Search Results for author: Xiangliang Zhang

Found 61 papers, 14 papers with code

Joint Abductive and Inductive Neural Logical Reasoning

1 code implementation29 May 2022 Zhenwei Tang, Shichao Pei, Xi Peng, Fuzhen Zhuang, Xiangliang Zhang, Robert Hoehndorf

Neural logical reasoning (NLR) is a fundamental task in knowledge discovery and artificial intelligence.

Target-aware Abstractive Related Work Generation with Contrastive Learning

1 code implementation26 May 2022 Xiuying Chen, Hind Alamro, Mingzhe Li, Shen Gao, Rui Yan, Xin Gao, Xiangliang Zhang

The related work section is an important component of a scientific paper, which highlights the contribution of the target paper in the context of the reference papers.

Contrastive Learning TAG

Few-Shot Learning on Graphs

no code implementations17 Mar 2022 Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla, Huan Liu

In light of this, few-shot learning on graphs (FSLG), which combines the strengths of graph representation learning and few-shot learning together, has been proposed to tackle the performance degradation in face of limited annotated data challenge.

Few-Shot Learning Graph Mining +1

Data-Driven State Estimation for Light-Emitting Diode Underwater Optical Communication

no code implementations30 Dec 2021 Yingquan Li, Zhenwen Liang, Ibrahima N'Doye, Xiangliang Zhang, Mohamed-Slim Alouini, Taous-Meriem Laleg-Kirati

Light-Emitting Diodes (LEDs) based underwater optical wireless communications (UOWCs), a technology with low latency and high data rates, have attracted significant importance for underwater robots.

Open-Set Crowdsourcing using Multiple-Source Transfer Learning

no code implementations7 Nov 2021 Guangyang Han, Guoxian Yu, Lei Liu, Lizhen Cui, Carlotta Domeniconi, Xiangliang Zhang

First, OSCrowd integrates crowd theme related datasets into a large source domain to facilitate partial transfer learning to approximate the label space inference of these tasks.

Transfer Learning

Crowdsourcing with Meta-Workers: A New Way to Save the Budget

no code implementations7 Nov 2021 Guangyang Han, Guoxian Yu, Lizhen Cui, Carlotta Domeniconi, Xiangliang Zhang

Due to the unreliability of Internet workers, it's difficult to complete a crowdsourcing project satisfactorily, especially when the tasks are multiple and the budget is limited.

Few-Shot Learning Image Classification

Cross-modal Zero-shot Hashing by Label Attributes Embedding

no code implementations7 Nov 2021 Runmin Wang, Guoxian Yu, Lei Liu, Lizhen Cui, Carlotta Domeniconi, Xiangliang Zhang

Cross-modal hashing (CMH) is one of the most promising methods in cross-modal approximate nearest neighbor search.

Meta Cross-Modal Hashing on Long-Tailed Data

no code implementations7 Nov 2021 Runmin Wang, Guoxian Yu, Carlotta Domeniconi, Xiangliang Zhang

Due to the lack of training samples in the tail classes, MetaCMH first learns direct features from data in different modalities, and then introduces an associative memory module to learn the memory features of samples of the tail classes.


Towards Understanding the Robustness Against Evasion Attack on Categorical Data

no code implementations ICLR 2022 Hongyan Bao, Yufei Han, Yujun Zhou, Yun Shen, Xiangliang Zhang

Characterizing and assessing the adversarial vulnerability of classification models with categorical input has been a practically important, while rarely explored research problem.


Overview of the Arabic Sentiment Analysis 2021 Competition at KAUST

no code implementations29 Sep 2021 Hind Alamro, Manal Alshehri, Basma Alharbi, Zuhair Khayyat, Manal Kalkatawi, Inji Ibrahim Jaber, Xiangliang Zhang

From our recently released ASAD dataset, we provide the competitors with 55K tweets for training, and 20K tweets for validation, based on which the performance of participating teams are ranked on a leaderboard, https://www. kaggle. com/c/arabic-sentiment-analysis-2021-kaust.

Arabic Sentiment Analysis

A Simple and Debiased Sampling Method for Personalized Ranking

no code implementations29 Sep 2021 Lu Yu, Shichao Pei, Chuxu Zhang, Xiangliang Zhang

Pairwise ranking models have been widely used to address various problems, such as recommendation.

