Search Results for author: Chengqi Zhang

Found 51 papers, 28 papers with code

Federated Learning on Non-IID Graphs via Structural Knowledge Sharing

1 code implementation23 Nov 2022 Yue Tan, Yixin Liu, Guodong Long, Jing Jiang, Qinghua Lu, Chengqi Zhang

Inspired by this, we propose FedStar, an FGL framework that extracts and shares the common underlying structure information for inter-graph federated learning tasks.

Federated Learning Graph Learning

Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection

no code implementations29 May 2022 Shaoshen Wang, Yanbin Liu, Ling Chen, Chengqi Zhang

Empirically, DERM outperformed the state-of-the-art on the unsupervised AD benchmark consisting of 18 datasets.

Unsupervised Anomaly Detection

On the Convergence of Clustered Federated Learning

no code implementations13 Feb 2022 Jie Ma, Guodong Long, Tianyi Zhou, Jing Jiang, Chengqi Zhang

Knowledge sharing and model personalization are essential components to tackle the non-IID challenge in federated learning (FL).

Federated Learning

EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning

no code implementations29 Sep 2021 Shuang Ao, Tianyi Zhou, Jing Jiang, Guodong Long, Xuan Song, Chengqi Zhang

They are complementary in acquiring more informative feedback for RL: the planning policy provides dense reward of finishing easier sub-tasks while the environment policy modifies these sub-tasks to be adequately challenging and diverse so the RL agent can quickly adapt to different tasks/environments.


Uncertainty Regularized Policy Learning for Offline Reinforcement Learning

no code implementations29 Sep 2021 Han Zheng, Jing Jiang, Pengfei Wei, Guodong Long, Xuan Song, Chengqi Zhang

URPL adds an uncertainty regularization term in the policy learning objective to enforce to learn a more stable policy under the offline setting.

D4RL Offline RL +1

Goal Randomization for Playing Text-based Games without a Reward Function

no code implementations29 Sep 2021 Meng Fang, Yunqiu Xu, Yali Du, Ling Chen, Chengqi Zhang

In a variety of text-based games, we show that this simple method results in competitive performance for agents.

Decision Making text-based games

Vote for Nearest Neighbors Meta-Pruning of Self-Supervised Networks

no code implementations29 Sep 2021 Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Liming Zhu, Chengqi Zhang

Can we find a better initialization for a new task, e. g., a much smaller network closer to the final pruned model, by exploiting its similar tasks?

Generalization in Text-based Games via Hierarchical Reinforcement Learning

1 code implementation Findings (EMNLP) 2021 Yunqiu Xu, Meng Fang, Ling Chen, Yali Du, Chengqi Zhang

Deep reinforcement learning provides a promising approach for text-based games in studying natural language communication between humans and artificial agents.

Hierarchical Reinforcement Learning reinforcement-learning +1

Federated Learning for Open Banking

no code implementations24 Aug 2021 Guodong Long, Yue Tan, Jing Jiang, Chengqi Zhang

In the near future, it is foreseeable to have decentralized data ownership in the finance sector using federated learning.

Federated Learning

Multi-Center Federated Learning

1 code implementation19 Aug 2021 Ming Xie, Guodong Long, Tao Shen, Tianyi Zhou, Xianzhi Wang, Jing Jiang, Chengqi Zhang

By comparison, a mixture of multiple global models could capture the heterogeneity across various users if assigning the users to different global models (i. e., centers) in FL.

Federated Learning

MIPO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning

1 code implementation20 Jul 2021 Xueping Peng, Guodong Long, Sen Wang, Jing Jiang, Allison Clarke, Clement Schlegel, Chengqi Zhang

Hence, some recent works train healthcare representations by incorporating medical ontology, by self-supervised tasks like diagnosis prediction, but (1) the small-scale, monotonous ontology is insufficient for robust learning, and (2) critical contexts or dependencies underlying patient journeys are barely exploited to enhance ontology learning.

Graph Embedding

FedProto: Federated Prototype Learning across Heterogeneous Clients

2 code implementations1 May 2021 Yue Tan, Guodong Long, Lu Liu, Tianyi Zhou, Qinghua Lu, Jing Jiang, Chengqi Zhang

Heterogeneity across clients in federated learning (FL) usually hinders the optimization convergence and generalization performance when the aggregation of clients' knowledge occurs in the gradient space.

Federated Learning

Isometric Propagation Network for Generalized Zero-shot Learning

no code implementations ICLR 2021 Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Xuanyi Dong, Chengqi Zhang

To resolve this problem, we propose Isometric Propagation Network (IPN), which learns to strengthen the relation between classes within each space and align the class dependency in the two spaces.

