$ -learning inspired technique. Its effectiveness for an essentially supervised task is demonstrated by experimental results on several UCI benchmark databases.

Online Policy Adaptation for Ensemble Classifiers

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Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Understanding generalization and robustness in modern deep learning

Topics in statistical physics of high-dimensional machine learning

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Understanding generalization and robustness in modern deep learning

Topics in statistical physics of high-dimensional machine learning