AppQ: Warm-starting App Recommendation Based on View Graphs

no code implementations8 Sep 2021 Dan Su, Jiqiang Liu, Sencun Zhu, Xiaoyang Wang, Wei Wang, Xiangliang Zhang

In this work, we propose AppQ, a novel app quality grading and recommendation system that extracts inborn features of apps based on app source code.

Recommendation Systems

FARF: A Fair and Adaptive Random Forests Classifier

no code implementations17 Aug 2021 Wenbin Zhang, Albert Bifet, Xiangliang Zhang, Jeremy C. Weiss, Wolfgang Nejdl

This algorithm, called FARF (Fair and Adaptive Random Forests), is based on using online component classifiers and updating them according to the current distribution, that also accounts for fairness and a single hyperparameters that alters fairness-accuracy balance.

Decision Making Fairness

Capturing Relations between Scientific Papers: An Abstractive Model for Related Work Section Generation

1 code implementation ACL 2021 Xiuying Chen, Hind Alamro, Mingzhe Li, Shen Gao, Xiangliang Zhang, Dongyan Zhao, Rui Yan

Hence, in this paper, we propose a Relation-aware Related work Generator (RRG), which generates an abstractive related work from the given multiple scientific papers in the same research area.

Attack Transferability Characterization for Adversarially Robust Multi-label Classification

1 code implementation29 Jun 2021 Zhuo Yang, Yufei Han, Xiangliang Zhang

We unveil how the transferability level of the attack determines the attackability of the classifier via establishing an information-theoretic analysis of the adversarial risk.

Adversarial Attack Classification +3

Socially-Aware Self-Supervised Tri-Training for Recommendation

1 code implementation7 Jun 2021 Junliang Yu, Hongzhi Yin, Min Gao, Xin Xia, Xiangliang Zhang, Nguyen Quoc Viet Hung

Under this scheme, only a bijective mapping is built between nodes in two different views, which means that the self-supervision signals from other nodes are being neglected.

Contrastive Learning Recommendation Systems +2

Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modelling

no code implementations5 Apr 2021 Tong Chen, Hongzhi Yin, Xiangliang Zhang, Zi Huang, Yang Wang, Meng Wang

As a well-established approach, factorization machine (FM) is capable of automatically learning high-order interactions among features to make predictions without the need for manual feature engineering.

Feature Engineering

Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning

no code implementations4 Apr 2021 Tong Chen, Hongzhi Yin, Jie Ren, Zi Huang, Xiangliang Zhang, Hao Wang

In WIDEN, we propose a novel inductive, meta path-free message passing scheme that packs up heterogeneous node features with their associated edges from both low- and high-order neighbor nodes.

Graph Representation Learning

Fast-adapting and Privacy-preserving Federated Recommender System

no code implementations2 Apr 2021 Qinyong Wang, Hongzhi Yin, Tong Chen, Junliang Yu, Alexander Zhou, Xiangliang Zhang

In the mobile Internet era, the recommender system has become an irreplaceable tool to help users discover useful items, and thus alleviating the information overload problem.

Federated Learning Meta-Learning +2

Hierarchical Hyperedge Embedding-based Representation Learning for Group Recommendation

no code implementations24 Mar 2021 Lei Guo, Hongzhi Yin, Tong Chen, Xiangliang Zhang, Kai Zheng

However, the representation learning for a group is most complex beyond the fusion of group member representation, as the personal preferences and group preferences may be in different spaces.

Representation Learning

Graph Embedding for Recommendation against Attribute Inference Attacks

no code implementations29 Jan 2021 Shijie Zhang, Hongzhi Yin, Tong Chen, Zi Huang, Lizhen Cui, Xiangliang Zhang

Specifically, in GERAI, we bind the information perturbation mechanism in differential privacy with the recommendation capability of graph convolutional networks.

Graph Embedding Recommendation Systems

Combat Data Shift in Few-shot Learning with Knowledge Graph

no code implementations27 Jan 2021 Yongchun Zhu, Fuzhen Zhuang, Xiangliang Zhang, Zhiyuan Qi, Zhiping Shi, Juan Cao, Qing He

However, in real-world applications, few-shot learning paradigm often suffers from data shift, i. e., samples in different tasks, even in the same task, could be drawn from various data distributions.

Few-Shot Learning

Does Head Label Help for Long-Tailed Multi-Label Text Classification

1 code implementation24 Jan 2021 Lin Xiao, Xiangliang Zhang, Liping Jing, Chi Huang, Mingyang Song

To address the challenge of insufficient training data on tail label classification, we propose a Head-to-Tail Network (HTTN) to transfer the meta-knowledge from the data-rich head labels to data-poor tail labels.

