Search Results for author: Jieping Ye

Found 122 papers, 21 papers with code

Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Bias

no code implementations26 Sep 2023 Zhihao Shi, Jie Wang, Fanghua Lu, Hanzhu Chen, Defu Lian, Zheng Wang, Jieping Ye, Feng Wu

The inverse mapping leads to an objective function that is equivalent to that by the joint training, while it can effectively incorporate GNNs in the training phase of NEs against the learning bias.

Representation Learning

A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects

no code implementations3 Sep 2023 Haomin Wen, Youfang Lin, Lixia Wu, Xiaowei Mao, Tianyue Cai, Yunfeng Hou, Shengnan Guo, Yuxuan Liang, Guangyin Jin, Yiji Zhao, Roger Zimmermann, Jieping Ye, Huaiyu Wan

An emerging research area within these services is service Route\&Time Prediction (RTP), which aims to estimate the future service route as well as the arrival time of a given worker.

Exploring the Application of Large-scale Pre-trained Models on Adverse Weather Removal

no code implementations15 Jun 2023 Zhentao Tan, Yue Wu, Qiankun Liu, Qi Chu, Le Lu, Jieping Ye, Nenghai Yu

Inspired by the various successful applications of large-scale pre-trained models (e. g, CLIP), in this paper, we explore the potential benefits of them for this task through both spatial feature representation learning and semantic information embedding aspects: 1) for spatial feature representation learning, we design a Spatially-Adaptive Residual (\textbf{SAR}) Encoder to extract degraded areas adaptively.

Image Restoration Representation Learning

Ultra-High Resolution Segmentation with Ultra-Rich Context: A Novel Benchmark

1 code implementation CVPR 2023 Deyi Ji, Feng Zhao, Hongtao Lu, Mingyuan Tao, Jieping Ye

With the increasing interest and rapid development of methods for Ultra-High Resolution (UHR) segmentation, a large-scale benchmark covering a wide range of scenes with full fine-grained dense annotations is urgently needed to facilitate the field.

Land Cover Classification Semantic Segmentation

Self-Learning Symmetric Multi-view Probabilistic Clustering

no code implementations12 May 2023 Junjie Liu, Junlong Liu, Rongxin Jiang, Yaowu Chen, Chen Shen, Jieping Ye

Then, SLS-MPC proposes a novel self-learning probability function without any prior knowledge and hyper-parameters to learn each view's individual distribution.

Clustering Incomplete multi-view clustering +1

Sim2Rec: A Simulator-based Decision-making Approach to Optimize Real-World Long-term User Engagement in Sequential Recommender Systems

1 code implementation3 May 2023 Xiong-Hui Chen, Bowei He, Yang Yu, Qingyang Li, Zhiwei Qin, Wenjie Shang, Jieping Ye, Chen Ma

However, building a user simulator with no reality-gap, i. e., can predict user's feedback exactly, is unrealistic because the users' reaction patterns are complex and historical logs for each user are limited, which might mislead the simulator-based recommendation policy.

Decision Making Recommendation Systems +1

Semi-Supervised 2D Human Pose Estimation Driven by Position Inconsistency Pseudo Label Correction Module

1 code implementation CVPR 2023 Linzhi Huang, Yulong Li, Hongbo Tian, Yue Yang, Xiangang Li, Weihong Deng, Jieping Ye

The previous method ignored two problems: (i) When conducting interactive training between large model and lightweight model, the pseudo label of lightweight model will be used to guide large models.

2D Human Pose Estimation Pose Estimation +1

Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions

no code implementations25 Jul 2022 Shuang Qiu, Xiaohan Wei, Jieping Ye, Zhaoran Wang, Zhuoran Yang

Our algorithms feature a combination of Upper Confidence Bound (UCB)-type optimism and fictitious play under the scope of simultaneous policy optimization in a non-stationary environment.

ResNorm: Tackling Long-tailed Degree Distribution Issue in Graph Neural Networks via Normalization

no code implementations16 Jun 2022 Langzhang Liang, Zenglin Xu, Zixing Song, Irwin King, Jieping Ye

In detail, by studying the long-tailed distribution of node degrees in the graph, we propose a novel normalization method for GNNs, which is termed ResNorm (\textbf{Res}haping the long-tailed distribution into a normal-like distribution via \textbf{norm}alization).

Node Classification

Stochastic Gradient Descent without Full Data Shuffle

1 code implementation12 Jun 2022 Lijie Xu, Shuang Qiu, Binhang Yuan, Jiawei Jiang, Cedric Renggli, Shaoduo Gan, Kaan Kara, Guoliang Li, Ji Liu, Wentao Wu, Jieping Ye, Ce Zhang

In this paper, we first conduct a systematic empirical study on existing data shuffling strategies, which reveals that all existing strategies have room for improvement -- they all suffer in terms of I/O performance or convergence rate.

Policy Evaluation for Temporal and/or Spatial Dependent Experiments in Ride-sourcing Platforms

no code implementations22 Feb 2022 Shikai Luo, Ying Yang, Chengchun Shi, Fang Yao, Jieping Ye, Hongtu Zhu

The aim of this paper is to establish causal relationship between ride-sharing platform's policies and outcomes of interest under complex temporal and/or spatial dependent experiments.


