Search Results for author: Jian Pei

Found 40 papers, 10 papers with code

Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation

1 code implementation8 Oct 2021 Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Xiyue Zhang, Hongsheng Yang, Jian Pei, Liefeng Bo

In particular: i) complex inter-dependencies across different types of user behaviors; ii) the incorporation of knowledge-aware item relations into the multi-behavior recommendation framework; iii) dynamic characteristics of multi-typed user-item interactions.

Graph Attention Recommendation Systems

AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization

no code implementations26 Sep 2021 Qingsong Zhang, Bin Gu, Cheng Deng, Songxiang Gu, Liefeng Bo, Jian Pei, Heng Huang

To address the challenges of communication and computation resource utilization, we propose an asynchronous stochastic quasi-Newton (AsySQN) framework for VFL, under which three algorithms, i. e. AsySQN-SGD, -SVRG and -SAGA, are proposed.

Federated Learning

Achieving Model Fairness in Vertical Federated Learning

no code implementations17 Sep 2021 Changxin Liu, Zirui Zhou, Yang Shi, Jian Pei, Lingyang Chu, Yong Zhang

Vertical federated learning (VFL), which enables multiple enterprises possessing non-overlapped features to strengthen their machine learning models without disclosing their private data and model parameters, has received increasing attention lately.

Fairness Federated Learning

FedFair: Training Fair Models In Cross-Silo Federated Learning

no code implementations13 Sep 2021 Lingyang Chu, Lanjun Wang, Yanjie Dong, Jian Pei, Zirui Zhou, Yong Zhang

In this paper, we first propose a federated estimation method to accurately estimate the fairness of a model without infringing the data privacy of any party.

Fairness Federated Learning

Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding

no code implementations EMNLP 2021 YingMei Guo, Linjun Shou, Jian Pei, Ming Gong, Mingxing Xu, Zhiyong Wu, Daxin Jiang

Although various data augmentation approaches have been proposed to synthesize training data in low-resource target languages, the augmented data sets are often noisy, and thus impede the performance of SLU models.

Data Augmentation Denoising +2

Auto-Split: A General Framework of Collaborative Edge-Cloud AI

1 code implementation30 Aug 2021 Amin Banitalebi-Dehkordi, Naveen Vedula, Jian Pei, Fei Xia, Lanjun Wang, Yong Zhang

At the same time, large amounts of input data are collected at the edge of cloud.

Finding Representative Interpretations on Convolutional Neural Networks

no code implementations ICCV 2021 Peter Cho-Ho Lam, Lingyang Chu, Maxim Torgonskiy, Jian Pei, Yong Zhang, Lanjun Wang

Interpreting the decision logic behind effective deep convolutional neural networks (CNN) on images complements the success of deep learning models.

Reasoning over Entity-Action-Location Graph for Procedural Text Understanding

no code implementations ACL 2021 Hao Huang, Xiubo Geng, Jian Pei, Guodong Long, Daxin Jiang

Procedural text understanding aims at tracking the states (e. g., create, move, destroy) and locations of the entities mentioned in a given paragraph.

graph construction Representation Learning

Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation

no code implementations8 Jul 2021 Yitong Pang, Lingfei Wu, Qi Shen, Yiming Zhang, Zhihua Wei, Fangli Xu, Ethan Chang, Bo Long, Jian Pei

Additionally, existing personalized session-based recommenders capture user preference only based on the sessions of the current user, but ignore the useful item-transition patterns from other user's historical sessions.

Session-Based Recommendations

Robust Counterfactual Explanations on Graph Neural Networks

no code implementations NeurIPS 2021 Mohit Bajaj, Lingyang Chu, Zi Yu Xue, Jian Pei, Lanjun Wang, Peter Cho-Ho Lam, Yong Zhang

Massive deployment of Graph Neural Networks (GNNs) in high-stake applications generates a strong demand for explanations that are robust to noise and align well with human intuition.

