Add to ORKGSimultaneously solving multiple related learning tasks is beneficial un-der a variety of circumstances, but the prior knowledge necessary to cor-rectly model task relationships is rarely available in practice. In this paper, we develop a novel kernel-based multi-task learning technique that automatically reveals structural inter-task relationships. Building over the framework of output kernel learning (OKL), we introduce a method that jointly learns multiple functions and a low-rank multi-task kernel by solving a non-convex regularization problem. Optimization is carried out via a block coordinate descent strategy, where each subproblem is solved using suitable conjugate gradient (CG) type iterative methods for linear operator equations. Th...
Regularization with matrix variables for multi-task learning Learning multiple tasks on a subspace ...
Several kernel based methods for multi-task learning have been proposed, which leverage relations am...
This paper proposes a novel algorithm, named Non-Convex Calibrated Multi-Task Learning (NC-CMTL), fo...
Editor: John Shawe-Taylor We study the problem of learning many related tasks simultaneously using k...
Several kernel-based methods for multi-task learning have been proposed, which leverage relations am...
Multi-task learning has received increasing attention in the past decade. Many supervised multi-task...
When faced with learning a set of inter-related tasks from a limited amount of usable data, learning...
The paradigm of multi-task learning is that one can achieve better generalization by learning tasks ...
Multi-task learning is a natural approach for computer vision applications that require the simultan...
Multi-task learning is a natural approach for computer vision applications that require the simultan...
Multi-task learning is a natural approach for computer vision applications that require the simultan...
Over the past few years, Multi-Kernel Learning (MKL) has received significant attention among data-d...
Several kernel based methods for multi-task learning have been proposed, which leverage relations am...
Multi-task learning is a natural approach for computer vision applications that require the simultan...
Multitask learning is a learning paradigm that seeks to improve the generalization performance of a ...
Regularization with matrix variables for multi-task learning Learning multiple tasks on a subspace ...
Several kernel based methods for multi-task learning have been proposed, which leverage relations am...
This paper proposes a novel algorithm, named Non-Convex Calibrated Multi-Task Learning (NC-CMTL), fo...
Editor: John Shawe-Taylor We study the problem of learning many related tasks simultaneously using k...
Several kernel-based methods for multi-task learning have been proposed, which leverage relations am...
Multi-task learning has received increasing attention in the past decade. Many supervised multi-task...
When faced with learning a set of inter-related tasks from a limited amount of usable data, learning...
The paradigm of multi-task learning is that one can achieve better generalization by learning tasks ...
Multi-task learning is a natural approach for computer vision applications that require the simultan...
Multi-task learning is a natural approach for computer vision applications that require the simultan...
Multi-task learning is a natural approach for computer vision applications that require the simultan...
Over the past few years, Multi-Kernel Learning (MKL) has received significant attention among data-d...
Several kernel based methods for multi-task learning have been proposed, which leverage relations am...
Multi-task learning is a natural approach for computer vision applications that require the simultan...
Multitask learning is a learning paradigm that seeks to improve the generalization performance of a ...
Regularization with matrix variables for multi-task learning Learning multiple tasks on a subspace ...
Several kernel based methods for multi-task learning have been proposed, which leverage relations am...
This paper proposes a novel algorithm, named Non-Convex Calibrated Multi-Task Learning (NC-CMTL), fo...