Multi-task learning via robust regularized clustering with non-convex group penalties

• URL: http://arxiv.org/abs/2404.03250v2
• Date: Mon, 27 May 2024 11:37:12 GMT
• Title: Multi-task learning via robust regularized clustering with non-convex group penalties
• Authors: Akira Okazaki, Shuichi Kawano,
• Abstract summary: Multi-task learning (MTL) aims to improve estimation performance by sharing common information among related tasks. Existing MTL methods based on this assumption often ignore outlier tasks. We propose a novel MTL method called MultiTask Regularized Clustering (MTLRRC)
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-task learning (MTL) aims to improve estimation and prediction performance by sharing common information among related tasks. One natural assumption in MTL is that tasks are classified into clusters based on their characteristics. However, existing MTL methods based on this assumption often ignore outlier tasks that have large task-specific components or no relation to other tasks. To address this issue, we propose a novel MTL method called Multi-Task Learning via Robust Regularized Clustering (MTLRRC). MTLRRC incorporates robust regularization terms inspired by robust convex clustering, which is further extended to handle non-convex and group-sparse penalties. The extension allows MTLRRC to simultaneously perform robust task clustering and outlier task detection. The connection between the extended robust clustering and the multivariate M-estimator is also established. This provides an interpretation of the robustness of MTLRRC against outlier tasks. An efficient algorithm based on a modified alternating direction method of multipliers is developed for the estimation of the parameters. The effectiveness of MTLRRC is demonstrated through simulation studies and application to real data.

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