Graph Neural Networks for Natural Language Processing: A Survey

1 code implementation10 Jun 2021 Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Jian Pei, Bo Long

Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProcessing (NLP).

graph construction Graph Representation Learning

Reinforced Multi-Teacher Selection for Knowledge Distillation

no code implementations11 Dec 2020 Fei Yuan, Linjun Shou, Jian Pei, Wutao Lin, Ming Gong, Yan Fu, Daxin Jiang

When multiple teacher models are available in distillation, the state-of-the-art methods assign a fixed weight to a teacher model in the whole distillation.

Knowledge Distillation Model Compression

CalibreNet: Calibration Networks for Multilingual Sequence Labeling

no code implementations11 Nov 2020 Shining Liang, Linjun Shou, Jian Pei, Ming Gong, Wanli Zuo, Daxin Jiang

To tackle the challenge of lack of training data in low-resource languages, we dedicatedly develop a novel unsupervised phrase boundary recovery pre-training task to enhance the multilingual boundary detection capability of CalibreNet.

Boundary Detection Cross-Lingual NER +3

Comprehensible Counterfactual Explanation on Kolmogorov-Smirnov Test

no code implementations1 Nov 2020 Zicun Cong, Lingyang Chu, Yu Yang, Jian Pei

One challenge remained untouched is how we can obtain an explanation on why a test set fails the KS test.

Anomaly Detection Counterfactual Explanation

Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation

no code implementations COLING 2020 Junhao Liu, Linjun Shou, Jian Pei, Ming Gong, Min Yang, Daxin Jiang

Then, we devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages.

Knowledge Distillation Machine Reading Comprehension +1

A Graph Representation of Semi-structured Data for Web Question Answering

no code implementations COLING 2020 Xingyao Zhang, Linjun Shou, Jian Pei, Ming Gong, Lijie Wen, Daxin Jiang

The abundant semi-structured data on the Web, such as HTML-based tables and lists, provide commercial search engines a rich information source for question answering (QA).

Question Answering

Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization

no code implementations18 Aug 2020 Feihu Huang, Shangqian Gao, Jian Pei, Heng Huang

Moreover, we propose an accelerated first-order momentum descent ascent (Acc-MDA) method for solving white-box minimax problems, and prove that it achieves a lower gradient complexity of $\tilde{O}(\kappa_y^{2. 5}\epsilon^{-3})$ given batch size $b=\kappa_y^{4}$ for finding an $\epsilon$-stationary point, which improves the best known result by a factor of $O(\kappa_y^{1/2})$.

Adversarial Attack

Momentum-Based Policy Gradient Methods

1 code implementation ICML 2020 Feihu Huang, Shangqian Gao, Jian Pei, Heng Huang

In particular, we present a non-adaptive version of IS-MBPG method, i. e., IS-MBPG*, which also reaches the best known sample complexity of $O(\epsilon^{-3})$ without any large batches.

Policy Gradient Methods

AM-GCN: Adaptive Multi-channel Graph Convolutional Networks

no code implementations5 Jul 2020 Xiao Wang, Meiqi Zhu, Deyu Bo, Peng Cui, Chuan Shi, Jian Pei

We tackle the challenge and propose an adaptive multi-channel graph convolutional networks for semi-supervised classification (AM-GCN).

General Classification

Measuring Model Complexity of Neural Networks with Curve Activation Functions

no code implementations16 Jun 2020 Xia Hu, Weiqing Liu, Jiang Bian, Jian Pei

Our results demonstrate that the occurrence of overfitting is positively correlated with the increase of model complexity during training.

Mining Implicit Relevance Feedback from User Behavior for Web Question Answering

no code implementations13 Jun 2020 Linjun Shou, Shining Bo, Feixiang Cheng, Ming Gong, Jian Pei, Daxin Jiang

In this paper, we make the first study to explore the correlation between user behavior and passage relevance, and propose a novel approach for mining training data for Web QA.