Rethinking Graph Convolutional Networks in Knowledge Graph Completion

2 code implementations8 Feb 2022 Zhanqiu Zhang, Jie Wang, Jieping Ye, Feng Wu

Surprisingly, we observe from experiments that the graph structure modeling in GCNs does not have a significant impact on the performance of KGC models, which is in contrast to the common belief.

Entity Embeddings Knowledge Graph Completion +1

Offline Model-based Adaptable Policy Learning

1 code implementation NeurIPS 2021 Xiong-Hui Chen, Yang Yu, Qingyang Li, Fan-Ming Luo, Zhiwei Qin, Wenjie Shang, Jieping Ye

Current offline reinforcement learning methods commonly learn in the policy space constrained to in-support regions by the offline dataset, in order to ensure the robustness of the outcome policies.

Decision Making reinforcement-learning +1

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

no code implementations19 Oct 2021 Shuang Qiu, Jieping Ye, Zhaoran Wang, Zhuoran Yang

Then, given any extrinsic reward, the agent computes the policy via a planning algorithm with offline data collected in the exploration phase.

Reinforcement Learning (RL)

A Deep Value-network Based Approach for Multi-Driver Order Dispatching

no code implementations8 Jun 2021 Xiaocheng Tang, Zhiwei Qin, Fan Zhang, Zhaodong Wang, Zhe Xu, Yintai Ma, Hongtu Zhu, Jieping Ye

In this work, we propose a deep reinforcement learning based solution for order dispatching and we conduct large scale online A/B tests on DiDi's ride-dispatching platform to show that the proposed method achieves significant improvement on both total driver income and user experience related metrics.

reinforcement-learning Reinforcement Learning (RL) +1

Reinforcement Learning for Ridesharing: An Extended Survey

no code implementations3 May 2021 Zhiwei Qin, Hongtu Zhu, Jieping Ye

In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement learning approaches to decision optimization problems in a typical ridesharing system.

reinforcement-learning Reinforcement Learning (RL)

Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning

no code implementations8 Mar 2021 Yan Jiao, Xiaocheng Tang, Zhiwei Qin, Shuaiji Li, Fan Zhang, Hongtu Zhu, Jieping Ye

We present a new practical framework based on deep reinforcement learning and decision-time planning for real-world vehicle repositioning on ride-hailing (a type of mobility-on-demand, MoD) platforms.

reinforcement-learning Reinforcement Learning (RL)

Graph-Based Equilibrium Metrics for Dynamic Supply-Demand Systems with Applications to Ride-sourcing Platforms

1 code implementation11 Feb 2021 Fan Zhou, Shikai Luo, XiaoHu Qie, Jieping Ye, Hongtu Zhu

How to dynamically measure the local-to-global spatio-temporal coherence between demand and supply networks is a fundamental task for ride-sourcing platforms, such as DiDi.

Optimization and Control Applications


no code implementations1 Jan 2021 Chengchun Shi, Xiaoyu Wang, Shikai Luo, Rui Song, Hongtu Zhu, Jieping Ye

A/B testing, or online experiment is a standard business strategy to compare a new product with an old one in pharmaceutical, technological, and traditional industries.

Reinforcement Learning (RL)

Offline Adaptive Policy Leaning in Real-World Sequential Recommendation Systems

no code implementations1 Jan 2021 Xiong-Hui Chen, Yang Yu, Qingyang Li, Zhiwei Tony Qin, Wenjie Shang, Yiping Meng, Jieping Ye

Instead of increasing the fidelity of models for policy learning, we handle the distortion issue via learning to adapt to diverse simulators generated by the offline dataset.

Sequential Recommendation

Selective Pseudo-Labeling with Reinforcement Learning for Semi-Supervised Domain Adaptation

no code implementations7 Dec 2020 Bingyu Liu, Yuhong Guo, Jieping Ye, Weihong Deng

Inspired by the effectiveness of pseudo-labels in domain adaptation, we propose a reinforcement learning based selective pseudo-labeling method for semi-supervised domain adaptation.

Q-Learning reinforcement-learning +3

Domain Adaptation with Incomplete Target Domains

no code implementations3 Dec 2020 Zhenpeng Li, Jianan Jiang, Yuhong Guo, Tiantian Tang, Chengxiang Zhuo, Jieping Ye

In the proposed model, we design a data imputation module to fill the missing feature values based on the partial observations in the target domain, while aligning the two domains via deep adversarial adaption.

Domain Adaptation Imputation

Joint predictions of multi-modal ride-hailing demands: a deep multi-task multigraph learning-based approach

no code implementations11 Nov 2020 Jintao Ke, Siyuan Feng, Zheng Zhu, Hai Yang, Jieping Ye

To address this issue, we propose a deep multi-task multi-graph learning approach, which combines two components: (1) multiple multi-graph convolutional (MGC) networks for predicting demands for different service modes, and (2) multi-task learning modules that enable knowledge sharing across multiple MGC networks.

Graph Learning Multi-Task Learning

Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction

no code implementations5 Nov 2020 Xuanzhao Wang, Zhengping Che, Bo Jiang, Ning Xiao, Ke Yang, Jian Tang, Jieping Ye, Jingyu Wang, Qi Qi

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.

