Traditionally, multitask learning (MTL) assumes that all the tasks are related. This can lead to negative transfer when tasks are indeed incoherent. Recently, a number of approaches have been proposed that alleviate this problem by discovering the underlying task clusters or relationships. However, they are limited to modeling these relationships at the task level, which may be restrictive in some applications. In this paper, we propose a novel MTL formulation that captures task relationships at the feature-level. Depending on the interactions among tasks and features, the proposed method construct different task clusters for different features, without even the need of pre-specifying the number of clusters. Computationally, the proposed formulation is strongly convex, and can be efficiently solved by accelerated proximal methods. Experiments are performed on a number of synthetic and real-world data sets. Under various degrees of task relationships, the accuracy of the proposed method is consistently among the best. Moreover, the feature-specific task clusters obtained agree with the known/plausible task structures of the data.
Multitask Battery Management with Flexible Pretraining
IIIS, Tsinghua University（清华大学交叉信息研究院）；Shanghai Qi Zhi Institute（上海期智研究院）；State Key Laboratory of Intelligent Green Vehicle and Mobility, School of Vehicle and Mobility, Tsinghua University（清华大学智能绿色车辆与交通全国重点实验室、车辆与运载学院）；School of Advanced Manufacturing and Robotics, National Engineering Laboratory for Big Data Analysis and Applications, Peking University（北京大学先进制造与机器人学院、大数据分析与应用技术国家工程实验室）
Scalable predictive processing framework for multitask caregiving robots
National Institute of Neuroscience, National Center of Neurology and Psychiatry（国立精神·神经医疗研究中心 神经科学研究所）；Future Robotics Organization, Waseda University（早稻田大学未来机器人机构）；Department of Intermedia Art and Science, Waseda University（早稻田大学跨媒体艺术与科学系）
FlexCare: Leveraging Cross-Task Synergy for Flexible Multimodal Healthcare Prediction
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；Medical Big Data Research Center, Chinese PLA General Hospital（中国人民解放军总医院医学大数据中心）
A non-smooth regularization framework for learning over multitask graphs
Université Côte d’Azur, I3S Laboratory, CNRS, France（法国大学皮特里特大学I3S实验室，CNRS）；Machine Learning Genoa Center (MaLGa), Department of Computer Science, University of Genoa（热那亚大学计算机科学系机器学习中心（MaLGa））
On Parallel or Distributed Asynchronous Iterations with Unbounded Delays and Possible Out of Order Messages or Flexible Communication for Convex Optimization Problems and Machine Learning
LAAS-CNRS（LAAS-法国国家科学研究中心）；Université de Toulouse（图卢兹大学）；CNRS（法国国家科学研究中心）