Generalized Zero-Shot Learning

MASP: Model-Agnostic Sample Propagation for Few-shot learning

no code implementations1 Jan 2021 Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Xuanyi Dong, Chengqi Zhang

Few-shot learning aims to train a classifier given only a few samples per class that are highly insufficient to describe the whole data distribution.

Few-Shot Learning

Extract Local Inference Chains of Deep Neural Nets

no code implementations1 Jan 2021 Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang

In this paper, we introduce an efficient method, \name, to extract the local inference chains by optimizing a differentiable sparse scoring for the filters and layers to preserve the outputs on given data from a local region.

Interpretable Machine Learning Network Pruning

Towards Coarse and Fine-grained Multi-Graph Multi-Label Learning

no code implementations19 Dec 2020 Yejiang Wang, Yuhai Zhao, Zhengkui Wang, Chengqi Zhang

Multi-graph multi-label learning (\textsc{Mgml}) is a supervised learning framework, which aims to learn a multi-label classifier from a set of labeled bags each containing a number of graphs.

Multi-Label Learning

Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention

2 code implementations COLING 2020 Yang Li, Tao Shen, Guodong Long, Jing Jiang, Tianyi Zhou, Chengqi Zhang

Then, facilitated by the proposed base model, we introduce collaborating relation features shared among relations in the hierarchies to promote the relation-augmenting process and balance the training data for long-tail relations.

Relation Extraction

BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes

1 code implementation24 Sep 2020 Xueping Peng, Guodong Long, Tao Shen, Sen Wang, Jing Jiang, Chengqi Zhang

Electronic health records (EHRs) are longitudinal records of a patient's interactions with healthcare systems.

Attribute Propagation Network for Graph Zero-shot Learning

no code implementations24 Sep 2020 Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang

To address this challenging task, most ZSL methods relate unseen test classes to seen(training) classes via a pre-defined set of attributes that can describe all classes in the same semantic space, so the knowledge learned on the training classes can be adapted to unseen classes.

Meta-Learning Zero-Shot Learning

Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy

1 code implementation28 Jun 2020 Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang

We study many-class few-shot (MCFS) problem in both supervised learning and meta-learning settings.

Few-Shot Learning

Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks

2 code implementations24 May 2020 Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, Chengqi Zhang

Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic.

Graph Learning Multivariate Time Series Forecasting +1

Multi-Center Federated Learning

4 code implementations3 May 2020 Ming Xie, Guodong Long, Tao Shen, Tianyi Zhou, Xianzhi Wang, Jing Jiang, Chengqi Zhang

However, due to the diverse nature of user behaviors, assigning users' gradients to different global models (i. e., centers) can better capture the heterogeneity of data distributions across users.

Federated Learning

Learning to Propagate for Graph Meta-Learning

1 code implementation NeurIPS 2019 Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang

It can significantly improve tasks that suffer from insufficient training data, e. g., few shot learning.

Few-Shot Image Classification

Graph WaveNet for Deep Spatial-Temporal Graph Modeling

7 code implementations31 May 2019 Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Chengqi Zhang

Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system.

Temporal Sequences Traffic Prediction

MahiNet: A Neural Network for Many-Class Few-Shot Learning with Class Hierarchy

no code implementations ICLR 2019 Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang

It addresses the ``many-class'' problem by exploring the class hierarchy, e. g., the coarse-class label that covers a subset of fine classes, which helps to narrow down the candidates for the fine class and is cheaper to obtain.

Few-Shot Learning General Classification

Search Efficient Binary Network Embedding

no code implementations14 Jan 2019 Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang

However, the learned dense vector representations are inefficient for large-scale similarity search, which requires to find the nearest neighbor measured by Euclidean distance in a continuous vector space.

Network Embedding online learning

Attributed Network Embedding via Subspace Discovery

1 code implementation14 Jan 2019 Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang

In this paper, we propose a unified framework for attributed network embedding-attri2vec-that learns node embeddings by discovering a latent node attribute subspace via a network structure guided transformation performed on the original attribute space.

Link Prediction Network Embedding +2

Learning Graph Embedding with Adversarial Training Methods

no code implementations4 Jan 2019 Shirui Pan, Ruiqi Hu, Sai-fu Fung, Guodong Long, Jing Jiang, Chengqi Zhang

Based on this framework, we derive two variants of adversarial models, the adversarially regularized graph autoencoder (ARGA) and its variational version, adversarially regularized variational graph autoencoder (ARVGA), to learn the graph embedding effectively.

