Search Results for author: Jian Pei

Found 73 papers, 28 papers with code

Anytime Neural Architecture Search on Tabular Data

no code implementations15 Mar 2024 Naili Xing, Shaofeng Cai, Zhaojing Luo, Bengchin Ooi, Jian Pei

This transition demands an efficient and responsive anytime NAS approach that is capable of returning current optimal architectures within any given time budget while progressively enhancing architecture quality with increased budget allocation.

Neural Architecture Search

Linear-Time Graph Neural Networks for Scalable Recommendations

1 code implementation21 Feb 2024 Jiahao Zhang, Rui Xue, Wenqi Fan, Xin Xu, Qing Li, Jian Pei, Xiaorui Liu

In this paper, we propose a Linear-Time Graph Neural Network (LTGNN) to scale up GNN-based recommender systems to achieve comparable scalability as classic MF approaches while maintaining GNNs' powerful expressiveness for superior prediction accuracy.

Recommendation Systems

FairSample: Training Fair and Accurate Graph Convolutional Neural Networks Efficiently

no code implementations26 Jan 2024 Zicun Cong, Shi Baoxu, Shan Li, Jaewon Yang, Qi He, Jian Pei

To address the bias in node features and model parameters, FairSample is complemented by a regularization objective to optimize fairness.

Attribute Fairness

Instructed Language Models with Retrievers Are Powerful Entity Linkers

1 code implementation6 Nov 2023 Zilin Xiao, Ming Gong, Jie Wu, Xingyao Zhang, Linjun Shou, Jian Pei, Daxin Jiang

Generative approaches powered by large language models (LLMs) have demonstrated emergent abilities in tasks that require complex reasoning abilities.

Entity Linking In-Context Learning

Coherent Entity Disambiguation via Modeling Topic and Categorical Dependency

no code implementations6 Nov 2023 Zilin Xiao, Linjun Shou, Xingyao Zhang, Jie Wu, Ming Gong, Jian Pei, Daxin Jiang

We propose CoherentED, an ED system equipped with novel designs aimed at enhancing the coherence of entity predictions.

Entity Disambiguation

RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation

no code implementations19 Sep 2023 Ning Wu, Ming Gong, Linjun Shou, Jian Pei, Daxin Jiang

RUEL is the first method that connects user browsing data with typical recommendation datasets and can be generalized to various recommendation scenarios and datasets.

Contrastive Learning Retrieval +3

Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining

1 code implementation6 Aug 2023 Xidong Wu, Zhengmian Hu, Jian Pei, Heng Huang

To address the above challenge, we study the serverless multi-party collaborative AUPRC maximization problem since serverless multi-party collaborative training can cut down the communications cost by avoiding the server node bottleneck, and reformulate it as a conditional stochastic optimization problem in a serverless multi-party collaborative learning setting and propose a new ServerLess biAsed sTochastic gradiEnt (SLATE) algorithm to directly optimize the AUPRC.

Federated Learning Stochastic Optimization

Alleviating Over-smoothing for Unsupervised Sentence Representation

1 code implementation9 May 2023 Nuo Chen, Linjun Shou, Ming Gong, Jian Pei, Bowen Cao, Jianhui Chang, Daxin Jiang, Jia Li

Currently, learning better unsupervised sentence representations is the pursuit of many natural language processing communities.

Contrastive Learning Semantic Textual Similarity +1

Typos-aware Bottlenecked Pre-Training for Robust Dense Retrieval

1 code implementation17 Apr 2023 Shengyao Zhuang, Linjun Shou, Jian Pei, Ming Gong, Houxing Ren, Guido Zuccon, Daxin Jiang

To address this challenge, we propose ToRoDer (TypOs-aware bottlenecked pre-training for RObust DEnse Retrieval), a novel re-training strategy for DRs that increases their robustness to misspelled queries while preserving their effectiveness in downstream retrieval tasks.

Language Modelling Retrieval

Lexicon-Enhanced Self-Supervised Training for Multilingual Dense Retrieval

no code implementations27 Mar 2023 Houxing Ren, Linjun Shou, Jian Pei, Ning Wu, Ming Gong, Daxin Jiang

In this paper, we propose to mine and generate self-supervised training data based on a large-scale unlabeled corpus.


