Jieping Ye

Alibaba Group (China)

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Zhiwei Qin

Yangtze University

Shuiwang Ji

Texas A&M University

Hongtu Zhu

University of North Carolina at Chapel Hill

Yalin Wang

Arizona State University

Paul M. Thompson

University of Southern California

Jiayu Zhou

Southwest Forestry University

Pinghua Gong

Kuaishou (China)

Sudhir Kumar

Temple University

Ravi Janardan

University of Minnesota

Sethuraman Panchanathan

U.S. National Science Foundation

Cooperative Institutions

Chinese Academy of Sciences

147

Tsinghua University

116

Arizona State University

112

Zhejiang University

107

Institute of Electrical and Electronics Engineers

University of Chinese Academy of Sciences

Signal Processing (United States)

Peking University

Didi Chuxing (China)

University of Science and Technology of China

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Adaptive diffusion kernel learning from biological networks for protein function prediction

BMC Bioinformatics (2008)

Liang Sun Shuiwang Ji Jieping Ye

Abstract Background Machine-learning tools have gained considerable attention during the last few years for analyzing biological networks for protein function prediction. Kernel methods are suitable for learning from graph-based data such as biological networks, as they only require the abstraction of the similarities between objects into the kernel matrix. One key issue in kernel methods is the selection of a good kernel function. Diffusion kernels, the discretization of the familiar Gaussian kernel of Euclidean space, are commonly used for graph-based data. Results In this paper, we address the issue of learning an optimal diffusion kernel, in the form of a convex combination of a set of pre-specified kernels constructed from biological networks, for protein function prediction. Most prior work on this kernel learning task focus on variants of the loss function based on Support Vector Machines (SVM). Their extensions to other loss functions such as the one based on Kullback-Leibler (KL) divergence, which is more suitable for mining biological networks, lead to expensive optimization problems. By exploiting the special structure of the diffusion kernel, we show that this KL divergence based kernel learning problem can be formulated as a simple optimization problem, which can then be solved efficiently. It is further extended to the multi-task case where we predict multiple functions of a protein simultaneously. We evaluate the efficiency and effectiveness of the proposed algorithms using two benchmark data sets. Conclusion Results show that the performance of linearly combined diffusion kernel is better than every single candidate diffusion kernel. When the number of tasks is large, the algorithms based on multiple tasks are favored due to their competitive recognition performance and small computational costs.

Kernel (algebra)

Graph kernel

String kernel

Tree kernel

Multiple kernel learning

10.1186/1471-2105-9-162

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Citations (15)

Geodesic Distance Function Learning via Heat Flow on Vector Fields

arXiv (Cornell University) (2014)

Binbin Lin Yang Ji Xiaofei He Jieping Ye

Learning a distance function or metric on a given data manifold is of great importance in machine learning and pattern recognition. Many of the previous works first embed the manifold to Euclidean space and then learn the distance function. However, such a scheme might not faithfully preserve the distance function if the original manifold is not Euclidean. Note that the distance function on a manifold can always be well-defined. In this paper, we propose to learn the distance function directly on the manifold without embedding. We first provide a theoretical characterization of the distance function by its gradient field. Based on our theoretical analysis, we propose to first learn the gradient field of the distance function and then learn the distance function itself. Specifically, we set the gradient field of a local distance function as an initial vector field. Then we transport it to the whole manifold via heat flow on vector fields. Finally, the geodesic distance function can be obtained by requiring its gradient field to be close to the normalized vector field. Experimental results on both synthetic and real data demonstrate the effectiveness of our proposed algorithm.

