Search Results for author: Peng Cui

Found 96 papers, 39 papers with code

Accurate and Reliable Predictions with Mutual-Transport Ensemble

no code implementations30 May 2024 Han Liu, Peng Cui, Bingning Wang, Jun Zhu, Xiaolin Hu

Deep Neural Networks (DNNs) have achieved remarkable success in a variety of tasks, especially when it comes to prediction accuracy.

Stability Evaluation via Distributional Perturbation Analysis

no code implementations6 May 2024 Jose Blanchet, Peng Cui, Jiajin Li, Jiashuo Liu

Empirically, we validate the practical utility of our stability evaluation criterion across a host of real-world applications.

Debiased Collaborative Filtering with Kernel-Based Causal Balancing

1 code implementation30 Apr 2024 Haoxuan Li, Chunyuan Zheng, Yanghao Xiao, Peng Wu, Zhi Geng, Xu Chen, Peng Cui

Inspired by these gaps, we propose to approximate the balancing functions in reproducing kernel Hilbert space and demonstrate that, based on the universal property and representer theorem of kernel functions, the causal balancing constraints can be better satisfied.

Collaborative Filtering

PPA-Game: Characterizing and Learning Competitive Dynamics Among Online Content Creators

no code implementations22 Mar 2024 Renzhe Xu, Haotian Wang, Xingxuan Zhang, Bo Li, Peng Cui

We introduce the Proportional Payoff Allocation Game (PPA-Game) to model how agents, akin to content creators on platforms like YouTube and TikTok, compete for divisible resources and consumers' attention.

On the Out-Of-Distribution Generalization of Multimodal Large Language Models

no code implementations9 Feb 2024 Xingxuan Zhang, Jiansheng Li, Wenjing Chu, Junjia Hai, Renzhe Xu, Yuqing Yang, Shikai Guan, Jiazheng Xu, Peng Cui

We investigate the generalization boundaries of current Multimodal Large Language Models (MLLMs) via comprehensive evaluation under out-of-distribution scenarios and domain-specific tasks.

In-Context Learning Out-of-Distribution Generalization +1

Deep Ensembles Meets Quantile Regression: Uncertainty-aware Imputation for Time Series

no code implementations3 Dec 2023 Ying Liu, Peng Cui, WenBo Hu, Richang Hong

Score-based diffusion method(i. e., CSDI) is effective for the time series imputation task but computationally expensive due to the nature of the generative diffusion model framework.

Imputation regression +1

Geometry-Calibrated DRO: Combating Over-Pessimism with Free Energy Implications

no code implementations8 Nov 2023 Jiashuo Liu, Jiayun Wu, Tianyu Wang, Hao Zou, Bo Li, Peng Cui

Machine learning algorithms minimizing average risk are susceptible to distributional shifts.

Investigating Uncertainty Calibration of Aligned Language Models under the Multiple-Choice Setting

no code implementations18 Oct 2023 Guande He, Peng Cui, Jianfei Chen, WenBo Hu, Jun Zhu

Despite the significant progress made in practical applications of aligned language models (LMs), they tend to be overconfident in output answers compared to the corresponding pre-trained LMs.


Agents: An Open-source Framework for Autonomous Language Agents

1 code implementation14 Sep 2023 Wangchunshu Zhou, Yuchen Eleanor Jiang, Long Li, Jialong Wu, Tiannan Wang, Shi Qiu, Jintian Zhang, Jing Chen, Ruipu Wu, Shuai Wang, Shiding Zhu, Jiyu Chen, Wentao Zhang, Xiangru Tang, Ningyu Zhang, Huajun Chen, Peng Cui, Mrinmaya Sachan

Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with environments, humans, and other agents using natural language interfaces.