Anomaly Detection Video Anomaly Detection

Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control

1 code implementation NeurIPS 2020 Zhiyuan Xu, Kun Wu, Zhengping Che, Jian Tang, Jieping Ye

While Deep Reinforcement Learning (DRL) has emerged as a promising approach to many complex tasks, it remains challenging to train a single DRL agent that is capable of undertaking multiple different continuous control tasks.

Continuous Control reinforcement-learning +2

A Survey on Machine Learning from Few Samples

no code implementations6 Sep 2020 Jiang Lu, Pinghua Gong, Jieping Ye, Jianwei Zhang, ChangShui Zhang

The capability of learning and generalizing from very few samples successfully is a noticeable demarcation separating artificial intelligence and human intelligence since humans can readily establish their cognition to novelty from just a single or a handful of examples whereas machine learning algorithms typically entail hundreds or thousands of supervised samples to guarantee generalization ability.

BIG-bench Machine Learning Meta-Learning

Single-Timescale Stochastic Nonconvex-Concave Optimization for Smooth Nonlinear TD Learning

no code implementations23 Aug 2020 Shuang Qiu, Zhuoran Yang, Xiaohan Wei, Jieping Ye, Zhaoran Wang

Existing approaches for this problem are based on two-timescale or double-loop stochastic gradient algorithms, which may also require sampling large-batch data.

Predicting Individual Treatment Effects of Large-scale Team Competitions in a Ride-sharing Economy

no code implementations7 Aug 2020 Teng Ye, Wei Ai, Lingyu Zhang, Ning Luo, Lulu Zhang, Jieping Ye, Qiaozhu Mei

Through interpreting the best-performing models, we discover many novel and actionable insights regarding how to optimize the design and the execution of team competitions on ride-sharing platforms.

Road Network Metric Learning for Estimated Time of Arrival

no code implementations24 Jun 2020 Yiwen Sun, Kun fu, Zheng Wang, Chang-Shui Zhang, Jieping Ye

To address the data sparsity problem, we propose the Road Network Metric Learning framework for ETA (RNML-ETA).

Metric Learning

Ensemble Model with Batch Spectral Regularization and Data Blending for Cross-Domain Few-Shot Learning with Unlabeled Data

no code implementations8 Jun 2020 Zhen Zhao, Bingyu Liu, Yuhong Guo, Jieping Ye

In this paper, we present our proposed ensemble model with batch spectral regularization and data blending mechanisms for the Track 2 problem of the cross-domain few-shot learning (CD-FSL) challenge.

cross-domain few-shot learning

A Transductive Multi-Head Model for Cross-Domain Few-Shot Learning

1 code implementation8 Jun 2020 Jianan Jiang, Zhenpeng Li, Yuhong Guo, Jieping Ye

The TMHFS method extends the Meta-Confidence Transduction (MCT) and Dense Feature-Matching Networks (DFMN) method [2] by introducing a new prediction head, i. e, an instance-wise global classification network based on semantic information, after the common feature embedding network.

cross-domain few-shot learning Data Augmentation

Fusion Recurrent Neural Network

no code implementations7 Jun 2020 Yiwen Sun, Yulu Wang, Kun fu, Zheng Wang, Chang-Shui Zhang, Jieping Ye

Furthermore, in order to evaluate Fusion RNN's sequence feature extraction capability, we choose a representative data mining task for sequence data, estimated time of arrival (ETA) and present a novel model based on Fusion RNN.

FMA-ETA: Estimating Travel Time Entirely Based on FFN With Attention

no code implementations7 Jun 2020 Yiwen Sun, Yulu Wang, Kun fu, Zheng Wang, Ziang Yan, Chang-Shui Zhang, Jieping Ye

Estimated time of arrival (ETA) is one of the most important services in intelligent transportation systems and becomes a challenging spatial-temporal (ST) data mining task in recent years.

Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification

no code implementations18 May 2020 Bingyu Liu, Zhen Zhao, Zhenpeng Li, Jianan Jiang, Yuhong Guo, Jieping Ye

In this paper, we propose a feature transformation ensemble model with batch spectral regularization for the Cross-domain few-shot learning (CD-FSL) challenge.

cross-domain few-shot learning Data Augmentation +2

Sequential Sentence Matching Network for Multi-turn Response Selection in Retrieval-based Chatbots

no code implementations16 May 2020 Chao Xiong, Che Liu, Zijun Xu, Junfeng Jiang, Jieping Ye

In this work, we propose a matching network, called sequential sentence matching network (S2M), to use the sentence-level semantic information to address the problem.

Retrieval Word Similarity

Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting

no code implementations23 Apr 2020 Yiwen Sun, Yulu Wang, Kun fu, Zheng Wang, Chang-Shui Zhang, Jieping Ye

Recently, deep learning based methods have achieved promising results by adopting graph convolutional network (GCN) to extract the spatial correlations and recurrent neural network (RNN) to capture the temporal dependencies.