General Classification Multi Label Text Classification +1

Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation

4 code implementations16 Jan 2021 Junliang Yu, Hongzhi Yin, Jundong Li, Qinyong Wang, Nguyen Quoc Viet Hung, Xiangliang Zhang

In this paper, we fill this gap and propose a multi-channel hypergraph convolutional network to enhance social recommendation by leveraging high-order user relations.

Recommendation Systems Self-Supervised Learning

FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection

no code implementations8 Jan 2021 Guanhua Ye, Hongzhi Yin, Tong Chen, Hongxu Chen, Lizhen Cui, Xiangliang Zhang

Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease that seriously jeopardizes the health of human beings.

Sleep apnea detection

Characterizing the Evasion Attackability of Multi-label Classifiers

no code implementations17 Dec 2020 Zhuo Yang, Yufei Han, Xiangliang Zhang

Evasion attack in multi-label learning systems is an interesting, widely witnessed, yet rarely explored research topic.

Multi-Label Learning

Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation

1 code implementation12 Dec 2020 Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang, Lizhen Cui, Xiangliang Zhang

Moreover, to enhance hypergraph modeling, we devise another graph convolutional network which is based on the line graph of the hypergraph and then integrate self-supervised learning into the training of the networks by maximizing mutual information between the session representations learned via the two networks, serving as an auxiliary task to improve the recommendation task.

Self-Supervised Learning Session-Based Recommendations

ASAD: A Twitter-based Benchmark Arabic Sentiment Analysis Dataset

no code implementations1 Nov 2020 Basma Alharbi, Hind Alamro, Manal Alshehri, Zuhair Khayyat, Manal Kalkatawi, Inji Ibrahim Jaber, Xiangliang Zhang

This paper provides a detailed description of a new Twitter-based benchmark dataset for Arabic Sentiment Analysis (ASAD), which is launched in a competition3, sponsored by KAUST for awarding 10000 USD, 5000 USD and 2000 USD to the first, second and third place winners, respectively.

Arabic Sentiment Analysis

Deterministic and probabilistic deep learning models for inverse design of broadband acoustic cloak

no code implementations28 Oct 2020 Waqas W. Ahmed, Mohamed Farhat, Xiangliang Zhang, Ying Wu

Concealing an object from incoming waves (light and/or sound) remained science fiction for a long time due to the absence of wave-shielding materials in nature.

Applied Physics Computational Physics

Multi-typed Objects Multi-view Multi-instance Multi-label Learning

no code implementations6 Oct 2020 Yuanlin Yang, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Xiangliang Zhang

Multi-typed objects Multi-view Multi-instance Multi-label Learning (M4L) deals with interconnected multi-typed objects (or bags) that are made of diverse instances, represented with heterogeneous feature views and annotated with a set of non-exclusive but semantically related labels.

Multi-Label Learning

Deep Incomplete Multi-View Multiple Clusterings

no code implementations2 Oct 2020 Shaowei Wei, Jun Wang, Guoxian Yu, Carlotta Domeniconi, Xiangliang Zhang

Multi-view clustering aims at exploiting information from multiple heterogeneous views to promote clustering.

PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization

no code implementations25 Aug 2020 Zhize Li, Hongyan Bao, Xiangliang Zhang, Peter Richtárik

Then, we show that PAGE obtains the optimal convergence results $O(n+\frac{\sqrt{n}}{\epsilon^2})$ (finite-sum) and $O(b+\frac{\sqrt{b}}{\epsilon^2})$ (online) matching our lower bounds for both nonconvex finite-sum and online problems.

Graph Factorization Machines for Cross-Domain Recommendation

no code implementations12 Jul 2020 Dongbo Xi, Fuzhen Zhuang, Yongchun Zhu, Pengpeng Zhao, Xiangliang Zhang, Qing He

In this paper, we propose a Graph Factorization Machine (GFM) which utilizes the popular Factorization Machine to aggregate multi-order interactions from neighborhood for recommendation.

Recommendation Systems

Attention-Aware Answers of the Crowd

no code implementations24 Dec 2019 Jingzheng Tu, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Xiangliang Zhang

However, they all assume that workers' label quality is stable over time (always at the same level whenever they conduct the tasks).

Bayesian Inference

Multi-View Multiple Clusterings using Deep Matrix Factorization

no code implementations26 Nov 2019 Shaowei Wei, Jun Wang, Guoxian Yu, Carlotta, Xiangliang Zhang

Multi-view clustering aims at integrating complementary information from multiple heterogeneous views to improve clustering results.