Manifold (fluid mechanics)

Distance transform

Signed distance function

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Citations (9)

Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction

arXiv (Cornell University) (2020)

Xuanzhao Wang Zhengping Che Bo Jiang Ning Xiao Ke Yang

Video anomaly detection is commonly used in many applications such as security surveillance and is very challenging.A majority of recent video anomaly detection approaches utilize deep reconstruction models, but their performance is often suboptimal because of insufficient reconstruction error differences between normal and abnormal video frames in practice. Meanwhile, frame prediction-based anomaly detection methods have shown promising performance. In this paper, we propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design which is more in line with the characteristics of surveillance videos. The proposed method is equipped with a multi-path ConvGRU-based frame prediction network that can better handle semantically informative objects and areas of different scales and capture spatial-temporal dependencies in normal videos. A noise tolerance loss is introduced during training to mitigate the interference caused by background noise. Extensive experiments have been conducted on the CUHK Avenue, ShanghaiTech Campus, and UCSD Pedestrian datasets, and the results show that our proposed method outperforms existing state-of-the-art approaches. Remarkably, our proposed method obtains the frame-level AUROC score of 88.3% on the CUHK Avenue dataset.

Anomaly (physics)

10.48550/arxiv.2011.02763

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On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game

International Conference on Machine Learning (2021)

Shuang Qiu Jieping Ye Zhaoran Wang Zhuoran Yang

To achieve sample efficiency in reinforcement learning (RL), it necessitates efficiently exploring the underlying environment. Under the offline setting, addressing the exploration challenge lies in collecting an offline dataset with sufficient coverage. Motivated by such a challenge, we study the reward-free RL problem, where an agent aims to thoroughly explore the environment without any pre-specified reward function. Then, given any extrinsic reward, the agent computes the policy via a planning algorithm with offline data collected in the exploration phase. Moreover, we tackle this problem under the context of function approximation, leveraging powerful function approximators. Specifically, we propose to explore via an optimistic variant of the value-iteration algorithm incorporating kernel and neural function approximations, where we adopt the associated exploration bonus as the exploration reward. Moreover, we design exploration and planning algorithms for both single-agent MDPs and zero-sum Markov games and prove that our methods can achieve $\widetilde{\mathcal{O}}(1 /\varepsilon^2)$ sample complexity for generating a $\varepsilon$-suboptimal policy or $\varepsilon$-approximate Nash equilibrium when given an arbitrary extrinsic reward. To the best of our knowledge, we establish the first provably efficient reward-free RL algorithm with kernel and neural function approximators.

Kernel (algebra)

Q-learning

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Citations (1)

On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game

arXiv (Cornell University) (2021)

Shuang Qiu Jieping Ye Zhaoran Wang Zhuoran Yang

Kernel (algebra)

Q-learning

10.48550/arxiv.2110.09771

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Citations (1)

A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications

arXiv (Cornell University) (2020)

Jie Gui Zhenan Sun Yonggang Wen Dacheng Tao Jieping Ye

Generative adversarial networks (GANs) are a hot research topic recently. GANs have been widely studied since 2014, and a large number of algorithms have been proposed. However, there is few comprehensive study explaining the connections among different GANs variants, and how they have evolved. In this paper, we attempt to provide a review on various GANs methods from the perspectives of algorithms, theory, and applications. Firstly, the motivations, mathematical representations, and structure of most GANs algorithms are introduced in details. Furthermore, GANs have been combined with other machine learning algorithms for specific applications, such as semi-supervised learning, transfer learning, and reinforcement learning. This paper compares the commonalities and differences of these GANs methods. Secondly, theoretical issues related to GANs are investigated. Thirdly, typical applications of GANs in image processing and computer vision, natural language processing, music, speech and audio, medical field, and data science are illustrated. Finally, the future open research problems for GANs are pointed out.