Hierarchical Adaptive Contextual Bandits for Resource Constraint based Recommendation

1 code implementation2 Apr 2020 Mengyue Yang, Qingyang Li, Zhiwei Qin, Jieping Ye

In this paper, we propose a hierarchical adaptive contextual bandit method (HATCH) to conduct the policy learning of contextual bandits with a budget constraint.

Multi-Armed Bandits

Mutual Learning Network for Multi-Source Domain Adaptation

no code implementations29 Mar 2020 Zhenpeng Li, Zhen Zhao, Yuhong Guo, Haifeng Shen, Jieping Ye

However, in practice the labeled data can come from multiple source domains with different distributions.

Unsupervised Domain Adaptation

Adaptive Object Detection with Dual Multi-Label Prediction

no code implementations ECCV 2020 Zhen Zhao, Yuhong Guo, Haifeng Shen, Jieping Ye

In this paper, we propose a novel end-to-end unsupervised deep domain adaptation model for adaptive object detection by exploiting multi-label object recognition as a dual auxiliary task.

Image-to-Image Translation object-detection +3

Augmented Parallel-Pyramid Net for Attention Guided Pose-Estimation

no code implementations17 Mar 2020 Luanxuan Hou, Jie Cao, Yuan Zhao, Haifeng Shen, Yiping Meng, Ran He, Jieping Ye

At last, we proposed a differentiable auto data augmentation method to further improve estimation accuracy.

Data Augmentation Pose Estimation

Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss

no code implementations NeurIPS 2020 Shuang Qiu, Xiaohan Wei, Zhuoran Yang, Jieping Ye, Zhaoran Wang

In particular, we prove that the proposed algorithm achieves $\widetilde{\mathcal{O}}(L|\mathcal{S}|\sqrt{|\mathcal{A}|T})$ upper bounds of both the regret and the constraint violation, where $L$ is the length of each episode.

reinforcement-learning Reinforcement Learning (RL)

Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework

1 code implementation5 Feb 2020 Chengchun Shi, Xiaoyu Wang, Shikai Luo, Hongtu Zhu, Jieping Ye, Rui Song

A/B testing, or online experiment is a standard business strategy to compare a new product with an old one in pharmaceutical, technological, and traditional industries.

reinforcement-learning Reinforcement Learning (RL)

An Attention-based Graph Neural Network for Heterogeneous Structural Learning

1 code implementation19 Dec 2019 Huiting Hong, Hantao Guo, Yu-Cheng Lin, Xiaoqing Yang, Zang Li, Jieping Ye

In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations.

Graph Embedding Graph Representation Learning +2

Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem

no code implementations25 Nov 2019 John Holler, Risto Vuorio, Zhiwei Qin, Xiaocheng Tang, Yan Jiao, Tiancheng Jin, Satinder Singh, Chenxi Wang, Jieping Ye

Order dispatching and driver repositioning (also known as fleet management) in the face of spatially and temporally varying supply and demand are central to a ride-sharing platform marketplace.

BIG-bench Machine Learning Decision Making +3

Building Effective Large-Scale Traffic State Prediction System: Traffic4cast Challenge Solution

1 code implementation11 Nov 2019 Yang Liu, Fanyou Wu, Baosheng Yu, Zhiyuan Liu, Jieping Ye

How to build an effective large-scale traffic state prediction system is a challenging but highly valuable problem.

Time Series Time Series Prediction

Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network

1 code implementation17 Oct 2019 Jintao Ke, Xiaoran Qin, Hai Yang, Zhengfei Zheng, Zheng Zhu, Jieping Ye

To overcome this challenge, we propose the Spatio-Temporal Encoder-Decoder Residual Multi-Graph Convolutional network (ST-ED-RMGC), a novel deep learning model for predicting ride-sourcing demand of various OD pairs.


Multi-grained Attention Networks for Single Image Super-Resolution

no code implementations26 Sep 2019 Huapeng Wu, Zhengxia Zou, Jie Gui, Wen-Jun Zeng, Jieping Ye, Jun Zhang, Hongyi Liu, Zhihui Wei

In this paper, we make a thorough investigation on the attention mechanisms in a SR model and shed light on how simple and effective improvements on these ideas improve the state-of-the-arts.

Feature Importance Image Super-Resolution

CGT: Clustered Graph Transformer for Urban Spatio-temporal Prediction

no code implementations25 Sep 2019 Xu Geng, Lingyu Zhang, Shulin Li, Yuanbo Zhang, Lulu Zhang, Leye Wang, Qiang Yang, Hongtu Zhu, Jieping Ye

Deep learning based approaches have been widely used in various urban spatio-temporal forecasting problems, but most of them fail to account for the unsmoothness issue of urban data in their architecture design, which significantly deteriorates their prediction performance.

Graph Attention Spatio-Temporal Forecasting +2

AHINE: Adaptive Heterogeneous Information Network Embedding

no code implementations20 Aug 2019 Yu-Cheng Lin, Xiaoqing Yang, Zang Li, Jieping Ye

In this paper, we propose two novel algorithms, GHINE (General Heterogeneous Information Network Embedding) and AHINE (Adaptive Heterogeneous Information Network Embedding), to compute distributed representations for elements in heterogeneous networks.