Prototypical Networks for Multi-Label Learning

no code implementations17 Nov 2019 Zhuo Yang, Yufei Han, Guoxian Yu, Qiang Yang, Xiangliang Zhang

We propose to formulate multi-label learning as a estimation of class distribution in a non-linear embedding space, where for each label, its positive data embeddings and negative data embeddings distribute compactly to form a positive component and negative component respectively, while the positive component and negative component are pushed away from each other.

Multi-Label Classification Multi-Label Learning

Active Multi-Label Crowd Consensus

no code implementations7 Nov 2019 Jinzheng Tu, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Xiangliang Zhang

AMCC accounts for the commonality and individuality of workers, and assumes that workers can be organized into different groups.

Recurrent Attention Walk for Semi-supervised Classification

1 code implementation22 Oct 2019 Uchenna Akujuobi, Qiannan Zhang, Han Yufei, Xiangliang Zhang

We propose to explore the neighborhood in a reinforcement learning setting and find a walk path well-tuned for classifying the unlabelled target nodes.

Classification General Classification +1

Cross-modal Zero-shot Hashing

no code implementations19 Aug 2019 Xuanwu Liu, Zhao Li, Jun Wang, Guoxian Yu, Carlotta Domeniconi, Xiangliang Zhang

It then defines an objective function to achieve deep feature learning compatible with the composite similarity preserving, category attribute space learning, and hashing coding function learning.

Weakly-paired Cross-Modal Hashing

no code implementations29 May 2019 Xuanwu Liu, Jun Wang, Guoxian Yu, Carlotta Domeniconi, Xiangliang Zhang

FlexCMH first introduces a clustering-based matching strategy to explore the local structure of each cluster, and thus to find the potential correspondence between clusters (and samples therein) across modalities.

ActiveHNE: Active Heterogeneous Network Embedding

no code implementations14 May 2019 Xia Chen, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Zhao Li, Xiangliang Zhang

To maximize the profit of utilizing the rare and valuable supervised information in HNEs, we develop a novel Active Heterogeneous Network Embedding (ActiveHNE) framework, which includes two components: Discriminative Heterogeneous Network Embedding (DHNE) and Active Query in Heterogeneous Networks (AQHN).

Network Embedding

Multi-View Multiple Clustering

no code implementations13 May 2019 Shixing Yao, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Xiangliang Zhang

It then uses matrix factorization on the individual matrices, along with the shared matrix, to generate diverse clusterings of high-quality.

Representation Learning

Robust Federated Training via Collaborative Machine Teaching using Trusted Instances

no code implementations8 May 2019 Yufei Han, Xiangliang Zhang

In our work, we propose a collaborative and privacy-preserving machine teaching paradigm with multiple distributed teachers, to improve robustness of the federated training process against local data corruption.

Data Poisoning Federated Learning +1

Risk Convergence of Centered Kernel Ridge Regression with Large Dimensional Data

no code implementations19 Apr 2019 Khalil Elkhalil, Abla Kammoun, Xiangliang Zhang, Mohamed-Slim Alouini, Tareq Al-Naffouri

This paper carries out a large dimensional analysis of a variation of kernel ridge regression that we call \emph{centered kernel ridge regression} (CKRR), also known in the literature as kernel ridge regression with offset.

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

CreditCoin: A Privacy-Preserving Blockchain-Based Incentive Announcement Network for Communications of Smart Vehicles

no code implementations7 Jul 2018 Lun Li, Jiqiang Liu, Lichen Cheng, Shuo Qiu, Wei Wang, Xiangliang Zhang, and Zonghua Zhang

The vehicular announcement network is one of the most promising utilities in the communications of smart vehicles and in the smart transportation systems.

Privacy Preserving

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

Graph Embedding with Rich Information through Heterogeneous Network

no code implementations18 Oct 2017 Guolei Sun, Xiangliang Zhang

In this paper, we proposed a novel and general framework of representation learning for graph with rich text information through constructing a bipartite heterogeneous network.

General Classification Graph Embedding +1

Coarse Grained Exponential Variational Autoencoders

no code implementations25 Feb 2017 Ke Sun, Xiangliang Zhang

Variational autoencoders (VAE) often use Gaussian or category distribution to model the inference process.

Cannot find the paper you are looking for? You can Submit a new open access paper.