Heterogeneous Multi-Task Gaussian Cox Processes

1 code implementation29 Aug 2023 Feng Zhou, Quyu Kong, Zhijie Deng, Fengxiang He, Peng Cui, Jun Zhu

This paper presents a novel extension of multi-task Gaussian Cox processes for modeling multiple heterogeneous correlated tasks jointly, e. g., classification and regression, via multi-output Gaussian processes (MOGP).

Bayesian Inference Data Augmentation +2

Inductive Meta-path Learning for Schema-complex Heterogeneous Information Networks

no code implementations8 Jul 2023 Shixuan Liu, Changjun Fan, Kewei Cheng, Yunfei Wang, Peng Cui, Yizhou Sun, Zhong Liu

Heterogeneous Information Networks (HINs) are information networks with multiple types of nodes and edges.


Adaptive and Personalized Exercise Generation for Online Language Learning

1 code implementation4 Jun 2023 Peng Cui, Mrinmaya Sachan

We train and evaluate our model on real-world learner interaction data from Duolingo and demonstrate that LMs guided by student states can generate superior exercises.

Knowledge Tracing Text Generation

Competing for Shareable Arms in Multi-Player Multi-Armed Bandits

1 code implementation30 May 2023 Renzhe Xu, Haotian Wang, Xingxuan Zhang, Bo Li, Peng Cui

In reality, agents often have to learn and maximize the rewards of the resources at the same time.

Multi-Armed Bandits

Meta Adaptive Task Sampling for Few-Domain Generalization

no code implementations25 May 2023 Zheyan Shen, Han Yu, Peng Cui, Jiashuo Liu, Xingxuan Zhang, Linjun Zhou, Furui Liu

Moreover, we propose a Meta Adaptive Task Sampling (MATS) procedure to differentiate base tasks according to their semantic and domain-shift similarity to the novel task.

Domain Generalization

Rethinking the Evaluation Protocol of Domain Generalization

no code implementations CVPR 2024 Han Yu, Xingxuan Zhang, Renzhe Xu, Jiashuo Liu, Yue He, Peng Cui

This paper examines the risks of test data information leakage from two aspects of the current evaluation protocol: supervised pretraining on ImageNet and oracle model selection.

Domain Generalization Model Selection

RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text

2 code implementations22 May 2023 Wangchunshu Zhou, Yuchen Eleanor Jiang, Peng Cui, Tiannan Wang, Zhenxin Xiao, Yifan Hou, Ryan Cotterell, Mrinmaya Sachan

In addition to producing AI-generated content (AIGC), we also demonstrate the possibility of using RecurrentGPT as an interactive fiction that directly interacts with consumers.

Language Modelling Large Language Model

Exploring and Exploiting Data Heterogeneity in Recommendation

no code implementations21 May 2023 Zimu Wang, Jiashuo Liu, Hao Zou, Xingxuan Zhang, Yue He, Dongxu Liang, Peng Cui

In this work, we focus on exploring two representative categories of heterogeneity in recommendation data that is the heterogeneity of prediction mechanism and covariate distribution and propose an algorithm that explores the heterogeneity through a bilevel clustering method.

Recommendation Systems

Predictive Heterogeneity: Measures and Applications

no code implementations1 Apr 2023 Jiashuo Liu, Jiayun Wu, Bo Li, Peng Cui

As an intrinsic and fundamental property of big data, data heterogeneity exists in a variety of real-world applications, such as precision medicine, autonomous driving, financial applications, etc.

Autonomous Driving Crop Yield Prediction +3

Gradient Norm Aware Minimization Seeks First-Order Flatness and Improves Generalization

1 code implementation CVPR 2023 Xingxuan Zhang, Renzhe Xu, Han Yu, Hao Zou, Peng Cui

Yet the current definition of flatness discussed in SAM and its follow-ups are limited to the zeroth-order flatness (i. e., the worst-case loss within a perturbation radius).

Confidence-based Reliable Learning under Dual Noises

no code implementations10 Feb 2023 Peng Cui, Yang Yue, Zhijie Deng, Jun Zhu

Deep neural networks (DNNs) have achieved remarkable success in a variety of computer vision tasks, where massive labeled images are routinely required for model optimization.