Link Prediction Network Embedding +1

Reward Advancement: Transforming Policy under Maximum Causal Entropy Principle

no code implementations11 Jul 2019 Guojun Wu, Yanhua Li, Zhenming Liu, Jie Bao, Yu Zheng, Jieping Ye, Jun Luo

In this paper, we define and investigate a general reward trans-formation problem (namely, reward advancement): Recovering the range of additional reward functions that transform the agent's policy from original policy to a predefined target policy under MCE principle.

Decision Making

AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates

no code implementations6 Jul 2019 Ning Liu, Xiaolong Ma, Zhiyuan Xu, Yanzhi Wang, Jian Tang, Jieping Ye

This work proposes AutoCompress, an automatic structured pruning framework with the following key performance improvements: (i) effectively incorporate the combination of structured pruning schemes in the automatic process; (ii) adopt the state-of-art ADMM-based structured weight pruning as the core algorithm, and propose an innovative additional purification step for further weight reduction without accuracy loss; and (iii) develop effective heuristic search method enhanced by experience-based guided search, replacing the prior deep reinforcement learning technique which has underlying incompatibility with the target pruning problem.

Model Compression

CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms

no code implementations27 May 2019 Jiarui Jin, Ming Zhou, Wei-Nan Zhang, Minne Li, Zilong Guo, Zhiwei Qin, Yan Jiao, Xiaocheng Tang, Chenxi Wang, Jun Wang, Guobin Wu, Jieping Ye

How to optimally dispatch orders to vehicles and how to trade off between immediate and future returns are fundamental questions for a typical ride-hailing platform.

Multiagent Systems

Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting

no code implementations27 May 2019 Xu Geng, Xiyu Wu, Lingyu Zhang, Qiang Yang, Yan Liu, Jieping Ye

To incorporate multiple relationships into spatial feature extraction, we define the problem as a multi-modal machine learning problem on multi-graph convolution networks.

BIG-bench Machine Learning

Optimal Passenger-Seeking Policies on E-hailing Platforms Using Markov Decision Process and Imitation Learning

no code implementations23 May 2019 Zhenyu Shou, Xuan Di, Jieping Ye, Hongtu Zhu, Hua Zhang, Robert Hampshire

Vacant taxi drivers' passenger seeking process in a road network generates additional vehicle miles traveled, adding congestion and pollution into the road network and the environment.

Imitation Learning

Object Detection in 20 Years: A Survey

1 code implementation13 May 2019 Zhengxia Zou, Keyan Chen, Zhenwei Shi, Yuhong Guo, Jieping Ye

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years.

Face Detection object-detection +2

D$^2$-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios

no code implementations3 Apr 2019 Zhengping Che, Guangyu Li, Tracy Li, Bo Jiang, Xuefeng Shi, Xinsheng Zhang, Ying Lu, Guobin Wu, Yan Liu, Jieping Ye

Driving datasets accelerate the development of intelligent driving and related computer vision technologies, while substantial and detailed annotations serve as fuels and powers to boost the efficacy of such datasets to improve learning-based models.

POI Semantic Model with a Deep Convolutional Structure

no code implementations18 Mar 2019 Ji Zhao, Meiyu Yu, Huan Chen, Boning Li, Lingyu Zhang, Qi Song, Li Ma, Hua Chai, Jieping Ye

An accurate similarity calculation is challenging since the mismatch between a query and a retrieval text may exist in the case of a mistyped query or an alias inquiry.


Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining

no code implementations11 Nov 2018 Ishan Jindal, Zhiwei Qin, Xue-wen Chen, Matthew Nokleby, Jieping Ye

In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy for carpooling that maximizes transportation efficiency so that fewer cars are required to fulfill the given amount of trip demand.

reinforcement-learning Reinforcement Learning (RL)

P^2IR: Universal Deep Node Representation via Partial Permutation Invariant Set Functions

no code implementations27 Sep 2018 Shupeng Gui, Xiangliang Zhang, Shuang Qiu, Mingrui Wu, Jieping Ye, Ji Liu

Our method can 1) learn an arbitrary form of the representation function from the neighborhood, without losing any potential dependence structures, 2) automatically decide the significance of neighbors at different distances, and 3) be applicable to both homogeneous and heterogeneous graph embedding, which may contain multiple types of nodes.

Graph Embedding Representation Learning

GESF: A Universal Discriminative Mapping Mechanism for Graph Representation Learning

no code implementations28 May 2018 Shupeng Gui, Xiangliang Zhang, Shuang Qiu, Mingrui Wu, Jieping Ye, Ji Liu

Graph embedding is a central problem in social network analysis and many other applications, aiming to learn the vector representation for each node.

Graph Embedding Graph Representation Learning

Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction

1 code implementation23 Feb 2018 Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, Zhenhui Li

Traditional demand prediction methods mostly rely on time series forecasting techniques, which fail to model the complex non-linear spatial and temporal relations.

Image Classification Time Series Forecasting +1

A Unified Neural Network Approach for Estimating Travel Time and Distance for a Taxi Trip

no code implementations12 Oct 2017 Ishan Jindal, Tony, Qin, Xue-wen Chen, Matthew Nokleby, Jieping Ye

In building intelligent transportation systems such as taxi or rideshare services, accurate prediction of travel time and distance is crucial for customer experience and resource management.