Disentangled Graph Social Recommendation

1 code implementation14 Mar 2023 Lianghao Xia, Yizhen Shao, Chao Huang, Yong Xu, Huance Xu, Jian Pei

In this work, we design a Disentangled Graph Neural Network (DGNN) with the integration of latent memory units, which empowers DGNN to maintain factorized representations for heterogeneous types of user and item connections.

Recommendation Systems

A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT

no code implementations18 Feb 2023 Ce Zhou, Qian Li, Chen Li, Jun Yu, Yixin Liu, Guangjing Wang, Kai Zhang, Cheng Ji, Qiben Yan, Lifang He, Hao Peng, JianXin Li, Jia Wu, Ziwei Liu, Pengtao Xie, Caiming Xiong, Jian Pei, Philip S. Yu, Lichao Sun

This study provides a comprehensive review of recent research advancements, challenges, and opportunities for PFMs in text, image, graph, as well as other data modalities.

Graph Learning Language Modelling +1

Bridge the Gap between Language models and Tabular Understanding

no code implementations16 Feb 2023 Nuo Chen, Linjun Shou, Ming Gong, Jian Pei, Chenyu You, Jianhui Chang, Daxin Jiang, Jia Li

For instance, TPLMs jointly pre-trained with table and text input could be effective for tasks also with table-text joint input like table question answering, but it may fail for tasks with only tables or text as input such as table retrieval.

Contrastive Learning Language Modelling +2

Knowledge-enhanced Neural Machine Reasoning: A Review

no code implementations4 Feb 2023 Tanmoy Chowdhury, Chen Ling, Xuchao Zhang, Xujiang Zhao, Guangji Bai, Jian Pei, Haifeng Chen, Liang Zhao

Knowledge-enhanced neural machine reasoning has garnered significant attention as a cutting-edge yet challenging research area with numerous practical applications.

LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation

1 code implementation3 Feb 2023 Rui Xue, Haoyu Han, MohamadAli Torkamani, Jian Pei, Xiaorui Liu

Recent works have demonstrated the benefits of capturing long-distance dependency in graphs by deeper graph neural networks (GNNs).

Graph Representation Learning

Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum

1 code implementation5 Oct 2022 Nian Liu, Xiao Wang, Deyu Bo, Chuan Shi, Jian Pei

Then we theoretically prove that GCL is able to learn the invariance information by contrastive invariance theorem, together with our GAME rule, for the first time, we uncover that the learned representations by GCL essentially encode the low-frequency information, which explains why GCL works.

Contrastive Learning

Knowledge-Injected Federated Learning

1 code implementation16 Aug 2022 Zhenan Fan, Zirui Zhou, Jian Pei, Michael P. Friedlander, Jiajie Hu, Chengliang Li, Yong Zhang

Federated learning is an emerging technique for training models from decentralized data sets.

Federated Learning

Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation

1 code implementation21 Jun 2022 Shengyao Zhuang, Houxing Ren, Linjun Shou, Jian Pei, Ming Gong, Guido Zuccon, Daxin Jiang

This problem is further exacerbated when using DSI for cross-lingual retrieval, where document text and query text are in different languages.

Passage Retrieval Retrieval

Communication-Efficient Robust Federated Learning with Noisy Labels

no code implementations11 Jun 2022 Junyi Li, Jian Pei, Heng Huang

Bilevel optimization problem is a type of optimization problem with two levels of entangled problems.

Bilevel Optimization Federated Learning +2

Multi-Behavior Sequential Recommendation with Temporal Graph Transformer

1 code implementation6 Jun 2022 Lianghao Xia, Chao Huang, Yong Xu, Jian Pei

The new TGT method endows the sequential recommendation architecture to distill dedicated knowledge for type-specific behavior relational context and the implicit behavior dependencies.

Sequential Recommendation

Trustworthy Graph Neural Networks: Aspects, Methods and Trends

no code implementations16 May 2022 He Zhang, Bang Wu, Xingliang Yuan, Shirui Pan, Hanghang Tong, Jian Pei

Graph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging from daily applications like recommendation systems and question answering to cutting-edge technologies such as drug discovery in life sciences and n-body simulation in astrophysics.