Model Optimization

Model Agnostic Sample Reweighting for Out-of-Distribution Learning

1 code implementation24 Jan 2023 Xiao Zhou, Yong Lin, Renjie Pi, Weizhong Zhang, Renzhe Xu, Peng Cui, Tong Zhang

The overfitting issue is addressed by considering a bilevel formulation to search for the sample reweighting, in which the generalization complexity depends on the search space of sample weights instead of the model size.

Stable Learning via Sparse Variable Independence

no code implementations2 Dec 2022 Han Yu, Peng Cui, Yue He, Zheyan Shen, Yong Lin, Renzhe Xu, Xingxuan Zhang

The problem of covariate-shift generalization has attracted intensive research attention.

Variable Selection

Neural Eigenfunctions Are Structured Representation Learners

1 code implementation23 Oct 2022 Zhijie Deng, Jiaxin Shi, Hao Zhang, Peng Cui, Cewu Lu, Jun Zhu

Unlike prior spectral methods such as Laplacian Eigenmap that operate in a nonparametric manner, Neural Eigenmap leverages NeuralEF to parametrically model eigenfunctions using a neural network.

Contrastive Learning Data Augmentation +7

Product Ranking for Revenue Maximization with Multiple Purchases

1 code implementation15 Oct 2022 Renzhe Xu, Xingxuan Zhang, Bo Li, Yafeng Zhang, Xiaolong Chen, Peng Cui

In this paper, we assume that each consumer can purchase multiple products at will.

NICO++: Towards Better Benchmarking for Domain Generalization

2 code implementations CVPR 2023 Xingxuan Zhang, Yue He, Renzhe Xu, Han Yu, Zheyan Shen, Peng Cui

Most current evaluation methods for domain generalization (DG) adopt the leave-one-out strategy as a compromise on the limited number of domains.

Benchmarking Domain Generalization +2

Towards Domain Generalization in Object Detection

no code implementations27 Mar 2022 Xingxuan Zhang, Zekai Xu, Renzhe Xu, Jiashuo Liu, Peng Cui, Weitao Wan, Chong Sun, Chen Li

Despite the striking performance achieved by modern detectors when training and test data are sampled from the same or similar distribution, the generalization ability of detectors under unknown distribution shifts remains hardly studied.

Domain Generalization Object +2

ZIN: When and How to Learn Invariance Without Environment Partition?

1 code implementation11 Mar 2022 Yong Lin, Shengyu Zhu, Lu Tan, Peng Cui

When data are divided into distinct environments according to the heterogeneity, recent invariant learning methods have proposed to learn robust and invariant models based on this environment partition.

Regulatory Instruments for Fair Personalized Pricing

1 code implementation9 Feb 2022 Renzhe Xu, Xingxuan Zhang, Peng Cui, Bo Li, Zheyan Shen, Jiazheng Xu

Personalized pricing is a business strategy to charge different prices to individual consumers based on their characteristics and behaviors.

CausPref: Causal Preference Learning for Out-of-Distribution Recommendation

1 code implementation8 Feb 2022 Yue He, Zimu Wang, Peng Cui, Hao Zou, Yafeng Zhang, Qiang Cui, Yong Jiang

In spite of the tremendous development of recommender system owing to the progressive capability of machine learning recently, the current recommender system is still vulnerable to the distribution shift of users and items in realistic scenarios, leading to the sharp decline of performance in testing environments.

Recommendation Systems

Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation

no code implementations5 Feb 2022 Xiangmeng Wang, Qian Li, Dianer Yu, Peng Cui, Zhichao Wang, Guandong Xu

Traditional recommendation models trained on observational interaction data have generated large impacts in a wide range of applications, it faces bias problems that cover users' true intent and thus deteriorate the recommendation effectiveness.