Feature Engineering Management

Identifying Genetic Risk Factors via Sparse Group Lasso with Group Graph Structure

no code implementations12 Sep 2017 Tao Yang, Paul Thompson, Sihai Zhao, Jieping Ye

As a regression model, it is competitive to the state-of-the-arts sparse models; as a variable selection method, SGLGG is promising for identifying Alzheimer's disease-related risk SNPs.

Variable Selection

Multi-task Dictionary Learning based Convolutional Neural Network for Computer aided Diagnosis with Longitudinal Images

no code implementations31 Aug 2017 Jie Zhang, Qingyang Li, Richard J. Caselli, Jieping Ye, Yalin Wang

Firstly, we pre-train CNN on the ImageNet dataset and transfer the knowledge from the pre-trained model to the medical imaging progression representation, generating the features for different tasks.

Dictionary Learning Image Classification +1

Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis

no code implementations19 Jul 2017 Xiang Li, Aoxiao Zhong, Ming Lin, Ning Guo, Mu Sun, Arkadiusz Sitek, Jieping Ye, James Thrall, Quanzheng Li

However, the development of a robust and reliable deep learning model for computer-aided diagnosis is still highly challenging due to the combination of the high heterogeneity in the medical images and the relative lack of training samples.

Computed Tomography (CT) Lesion Detection

Coupled Support Vector Machines for Supervised Domain Adaptation

no code implementations22 Jun 2017 Hemanth Venkateswara, Prasanth Lade, Jieping Ye, Sethuraman Panchanathan

Popular domain adaptation (DA) techniques learn a classifier for the target domain by sampling relevant data points from the source and combining it with the target data.

Domain Adaptation

Nonconvex One-bit Single-label Multi-label Learning

no code implementations17 Mar 2017 Shuang Qiu, Tingjin Luo, Jieping Ye, Ming Lin

We study an extreme scenario in multi-label learning where each training instance is endowed with a single one-bit label out of multiple labels.

Multi-Label Learning

The Second Order Linear Model

no code implementations2 Mar 2017 Ming Lin, Shuang Qiu, Bin Hong, Jieping Ye

We show that the conventional gradient descent heuristic is biased by the skewness of the distribution therefore is no longer the best practice of learning the SLM.

Open-Ended Question Answering

A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing

no code implementations NeurIPS 2016 Ming Lin, Jieping Ye

We develop an efficient alternating framework for learning a generalized version of Factorization Machine (gFM) on steaming data with provable guarantees.

Matrix Completion Retrieval

Large-scale Collaborative Imaging Genetics Studies of Risk Genetic Factors for Alzheimer's Disease Across Multiple Institutions

no code implementations19 Aug 2016 Qingyang Li, Tao Yang, Liang Zhan, Derrek Paul Hibar, Neda Jahanshad, Yalin Wang, Jieping Ye, Paul M. Thompson, Jie Wang

To the best of our knowledge, this is the first successful run of the computationally intensive model selection procedure to learn a consistent model across different institutions without compromising their privacy while ranking the SNPs that may collectively affect AD.

Model Selection

Seeing the Forest from the Trees in Two Looks: Matrix Sketching by Cascaded Bilateral Sampling

no code implementations25 Jul 2016 Kai Zhang, Chuanren Liu, Jie Zhang, Hui Xiong, Eric Xing, Jieping Ye

Given a matrix A of size m by n, state-of-the-art randomized algorithms take O(m * n) time and space to obtain its low-rank decomposition.

Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction

1 code implementation ICML 2017 Weizhong Zhang, Bin Hong, Wei Liu, Jieping Ye, Deng Cai, Xiaofei He, Jie Wang

By noting that sparse SVMs induce sparsities in both feature and sample spaces, we propose a novel approach, which is based on accurate estimations of the primal and dual optima of sparse SVMs, to simultaneously identify the inactive features and samples that are guaranteed to be irrelevant to the outputs.

Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection

no code implementations NeurIPS 2015 Jie Wang, Jieping Ye

By a novel hierarchical projection algorithm, MLFre is able to test the nodes independently from any of their ancestor nodes.

HONOR: Hybrid Optimization for NOn-convex Regularized problems

no code implementations NeurIPS 2015 Pinghua Gong, Jieping Ye

(2) We establish a rigorous convergence analysis for HONOR, which shows that convergence is guaranteed even for non-convex problems, while it is typically challenging to analyze the convergence for non-convex problems.

Sparse Learning

Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices

no code implementations15 May 2015 Jie Wang, Jieping Ye

One of the appealing features of DPC is that: it is safe in the sense that the detected inactive features are guaranteed to have zero coefficients in the solution vectors across all tasks.


Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets

no code implementations NeurIPS 2014 Jie Wang, Jieping Ye

Sparse-Group Lasso (SGL) has been shown to be a powerful regression technique for simultaneously discovering group and within-group sparse patterns by using a combination of the $\ell_1$ and $\ell_2$ norms.

Stochastic Coordinate Coding and Its Application for Drosophila Gene Expression Pattern Annotation

no code implementations30 Jul 2014 Binbin Lin, Qingyang Li, Qian Sun, Ming-Jun Lai, Ian Davidson, Wei Fan, Jieping Ye

The effectiveness of gene expression pattern annotation relies on the quality of feature representation.

