PROVABLY EFFICIENT FEDERATED ACTIVE MULTI-TASK REPRESENTATION LEARNING
Keywords: Representation learning, alternating gradient descent and minimization, active learning, multi-task learning
TL;DR: We develop a fast and sample- efficient approach for multi-task active learning when the amount of data from source tasks and target tasks is limite
Abstract: Multi-task learning is an emerging machine learning paradigm that integrates data from multiple sources, harnessing task similarities to enhance overall model performance. The application of multi-task learning to real-world settings is hindered due to data scarcity, along with challenges related to scalability and computational resources. To address this challenge, we develop a fast and sample-efficient approach for multi-task active learning when the amount of data from source tasks and target tasks is limited. By leveraging the techniques from active learning, we propose an adaptive sampling-based alternating projected gradient descent (GD) and minimization algorithm that iteratively estimates the relevance of each source task to the target task and samples from each source task based on the estimated relevance. We present the convergence guarantee of our algorithm and the sample complexity of our approach. We evaluated the effectiveness of our algorithm using numerical experiments and compared it empirically against four benchmark algorithms using synthetic and real datasets.
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Submission Number: 8199
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