Revisiting Transformation Invariant Geometric Deep Learning: Are Initial Representations All You Need?

no code implementations23 Dec 2021 Ziwei Zhang, Xin Wang, Zeyang Zhang, Peng Cui, Wenwu Zhu

Based on the experimental results, we advocate that TinvNN should be considered a new starting point and an essential baseline for further studies of transformation-invariant geometric deep learning.

Combinatorial Optimization Graph Neural Network +1

Integrated Latent Heterogeneity and Invariance Learning in Kernel Space

no code implementations NeurIPS 2021 Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, Zheyan Shen

The ability to generalize under distributional shifts is essential to reliable machine learning, while models optimized with empirical risk minimization usually fail on non-$i. i. d$ testing data.

Generalizing Graph Neural Networks on Out-Of-Distribution Graphs

1 code implementation20 Nov 2021 Shaohua Fan, Xiao Wang, Chuan Shi, Peng Cui, Bai Wang

Graph Neural Networks (GNNs) are proposed without considering the agnostic distribution shifts between training and testing graphs, inducing the degeneration of the generalization ability of GNNs on Out-Of-Distribution (OOD) settings.

Causal Inference

A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization

1 code implementation3 Nov 2021 Renzhe Xu, Xingxuan Zhang, Zheyan Shen, Tong Zhang, Peng Cui

Afterward, we prove that under ideal conditions, independence-driven importance weighting algorithms could identify the variables in this set.

feature selection

Kernelized Heterogeneous Risk Minimization

1 code implementation24 Oct 2021 Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, Zheyan Shen

The ability to generalize under distributional shifts is essential to reliable machine learning, while models optimized with empirical risk minimization usually fail on non-$i. i. d$ testing data.

Towards Out-Of-Distribution Generalization: A Survey

no code implementations31 Aug 2021 Jiashuo Liu, Zheyan Shen, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, Peng Cui

This paper represents the first comprehensive, systematic review of OOD generalization, encompassing a spectrum of aspects from problem definition, methodological development, and evaluation procedures, to the implications and future directions of the field.

Out-of-Distribution Generalization Representation Learning

Towards Unsupervised Domain Generalization

no code implementations CVPR 2022 Xingxuan Zhang, Linjun Zhou, Renzhe Xu, Peng Cui, Zheyan Shen, Haoxin Liu

Domain generalization (DG) aims to help models trained on a set of source domains generalize better on unseen target domains.

Domain Generalization Representation Learning

Distributionally Robust Learning with Stable Adversarial Training

no code implementations30 Jun 2021 Jiashuo Liu, Zheyan Shen, Peng Cui, Linjun Zhou, Kun Kuang, Bo Li

In this paper, we propose a novel Stable Adversarial Learning (SAL) algorithm that leverages heterogeneous data sources to construct a more practical uncertainty set and conduct differentiated robustness optimization, where covariates are differentiated according to the stability of their correlations with the target.

Context-Aware Attention-Based Data Augmentation for POI Recommendation

no code implementations30 Jun 2021 Yang Li, Yadan Luo, Zheng Zhang, Shazia W. Sadiq, Peng Cui

It aims at suggesting the next POI to a user in spatial and temporal context, which is a practical yet challenging task in various applications.

Data Augmentation Decoder

Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge

no code implementations26 May 2021 Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Xin Wang, Wenwu Zhu, Junzhou Huang

We investigate the theoretical connections between graph signal processing and graph embedding models and formulate the graph embedding model as a general graph signal process with a corresponding graph filter.

Adversarial Attack Graph Embedding +1

Heterogeneous Risk Minimization

1 code implementation9 May 2021 Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, Zheyan Shen

In this paper, we propose Heterogeneous Risk Minimization (HRM) framework to achieve joint learning of latent heterogeneity among the data and invariant relationship, which leads to stable prediction despite distributional shifts.