Drug Discovery Edge-computing +4

Label-aware Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding

no code implementations7 May 2022 Shining Liang, Linjun Shou, Jian Pei, Ming Gong, Wanli Zuo, Xianglin Zuo, Daxin Jiang

Despite the great success of spoken language understanding (SLU) in high-resource languages, it remains challenging in low-resource languages mainly due to the lack of labeled training data.

Contrastive Learning Spoken Language Understanding +1

Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction

1 code implementation18 Apr 2022 Zhonghang Li, Chao Huang, Lianghao Xia, Yong Xu, Jian Pei

Crime has become a major concern in many cities, which calls for the rising demand for timely predicting citywide crime occurrence.

Crime Prediction Decision Making +1

Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling

no code implementations NAACL 2022 Nuo Chen, Linjun Shou, Ming Gong, Jian Pei, Daxin Jiang

Large-scale cross-lingual pre-trained language models (xPLMs) have shown effectiveness in cross-lingual sequence labeling tasks (xSL), such as cross-lingual machine reading comprehension (xMRC) by transferring knowledge from a high-resource language to low-resource languages.

Contrastive Learning Language Modelling +1

Membership Privacy Protection for Image Translation Models via Adversarial Knowledge Distillation

no code implementations10 Mar 2022 Saeed Ranjbar Alvar, Lanjun Wang, Jian Pei, Yong Zhang

Image-to-image translation models are shown to be vulnerable to the Membership Inference Attack (MIA), in which the adversary's goal is to identify whether a sample is used to train the model or not.

Image-to-Image Translation Inference Attack +3

Fair and efficient contribution valuation for vertical federated learning

no code implementations7 Jan 2022 Zhenan Fan, Huang Fang, Zirui Zhou, Jian Pei, Michael P. Friedlander, Yong Zhang

We show that VerFedSV not only satisfies many desirable properties for fairness but is also efficient to compute, and can be adapted to both synchronous and asynchronous vertical federated learning algorithms.

Fairness Vertical Federated Learning

Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation

1 code implementation22 Dec 2021 Yiming Zhang, Lingfei Wu, Qi Shen, Yitong Pang, Zhihua Wei, Fangli Xu, Bo Long, Jian Pei

As a result, we first propose a more realistic CRS learning setting, namely Multi-Interest Multi-round Conversational Recommendation, where users may have multiple interests in attribute instance combinations and accept multiple items with partially overlapped combinations of attribute instances.

Attribute Multiple-choice

Mining Minority-class Examples With Uncertainty Estimates

no code implementations15 Dec 2021 Gursimran Singh, Lingyang Chu, Lanjun Wang, Jian Pei, Qi Tian, Yong Zhang

In the real world, the frequency of occurrence of objects is naturally skewed forming long-tail class distributions, which results in poor performance on the statistically rare classes.

From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension

no code implementations9 Dec 2021 Nuo Chen, Linjun Shou, Min Gong, Jian Pei, Daxin Jiang

Cross-lingual Machine Reading Comprehension (xMRC) is challenging due to the lack of training data in low-resource languages.

Contrastive Learning Machine Reading Comprehension

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.

Privacy Preserving Vertical Federated Learning

Achieving Model Fairness in Vertical Federated Learning

1 code implementation17 Sep 2021 Changxin Liu, Zhenan Fan, Zirui Zhou, Yang Shi, Jian Pei, Lingyang Chu, Yong Zhang

To solve it in a federated and privacy-preserving manner, we consider the equivalent dual form of the problem and develop an asynchronous gradient coordinate-descent ascent algorithm, where some active data parties perform multiple parallelized local updates per communication round to effectively reduce the number of communication rounds.

BIG-bench Machine Learning Fairness +2

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 +1

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 Procedural Text Understanding +1

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.


Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation

1 code implementation8 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

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 +4

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 Astronomy +2

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

Our Acc-MDA achieves a low gradient complexity of $\tilde{O}(\kappa_y^{4. 5}\epsilon^{-3})$ without requiring large batches for finding an $\epsilon$-stationary point.

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.

Passage Ranking 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 (a. k. a, derivative-free) methods are a class of effective optimization methods for solving complex machine learning problems, where gradients of the objective functions are not available or computationally prohibitive.

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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