Graph Clustering Graph Embedding +2

A Comprehensive Survey on Graph Neural Networks

5 code implementations3 Jan 2019 Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu

In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields.

BIG-bench Machine Learning Image Classification +2

A Review for Weighted MinHash Algorithms

1 code implementation12 Nov 2018 Wei Wu, Bin Li, Ling Chen, Junbin Gao, Chengqi Zhang

In this review, we mainly categorize the Weighted MinHash algorithms into quantization-based approaches, "active index"-based ones and others, and show the evolution and inherent connection of the weighted MinHash algorithms, from the integer weighted MinHash algorithms to real-valued weighted MinHash ones (particularly the Consistent Weighted Sampling scheme).

Data Structures and Algorithms

Binarized Attributed Network Embedding

2 code implementations ICDM 2018 Hong Yang, Shirui Pan, Peng Zhang, Ling Chen, Defu Lian, Chengqi Zhang

To this end, we present a Binarized Attributed Network Embedding model (BANE for short) to learn binary node representation.

Graph Embedding Link Prediction +2

SINE: Scalable Incomplete Network Embedding

2 code implementations16 Oct 2018 Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang

In this paper, we propose a Scalable Incomplete Network Embedding (SINE) algorithm for learning node representations from incomplete graphs.

Social and Information Networks

Tensorized Self-Attention: Efficiently Modeling Pairwise and Global Dependencies Together

2 code implementations NAACL 2019 Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang

Neural networks equipped with self-attention have parallelizable computation, light-weight structure, and the ability to capture both long-range and local dependencies.

Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling

1 code implementation ICLR 2018 Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang

In this paper, we propose a model, called "bi-directional block self-attention network (Bi-BloSAN)", for RNN/CNN-free sequence encoding.

MetaGraph2Vec: Complex Semantic Path Augmented Heterogeneous Network Embedding

no code implementations7 Mar 2018 Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang

Network embedding in heterogeneous information networks (HINs) is a challenging task, due to complications of different node types and rich relationships between nodes.

Social and Information Networks

Adversarially Regularized Graph Autoencoder for Graph Embedding

4 code implementations13 Feb 2018 Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, Chengqi Zhang

Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics.

Graph Clustering Graph Embedding +1

Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling

1 code implementation31 Jan 2018 Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Sen Wang, Chengqi Zhang

In this paper, we integrate both soft and hard attention into one context fusion model, "reinforced self-attention (ReSA)", for the mutual benefit of each other.

Hard Attention Natural Language Inference

Network Representation Learning: A Survey

no code implementations4 Dec 2017 Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang

Network representation learning has been recently proposed as a new learning paradigm to embed network vertices into a low-dimensional vector space, by preserving network topology structure, vertex content, and other side information.

Representation Learning

Deep Learning from Noisy Image Labels with Quality Embedding

no code implementations2 Nov 2017 Jiangchao Yao, Jiajie Wang, Ivor Tsang, Ya zhang, Jun Sun, Chengqi Zhang, Rui Zhang

However, the label noise among the datasets severely degenerates the \mbox{performance of deep} learning approaches.

DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding

1 code implementation14 Sep 2017 Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Shirui Pan, Chengqi Zhang

Recurrent neural nets (RNN) and convolutional neural nets (CNN) are widely used on NLP tasks to capture the long-term and local dependencies, respectively.

Natural Language Inference Sentence Embedding +1

Improved Consistent Weighted Sampling Revisited

1 code implementation5 Jun 2017 Wei Wu, Bin Li, Ling Chen, Chengqi Zhang, Philip S. Yu

Min-Hash is a popular technique for efficiently estimating the Jaccard similarity of binary sets.

Data Structures and Algorithms

Temporal Feature Selection on Networked Time Series

no code implementations20 Dec 2016 Haishuai Wang, Jia Wu, Peng Zhang, Chengqi Zhang

For example, social network users are considered to be social sensors that continuously generate social signals (tweets) represented as a time series.

Time Series Analysis Time Series Classification

Dynamic Concept Composition for Zero-Example Event Detection

no code implementations14 Jan 2016 Xiaojun Chang, Yi Yang, Guodong Long, Chengqi Zhang, Alexander G. Hauptmann

In this paper, we focus on automatically detecting events in unconstrained videos without the use of any visual training exemplars.

Event Detection Zero-Shot Learning

Compound Rank-k Projections for Bilinear Analysis

no code implementations23 Nov 2014 Xiaojun Chang, Feiping Nie, Sen Wang, Yi Yang, Xiaofang Zhou, Chengqi Zhang

In many real-world applications, data are represented by matrices or high-order tensors.

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