Deep Stable Learning for Out-Of-Distribution Generalization

2 code implementations CVPR 2021 Xingxuan Zhang, Peng Cui, Renzhe Xu, Linjun Zhou, Yue He, Zheyan Shen

Approaches based on deep neural networks have achieved striking performance when testing data and training data share similar distribution, but can significantly fail otherwise.

Domain Generalization Out-of-Distribution Generalization

Reinforcement Learning with a Disentangled Universal Value Function for Item Recommendation

no code implementations7 Apr 2021 Kai Wang, Zhene Zou, Qilin Deng, Runze Wu, Jianrong Tao, Changjie Fan, Liang Chen, Peng Cui

As a part of the value function, free from the sparse and high-variance reward signals, a high-capacity reward-independent world model is trained to simulate complex environmental dynamics under a certain goal.

Model-based Reinforcement Learning Recommendation Systems +2

Accurate and Reliable Forecasting using Stochastic Differential Equations

no code implementations28 Mar 2021 Peng Cui, Zhijie Deng, WenBo Hu, Jun Zhu

It is critical yet challenging for deep learning models to properly characterize uncertainty that is pervasive in real-world environments.

Prediction Intervals Uncertainty Quantification

Improving Accuracy and Diversity in Matching of Recommendation with Diversified Preference Network

no code implementations7 Feb 2021 Ruobing Xie, Qi Liu, Shukai Liu, Ziwei Zhang, Peng Cui, Bo Zhang, Leyu Lin

In this paper, we propose a novel Heterogeneous graph neural network framework for diversified recommendation (GraphDR) in matching to improve both recommendation accuracy and diversity.

Graph Attention Graph Neural Network +1

Interpreting and Unifying Graph Neural Networks with An Optimization Framework

1 code implementation28 Jan 2021 Meiqi Zhu, Xiao Wang, Chuan Shi, Houye Ji, Peng Cui

Graph Neural Networks (GNNs) have received considerable attention on graph-structured data learning for a wide variety of tasks.

Sample Balancing for Improving Generalization under Distribution Shifts

no code implementations1 Jan 2021 Xingxuan Zhang, Peng Cui, Renzhe Xu, Yue He, Linjun Zhou, Zheyan Shen

We propose to address this problem by removing the dependencies between features via reweighting training samples, which results in a more balanced distribution and helps deep models get rid of spurious correlations and, in turn, concentrate more on the true connection between features and labels.

Domain Adaptation Object Recognition

Counterfactual Prediction for Bundle Treatment

no code implementations NeurIPS 2020 Hao Zou, Peng Cui, Bo Li, Zheyan Shen, Jianxin Ma, Hongxia Yang, Yue He

Estimating counterfactual outcome of different treatments from observational data is an important problem to assist decision making in a variety of fields.

counterfactual Decision Making +2

On the Equivalence of Decoupled Graph Convolution Network and Label Propagation

1 code implementation23 Oct 2020 Hande Dong, Jiawei Chen, Fuli Feng, Xiangnan He, Shuxian Bi, Zhaolin Ding, Peng Cui

The original design of Graph Convolution Network (GCN) couples feature transformation and neighborhood aggregation for node representation learning.

Node Classification Pseudo Label +1

Adversarial Eigen Attack on Black-Box Models

no code implementations CVPR 2022 Linjun Zhou, Peng Cui, Yinan Jiang, Shiqiang Yang

In this paper, we propose a novel setting of transferable black-box attack: attackers may use external information from a pre-trained model with available network parameters, however, different from previous studies, no additional training data is permitted to further change or tune the pre-trained model.

Adversarial Attack

Disentangled Self-Supervision in Sequential Recommenders

1 code implementation23 Aug 2020 Jianxin Ma, Chang Zhou, Hongxia Yang, Peng Cui, Xin Wang, Wenwu Zhu

There exist two challenges: i) reconstructing a future sequence containing many behaviors is exponentially harder than reconstructing a single next behavior, which can lead to difficulty in convergence, and ii) the sequence of all future behaviors can involve many intentions, not all of which may be predictable from the sequence of earlier behaviors.


