# A Unifying Framework for Typical Multi-Task Multiple Kernel Learning Problems

arXiv: https://arxiv.org/abs/1401.5136 · HTML: https://mlsanity.com/paper/1401.5136/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2014.

## Built on by (most cited first, 1 of 1)

- [Universal Feature Selection for Simultaneous Interpretability of Multitask Datasets](https://mlsanity.com/paper/2403.14466/) (2024)

## Abstract (arXiv)

Over the past few years, Multi-Kernel Learning (MKL) has received significant attention among data-driven feature selection techniques in the context of kernel-based learning. MKL formulations have been devised and solved for a broad spectrum of machine learning problems, including Multi-Task Learning (MTL). Solving different MKL formulations usually involves designing algorithms that are tailored to the problem at hand, which is, typically, a non-trivial accomplishment. In this paper we present a general Multi-Task Multi-Kernel Learning (Multi-Task MKL) framework that subsumes well-known Multi-Task MKL formulations, as well as several important MKL approaches on single-task problems. We then derive a simple algorithm that can solve the unifying framework. To demonstrate the flexibility of the proposed framework, we formulate a new learning problem, namely Partially-Shared Common Space (PSCS) Multi-Task MKL, and demonstrate its merits through experimentation.