Weishan Dong

IJCAI Conference 2017 Conference Paper

Autoencoder Regularized Network For Driving Style Representation Learning

• Weishan Dong
• Ting Yuan
• Kai Yang
• Changsheng Li
• Shilei Zhang

In this paper, we study learning generalized driving style representations from automobile GPS trip data. We propose a novel Autoencoder Regularized deep neural Network (ARNet) and a trip encoding framework trip2vec to learn drivers' driving styles directly from GPS records, by combining supervised and unsupervised feature learning in a unified architecture. Experiments on a challenging driver number estimation problem and the driver identification problem show that ARNet can learn a good generalized driving style representation: It significantly outperforms existing methods and alternative architectures by reaching the least estimation error on average (0. 68, less than one driver) and the highest identification accuracy (by at least 3% improvement) compared with traditional supervised learning methods.

AAAI Conference 2017 Conference Paper

Self-Paced Multi-Task Learning

• Changsheng Li
• Junchi Yan
• Fan Wei
• Weishan Dong
• Qingshan Liu
• Hongyuan Zha

Multi-task learning is a paradigm, where multiple tasks are jointly learnt. Previous multi-task learning models usually treat all tasks and instances per task equally during learning. Inspired by the fact that humans often learn from easy concepts to hard ones in the cognitive process, in this paper, we propose a novel multi-task learning framework that attempts to learn the tasks by simultaneously taking into consideration the complexities of both tasks and instances per task. We propose a novel formulation by presenting a new task-oriented regularizer that can jointly prioritize tasks and instances. Thus it can be interpreted as a self-paced learner for multi-task learning. An efficient block coordinate descent algorithm is developed to solve the proposed objective function, and the convergence of the algorithm can be guaranteed. Experimental results on the toy and real-world datasets demonstrate the effectiveness of the proposed approach, compared to the state-of-the-arts.

AAAI Conference 2016 Conference Paper

Spatially Regularized Streaming Sensor Selection

• Changsheng Li
• Fan Wei
• Weishan Dong
• Xiangfeng Wang
• Junchi Yan
• Xiaobin Zhu
• Qingshan Liu
• Xin Zhang

IJCAI Conference 2013 Conference Paper

Towards Effective Prioritizing Water Pipe Replacement and Rehabilitation

• Junchi Yan
• Yu Wang
• Ke Zhou
• Jin Huang
• Chunhua Tian
• Hongyuan Zha
• Weishan Dong

Water pipe failures can not only have a great impact on people’s daily life but also cause significant waste of water which is an essential and precious resource to human beings. As a result, preventative maintenance for water pipes, particularly in urbanscale networks, is of great importance for a sustainable society. To achieve effective replacement and rehabilitation, failure prediction aims to proactively find those ‘most-likely-to-fail’ pipes becomes vital and has been attracting more attention from both academia and industry, especially from the civil engineering field. This paper presents an alreadydeployed industrial computational system for pipe failure prediction. As an alternative to risk matrix methods often depending on ad-hoc domain heuristics, learning based methods are adopted using the attributes with respect to physical, environmental, operational conditions and etc. Further challenge arises in practice when lacking of profile attributes. A dive into the failure records shows that the failure event sequences typically exhibit temporal clustering patterns, which motivates us to use the stochastic process to tackle the failure prediction task. Specifically, the failure sequence is formulated as a self-exciting stochastic process which is, to our best knowledge, a novel formulation for pipe failure prediction. And we show that it outperforms a baseline assuming the failure risk grows linearly with aging. Broad new problems and research points for the machine learning community are also introduced for future work.