Question Answering

Asymmetric Transitivity Preserving Graph Embedding

1 code implementation ‏‏‎ ‎ 2020 Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, Wenwu Zhu

In particular, we develop a novel graph embedding algorithm, High-Order Proximity preserved Embedding (HOPE for short), which is scalable to preserve high-order proximities of large scale graphs and capable of capturing the asymmetric transitivity.

Graph Embedding Link Prediction

Nonconvex Zeroth-Order Stochastic ADMM Methods with Lower Function Query Complexity

no code implementations30 Jul 2019 Feihu Huang, Shangqian Gao, Jian Pei, Heng Huang

Zeroth-order methods powerful optimization tools for solving many machine learning problems because it only need function values (not gradient) in the optimization.

Adversarial Attack

Exact and Consistent Interpretation of Piecewise Linear Models Hidden behind APIs: A Closed Form Solution

1 code implementation17 Jun 2019 Zicun Cong, Lingyang Chu, Lanjun Wang, Xia Hu, Jian Pei

More and more AI services are provided through APIs on cloud where predictive models are hidden behind APIs.

Detecting Customer Complaint Escalation with Recurrent Neural Networks and Manually-Engineered Features

no code implementations NAACL 2019 Wei Yang, Luchen Tan, Chunwei Lu, Anqi Cui, Han Li, Xi Chen, Kun Xiong, Muzi Wang, Ming Li, Jian Pei, Jimmy Lin

Consumers dissatisfied with the normal dispute resolution process provided by an e-commerce company{'}s customer service agents have the option of escalating their complaints by filing grievances with a government authority.

Visually-aware Recommendation with Aesthetic Features

no code implementations2 May 2019 Wenhui Yu, Xiangnan He, Jian Pei, Xu Chen, Li Xiong, Jinfei Liu, Zheng Qin

While recent developments on visually-aware recommender systems have taken the product image into account, none of them has considered the aesthetic aspect.

Decision Making Recommendation Systems +1

Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution

no code implementations17 Feb 2018 Lingyang Chu, Xia Hu, Juhua Hu, Lanjun Wang, Jian Pei

Strong intelligent machines powered by deep neural networks are increasingly deployed as black boxes to make decisions in risk-sensitive domains, such as finance and medical.

TIMERS: Error-Bounded SVD Restart on Dynamic Networks

1 code implementation27 Nov 2017 Ziwei Zhang, Peng Cui, Jian Pei, Xiao Wang, Wenwu Zhu

By setting a maximum tolerated error as a threshold, we can trigger SVD restart automatically when the margin exceeds this threshold. We prove that the time complexity of our method is linear with respect to the number of local dynamic changes, and our method is general across different types of dynamic networks.

Social and Information Networks

A Survey on Network Embedding

no code implementations23 Nov 2017 Peng Cui, Xiao Wang, Jian Pei, Wenwu Zhu

Network embedding assigns nodes in a network to low-dimensional representations and effectively preserves the network structure.

Social and Information Networks

Finding Theme Communities from Database Networks

no code implementations23 Sep 2017 Lingyang Chu, Zhefeng Wang, Jian Pei, Yanyan Zhang, Yu Yang, Enhong Chen

Given a database network where each vertex is associated with a transaction database, we are interested in finding theme communities.

Font Size: Community Preserving Network Embedding

2 code implementations AAAI 2017 Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, Shiqiang Yang

While previous network embedding methods primarily preserve the microscopic structure, such as the first- and second-order proximities of nodes, the mesoscopic community structure, which is one of the most prominent feature of networks, is largely ignored.

Community Detection Network Embedding

Online Visual Analytics of Text Streams

1 code implementation13 Dec 2015 Shixia Liu, Jialun Yin, Xiting Wang, Weiwei Cui, Kelei Cao, Jian Pei

To this end, we learn a set of streaming tree cuts from topic trees based on user-selected focus nodes.

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