Linear Convergence of Variance-Reduced Stochastic Gradient without Strong Convexity

no code implementations4 Jun 2014 Pinghua Gong, Jieping Ye

Under the strongly convex condition, these variance-reduced stochastic gradient algorithms achieve a linear convergence rate.

Geodesic Distance Function Learning via Heat Flow on Vector Fields

no code implementations1 May 2014 Binbin Lin, Ji Yang, Xiaofei He, Jieping Ye

Based on our theoretical analysis, we propose to first learn the gradient field of the distance function and then learn the distance function itself.

Orthogonal Rank-One Matrix Pursuit for Low Rank Matrix Completion

1 code implementation4 Apr 2014 Zheng Wang, Ming-Jun Lai, Zhaosong Lu, Wei Fan, Hasan Davulcu, Jieping Ye

Numerical results show that our proposed algorithm is more efficient than competing algorithms while achieving similar or better prediction performance.

Low-Rank Matrix Completion

Generalization Bounds for Representative Domain Adaptation

no code implementations2 Jan 2014 Chao Zhang, Lei Zhang, Wei Fan, Jieping Ye

Finally, we analyze the asymptotic convergence and the rate of convergence of the learning process for representative domain adaptation.

Domain Adaptation Generalization Bounds +1

Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraint

no code implementations31 Dec 2013 Ji Liu, Ryohei Fujimaki, Jieping Ye

Our new bounds are consistent with the bounds of a special case (least squares) and fills a previously existing theoretical gap for general convex smooth functions; 3) We show that the restricted strong convexity condition is satisfied if the number of independent samples is more than $\bar{k}\log d$ where $\bar{k}$ is the sparsity number and $d$ is the dimension of the variable; 4) We apply FoBa-gdt (with the conditional random field objective) to the sensor selection problem for human indoor activity recognition and our results show that FoBa-gdt outperforms other methods (including the ones based on forward greedy selection and L1-regularization).

Activity Recognition feature selection

Scaling SVM and Least Absolute Deviations via Exact Data Reduction

no code implementations25 Oct 2013 Jie Wang, Peter Wonka, Jieping Ye

Some appealing features of our screening method are: (1) DVI is safe in the sense that the vectors discarded by DVI are guaranteed to be non-support vectors; (2) the data set needs to be scanned only once to run the screening, whose computational cost is negligible compared to that of solving the SVM problem; (3) DVI is independent of the solvers and can be integrated with any existing efficient solvers.

Safe Screening With Variational Inequalities and Its Application to LASSO

no code implementations29 Jul 2013 Jun Liu, Zheng Zhao, Jie Wang, Jieping Ye

Safe screening is gaining increasing attention since 1) solving sparse learning formulations usually has a high computational cost especially when the number of features is large and 2) one needs to try several regularization parameters to select a suitable model.

feature selection Sparse Learning

Efficient Mixed-Norm Regularization: Algorithms and Safe Screening Methods

no code implementations16 Jul 2013 Jie Wang, Jun Liu, Jieping Ye

One key building block of the proposed algorithm is the l1q-regularized Euclidean projection (EP_1q).

Sparse Learning

A Safe Screening Rule for Sparse Logistic Regression

no code implementations NeurIPS 2014 Jie Wang, Jiayu Zhou, Jun Liu, Peter Wonka, Jieping Ye

The l1-regularized logistic regression (or sparse logistic regression) is a widely used method for simultaneous classification and feature selection.

feature selection regression

Dictionary LASSO: Guaranteed Sparse Recovery under Linear Transformation

no code implementations30 Apr 2013 Ji Liu, Lei Yuan, Jieping Ye

Specifically, we show 1) in the noiseless case, if the condition number of $D$ is bounded and the measurement number $n\geq \Omega(s\log(p))$ where $s$ is the sparsity number, then the true solution can be recovered with high probability; and 2) in the noisy case, if the condition number of $D$ is bounded and the measurement increases faster than $s\log(p)$, that is, $s\log(p)=o(n)$, the estimate error converges to zero with probability 1 when $p$ and $s$ go to infinity.

Generalization Bounds for Domain Adaptation

no code implementations NeurIPS 2012 Chao Zhang, Lei Zhang, Jieping Ye

Afterwards, we analyze the asymptotic convergence and the rate of convergence of the learning process for such kind of domain adaptation.

Domain Adaptation Generalization Bounds

A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems

4 code implementations18 Mar 2013 Pinghua Gong, Chang-Shui Zhang, Zhaosong Lu, Jianhua Huang, Jieping Ye

A commonly used approach is the Multi-Stage (MS) convex relaxation (or DC programming), which relaxes the original non-convex problem to a sequence of convex problems.

Sparse Learning

Multi-task Vector Field Learning

no code implementations NeurIPS 2012 Binbin Lin, Sen yang, Chiyuan Zhang, Jieping Ye, Xiaofei He

MTVFL has the following key properties: (1) the vector fields we learned are close to the gradient fields of the prediction functions; (2) within each task, the vector field is required to be as parallel as possible which is expected to span a low dimensional subspace; (3) the vector fields from all tasks share a low dimensional subspace.