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

Algorithmic Decision Making with Conditional Fairness

1 code implementation18 Jun 2020 Renzhe Xu, Peng Cui, Kun Kuang, Bo Li, Linjun Zhou, Zheyan Shen, Wei Cui

In practice, there frequently exist a certain set of variables we term as fair variables, which are pre-decision covariates such as users' choices.

Decision Making Fairness

Stable Prediction via Leveraging Seed Variable

no code implementations9 Jun 2020 Kun Kuang, Bo Li, Peng Cui, Yue Liu, Jianrong Tao, Yueting Zhuang, Fei Wu

By assuming the relationships between causal variables and response variable are invariant across data, to address this problem, we propose a conditional independence test based algorithm to separate those causal variables with a seed variable as priori, and adopt them for stable prediction.

Stable Adversarial Learning under Distributional Shifts

no code implementations8 Jun 2020 Jiashuo Liu, Zheyan Shen, Peng Cui, Linjun Zhou, Kun Kuang, Bo Li, Yishi Lin

Machine learning algorithms with empirical risk minimization are vulnerable under distributional shifts due to the greedy adoption of all the correlations found in training data.

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

A Semi-supervised Graph Attentive Network for Financial Fraud Detection

1 code implementation28 Feb 2020 Daixin Wang, Jianbin Lin, Peng Cui, Quanhui Jia, Zhen Wang, Yanming Fang, Quan Yu, Jun Zhou, Shuang Yang, Yuan Qi

Additionally, among the network, only very few of the users are labelled, which also poses a great challenge for only utilizing labeled data to achieve a satisfied performance on fraud detection.

Fraud Detection Graph Neural Network

Structural Deep Clustering Network

2 code implementations5 Feb 2020 Deyu Bo, Xiao Wang, Chuan Shi, Meiqi Zhu, Emiao Lu, Peng Cui

The strength of deep clustering methods is to extract the useful representations from the data itself, rather than the structure of data, which receives scarce attention in representation learning.

Clustering Deep Clustering +1

Stable Prediction with Model Misspecification and Agnostic Distribution Shift

no code implementations31 Jan 2020 Kun Kuang, Ruoxuan Xiong, Peng Cui, Susan Athey, Bo Li

Then, these weights are used in the weighted regression to improve the accuracy of estimation on the effect of each variable, thus help to improve the stability of prediction across unknown test data.


Deep Learning for Learning Graph Representations

no code implementations2 Jan 2020 Wenwu Zhu, Xin Wang, Peng Cui

Mining graph data has become a popular research topic in computer science and has been widely studied in both academia and industry given the increasing amount of network data in the recent years.

Network Embedding

Stable Learning via Sample Reweighting

no code implementations28 Nov 2019 Zheyan Shen, Peng Cui, Tong Zhang, Kun Kuang

We consider the problem of learning linear prediction models with model misspecification bias.

Variable Selection

Rule-Guided Compositional Representation Learning on Knowledge Graphs

1 code implementation20 Nov 2019 Guanglin Niu, Yongfei Zhang, Bo Li, Peng Cui, Si Liu, Jingyang Li, Xiaowei Zhang

Representation learning on a knowledge graph (KG) is to embed entities and relations of a KG into low-dimensional continuous vector spaces.

Knowledge Graphs Representation Learning

Learning Disentangled Representations for Recommendation

no code implementations NeurIPS 2019 Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, Wenwu Zhu

Our approach achieves macro disentanglement by inferring the high-level concepts associated with user intentions (e. g., to buy a shirt or a cellphone), while capturing the preference of a user regarding the different concepts separately.

Decision Making Disentanglement +1

A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models

1 code implementation4 Aug 2019 Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Wenwu Zhu, Junzhou Huang

To this end, we begin by investigating the theoretical connections between graph signal processing and graph embedding models in a principled way and formulate the graph embedding model as a general graph signal process with corresponding graph filter.