10.48550/arxiv.2001.06937

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Citations (159)

A Taxi Order Dispatch Model based On Combinatorial Optimization

Lingyu Zhang Tao Hu Min Yue Guobin Wu Junying Zhang

Taxi-booking apps have been very popular all over the world as they provide convenience such as fast response time to the users. The key component of a taxi-booking app is the dispatch system which aims to provide optimal matches between drivers and riders. Traditional dispatch systems sequentially dispatch taxis to riders and aim to maximize the driver acceptance rate for each individual order. However, the traditional systems may lead to a low global success rate, which degrades the rider experience when using the app. In this paper, we propose a novel system that attempts to optimally dispatch taxis to serve multiple bookings. The proposed system aims to maximize the global success rate, thus it optimizes the overall travel efficiency, leading to enhanced user experience. To further enhance users' experience, we also propose a method to predict destinations of a user once the taxi-booking APP is started. The proposed method employs the Bayesian framework to model the distribution of a user's destination based on his/her travel histories.

Taxis

Component (thermodynamics)

10.1145/3097983.3098138

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Citations (213)

Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer’s disease

Frontiers in Aging Neuroscience (2015)

Liang Zhan Jiayu Zhou Yalin Wang Yan Jin Neda Jahanshad

Alzheimer's disease (AD) involves a gradual breakdown of brain connectivity, and network analyses offer a promising new approach to track and understand disease progression. Even so, our ability to detect degenerative changes in brain networks depends on the methods used. Here we compared several tractography and feature extraction methods to see which ones gave best diagnostic classification for 202 people with AD, mild cognitive impairment or normal cognition, scanned with 41-gradient diffusion-weighted magnetic resonance imaging as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. We computed brain networks based on whole brain tractography with nine different methods - four of them tensor-based deterministic (FACT, RK2, SL, and TL), two orientation distribution function (ODF)-based deterministic (FACT, RK2), two ODF-based probabilistic approaches (Hough and PICo), and one "ball-and-stick" approach (Probtrackx). Brain networks derived from different tractography algorithms did not differ in terms of classification performance on ADNI, but performing principal components analysis on networks helped classification in some cases. Small differences may still be detectable in a truly vast cohort, but these experiments help assess the relative advantages of different tractography algorithms, and different post-processing choices, when used for classification.

10.3389/fnagi.2015.00048

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Citations (139)

Network Reconstruction Based on Evolutionary-Game Data via Compressive Sensing

Physical Review X (2011)

Wen-Xu Wang Ying-Cheng Lai Celso Grebogi Jieping Ye

Given a group of people who are possibly linked to each other socially, how does one find out who is actually linked to whom? Inspired by the compressive-sensing technique in signal processing, a team of researchers from China, U.S. and U.K. shows the way. Reconstructions of structures of gene-regulation networks or digital communication networks by this method may be the next step.

SIGNAL (programming language)

10.1103/physrevx.1.021021

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Citations (153)

Did You Enjoy the Ride? Understanding Passenger Experience via Heterogeneous Network Embedding

2022 IEEE 38th International Conference on Data Engineering (ICDE) (2018)

Carl Yang Chao Zhang Xuewen Chen Jieping Ye Jiawei Han

Online taxicab platforms like DiDi and Uber have impacted hundreds of millions of users on their choices of traveling, but how do users feel about the ride-sharing services, and how to improve their experience? While current ride-sharing services have collected massive travel data, it remains challenging to develop data-driven techniques for modeling and predicting user ride experience. In this work, we aim to accurately predict passenger satisfaction over their rides and understand the key factors that lead to good/bad experiences. Based on in-depth analysis of large-scale travel data from a popular taxicab platform in China, we develop PHINE (Pattern-aware Heterogeneous Information Network Embedding) for data-driven user experience modeling. Our PHINE framework is novel in that it is composed of spatial-temporal node binding and grouping for addressing the inherent data variation, and pattern preservation based joint training for modeling the interactions among drivers, passengers, locations, and time. Extensive experiments on 12 real-world travel datasets demonstrate the effectiveness of PHINE over strong baseline methods. We have deployed PHINE in the DiDi Big Data Center, delivering high-quality predictions for passenger satisfaction on a daily basis.

Baseline (sea)

10.1109/icde.2018.00158

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Citations (28)