Multi-Task Learning

Multi-Stage Multi-Task Feature Learning

no code implementations NeurIPS 2012 Pinghua Gong, Jieping Ye, Chang-Shui Zhang

In this paper, we propose a non-convex formulation for multi-task sparse feature learning based on a novel regularizer.

Lasso Screening Rules via Dual Polytope Projection

no code implementations NeurIPS 2013 Jie Wang, Peter Wonka, Jieping Ye

To improve the efficiency of solving large-scale Lasso problems, El Ghaoui and his colleagues have proposed the SAFE rules which are able to quickly identify the inactive predictors, i. e., predictors that have $0$ components in the solution vector.

Fused Multiple Graphical Lasso

no code implementations10 Sep 2012 Sen Yang, Zhaosong Lu, Xiaotong Shen, Peter Wonka, Jieping Ye

We expect the two brain networks for NC and MCI to share common structures but not to be identical to each other; similarly for the two brain networks for MCI and AD.

Clustered Multi-Task Learning Via Alternating Structure Optimization

no code implementations NeurIPS 2011 Jiayu Zhou, Jianhui Chen, Jieping Ye

We further establish the equivalence relationship between the proposed convex relaxation of CMTL and an existing convex relaxation of ASO, and show that the proposed convex CMTL formulation is significantly more efficient especially for high-dimensional data.

Multi-Task Learning

Efficient Methods for Overlapping Group Lasso

no code implementations NeurIPS 2011 Lei Yuan, Jun Liu, Jieping Ye

There have been several recent attempts to study a more general formulation, where groups of features are given, potentially with overlaps between the groups.

feature selection

Projection onto A Nonnegative Max-Heap

no code implementations NeurIPS 2011 Jun Liu, Liang Sun, Jieping Ye

In this paper, we show that such Euclidean projection problem admits an analytical solution and we develop a top-down algorithm where the key operation is to find the so-called \emph{maximal root-tree} of the subtree rooted at each node.


A Two-Stage Weighting Framework for Multi-Source Domain Adaptation

no code implementations NeurIPS 2011 Qian Sun, Rita Chattopadhyay, Sethuraman Panchanathan, Jieping Ye

In this paper we propose a two-stage domain adaptation methodology which combines weighted data from multiple sources based on marginal probability differences (first stage) as well as conditional probability differences (second stage), with the target domain data.

Domain Adaptation Vocal Bursts Valence Prediction

Identifying Alzheimer's Disease-Related Brain Regions from Multi-Modality Neuroimaging Data using Sparse Composite Linear Discrimination Analysis

no code implementations NeurIPS 2011 Shuai Huang, Jing Li, Jieping Ye, Teresa Wu, Kewei Chen, Adam Fleisher, Eric Reiman

This is especially true for early AD, at which stage the disease-related regions are most likely to be weak-effect regions that are difficult to be detected from a single modality alone.

feature selection Specificity

Multi-Stage Dantzig Selector

no code implementations NeurIPS 2010 Ji Liu, Peter Wonka, Jieping Ye

We show that if $X$ obeys a certain condition, then with a large probability the difference between the solution $\hat\beta$ estimated by the proposed method and the true solution $\beta^*$ measured in terms of the $l_p$ norm ($p\geq 1$) is bounded as \begin{equation*} \|\hat\beta-\beta^*\|_p\leq \left(C(s-N)^{1/p}\sqrt{\log m}+\Delta\right)\sigma, \end{equation*} $C$ is a constant, $s$ is the number of nonzero entries in $\beta^*$, $\Delta$ is independent of $m$ and is much smaller than the first term, and $N$ is the number of entries of $\beta^*$ larger than a certain value in the order of $\mathcal{O}(\sigma\sqrt{\log m})$.

feature selection

Moreau-Yosida Regularization for Grouped Tree Structure Learning

no code implementations NeurIPS 2010 Jun Liu, Jieping Ye

The structured regularization with a pre-defined tree structure is based on a group-Lasso penalty, where one group is defined for each node in the tree.

Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data

no code implementations NeurIPS 2009 Shuai Huang, Jing Li, Liang Sun, Jun Liu, Teresa Wu, Kewei Chen, Adam Fleisher, Eric Reiman, Jieping Ye

Recent advances in neuroimaging techniques provide great potentials for effective diagnosis of Alzheimer’s disease (AD), the most common form of dementia.

Efficient Recovery of Jointly Sparse Vectors

no code implementations NeurIPS 2009 Liang Sun, Jun Liu, Jianhui Chen, Jieping Ye

MMV is an extension of the single measurement vector (SMV) model employed in standard compressive sensing (CS).

Compressive Sensing

Multi-label Multiple Kernel Learning

no code implementations NeurIPS 2008 Shuiwang Ji, Liang Sun, Rong Jin, Jieping Ye

We present a multi-label multiple kernel learning (MKL) formulation, in which the data are embedded into a low-dimensional space directed by the instance-label correlations encoded into a hypergraph.

Discriminative K-means for Clustering

no code implementations NeurIPS 2007 Jieping Ye, Zheng Zhao, Mingrui Wu

The connection between DisKmeans and several other clustering algorithms is also analyzed.


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