Adversarial Attack Graph Embedding +2

Towards Non-I.I.D. Image Classification: A Dataset and Baselines

no code implementations7 Jun 2019 Yue He, Zheyan Shen, Peng Cui

The experimental results demonstrate that NICO can well support the training of ConvNet model from scratch, and a batch balancing module can help ConvNets to perform better in Non-I. I. D.

Classification General Classification +1

Heterogeneous Graph Attention Network

3 code implementations WWW 2019 2019 Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, P. Yu, Yanfang Ye

With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered.

Social and Information Networks

Disparity-preserved Deep Cross-platform Association for Cross-platform Video Recommendation

no code implementations1 Jan 2019 Shengze Yu, Xin Wang, Wenwu Zhu, Peng Cui, Jingdong Wang

However, there remain two unsolved challenges: i) there exist inconsistencies in cross-platform association due to platform-specific disparity, and ii) data from distinct platforms may have different semantic granularities.

Deep Learning on Graphs: A Survey

1 code implementation11 Dec 2018 Ziwei Zhang, Peng Cui, Wenwu Zhu

Recently, substantial research efforts have been devoted to applying deep learning methods to graphs, resulting in beneficial advances in graph analysis techniques.

Collaborative Learning for Extremely Low Bit Asymmetric Hashing

1 code implementation25 Sep 2018 Yadan Luo, Zi Huang, Yang Li, Fumin Shen, Yang Yang, Peng Cui

Hashing techniques are in great demand for a wide range of real-world applications such as image retrieval and network compression.

Image Retrieval Retrieval

Stable Prediction across Unknown Environments

no code implementations16 Jun 2018 Kun Kuang, Ruoxuan Xiong, Peng Cui, Susan Athey, Bo Li

In this paper, we propose a novel Deep Global Balancing Regression (DGBR) algorithm to jointly optimize a deep auto-encoder model for feature selection and a global balancing model for stable prediction across unknown environments.

feature selection

Billion-scale Network Embedding with Iterative Random Projection

2 code implementations7 May 2018 Ziwei Zhang, Peng Cui, Haoyang Li, Xiao Wang, Wenwu Zhu

Network embedding, which learns low-dimensional vector representation for nodes in the network, has attracted considerable research attention recently.

Distributed Computing Link Prediction +2

Structural Deep Embedding for Hyper-Networks

1 code implementation28 Nov 2017 Ke Tu, Peng Cui, Xiao Wang, Fei Wang, Wenwu Zhu

These hyper-networks pose great challenges to existing network embedding methods when the hyperedges are indecomposable, that is to say, any subset of nodes in a hyperedge cannot form another hyperedge.

Social and Information Networks

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

Learning to Learn Image Classifiers with Visual Analogy

no code implementations CVPR 2019 Linjun Zhou, Peng Cui, Shiqiang Yang, Wenwu Zhu, Qi Tian

We then propose an out-of-sample embedding method to learn the embedding of a new class represented by a few samples through its visual analogy with base classes and derive the classification parameters for the new class.

Classification General Classification +1

Causally Regularized Learning with Agnostic Data Selection Bias

no code implementations22 Aug 2017 Zheyan Shen, Peng Cui, Kun Kuang, Bo Li, Peixuan Chen

However, this ideal assumption is often violated in real applications, where selection bias may arise between training and testing process.

regression Selection bias +1

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

From Micro to Macro: Uncovering and Predicting Information Cascading Process with Behavioral Dynamics

no code implementations27 May 2015 Linyun Yu, Peng Cui, Fei Wang, Chaoming Song, Shiqiang Yang

As cascades are typical dynamic processes, it is always interesting and important to predict the cascade size at any time, or predict the time when a cascade will reach a certain size (e. g. an threshold for outbreak).

Social and Information Networks Physics and Society

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