Search Results for author: Kun Kuang

Found 59 papers, 18 papers with code

De-Biased Court's View Generation with Causality

no code implementations EMNLP 2020 Yiquan Wu, Kun Kuang, Yating Zhang, Xiaozhong Liu, Changlong Sun, Jun Xiao, Yueting Zhuang, Luo Si, Fei Wu

Court{'}s view generation is a novel but essential task for legal AI, aiming at improving the interpretability of judgment prediction results and enabling automatic legal document generation.

Text Generation

Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalization

no code implementations25 May 2023 Yunze Tong, Junkun Yuan, Min Zhang, Didi Zhu, Keli Zhang, Fei Wu, Kun Kuang

With contrastive learning, we propose a learning potential-guided metric for domain heterogeneity by promoting learning variant features.

Contrastive Learning Domain Generalization

Generalized Universal Domain Adaptation with Generative Flow Networks

no code implementations8 May 2023 Didi Zhu, Yinchuan Li, Yunfeng Shao, Jianye Hao, Fei Wu, Kun Kuang, Jun Xiao, Chao Wu

We introduce a new problem in unsupervised domain adaptation, termed as Generalized Universal Domain Adaptation (GUDA), which aims to achieve precise prediction of all target labels including unknown categories.

Universal Domain Adaptation Unsupervised Domain Adaptation

Universal Domain Adaptation via Compressive Attention Matching

no code implementations24 Apr 2023 Didi Zhu, Yincuan Li, Junkun Yuan, Zexi Li, Yunfeng Shao, Kun Kuang, Chao Wu

To address this issue, we propose a Universal Attention Matching (UniAM) framework by exploiting the self-attention mechanism in vision transformer to capture the crucial object information.

Universal Domain Adaptation

IDEAL: Toward High-efficiency Device-Cloud Collaborative and Dynamic Recommendation System

no code implementations14 Feb 2023 Zheqi Lv, Zhengyu Chen, Shengyu Zhang, Kun Kuang, Wenqiao Zhang, Mengze Li, Beng Chin Ooi, Fei Wu

The aforementioned two trends enable the device-cloud collaborative and dynamic recommendation, which deeply exploits the recommendation pattern among cloud-device data and efficiently characterizes different instances with different underlying distributions based on the cost of frequent device-cloud communication.

Recommendation Systems Vocal Bursts Intensity Prediction

Adaptive Value Decomposition with Greedy Marginal Contribution Computation for Cooperative Multi-Agent Reinforcement Learning

1 code implementation14 Feb 2023 Shanqi Liu, Yujing Hu, Runze Wu, Dong Xing, Yu Xiong, Changjie Fan, Kun Kuang, Yong liu

We first illustrate that the proposed value decomposition can consider the complicated interactions among agents and is feasible to learn in large-scale scenarios.

Multi-agent Reinforcement Learning

Instrumental Variables in Causal Inference and Machine Learning: A Survey

1 code implementation12 Dec 2022 Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Fei Wu

This paper serves as the first effort to systematically and comprehensively introduce and discuss the IV methods and their applications in both causal inference and machine learning.

Causal Inference

ConfounderGAN: Protecting Image Data Privacy with Causal Confounder

no code implementations4 Dec 2022 Qi Tian, Kun Kuang, Kelu Jiang, Furui Liu, Zhihua Wang, Fei Wu

The success of deep learning is partly attributed to the availability of massive data downloaded freely from the Internet.

Image Classification

Confounder Balancing for Instrumental Variable Regression with Latent Variable

no code implementations18 Nov 2022 Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Bo Li, Fei Wu

This paper studies the confounding effects from the unmeasured confounders and the imbalance of observed confounders in IV regression and aims at unbiased causal effect estimation.


Exploiting Contrastive Learning and Numerical Evidence for Improving Confusing Legal Judgment Prediction

no code implementations15 Nov 2022 Leilei Gan, Baokui Li, Kun Kuang, Yi Yang, Fei Wu

Given the fact description text of a legal case, legal judgment prediction (LJP) aims to predict the case's charge, law article and penalty term.

Contrastive Learning

Learning Individual Treatment Effects under Heterogeneous Interference in Networks

no code implementations25 Oct 2022 Ziyu Zhao, Kun Kuang, Ruoxuan Xiong, Fei Wu

In network data, due to interference, the outcome of a unit is influenced not only by its treatment (i. e., direct effects) but also by others' treatments (i. e., spillover effects).

Investigating the Robustness of Natural Language Generation from Logical Forms via Counterfactual Samples

1 code implementation16 Oct 2022 Chengyuan Liu, Leilei Gan, Kun Kuang, Fei Wu

To verify this hypothesis, we manually construct a set of counterfactual samples, which modify the original logical forms to generate counterfactual logical forms with rarely co-occurred table headers and logical operators.

Logical Reasoning Text Generation

Domain Generalization via Contrastive Causal Learning

no code implementations6 Oct 2022 Qiaowei Miao, Junkun Yuan, Kun Kuang

Specifically, CCM is composed of three components: (i) domain-conditioned supervised learning which teaches CCM the correlation between images and labels, (ii) causal effect learning which helps CCM measure the true causal effects of images to labels, (iii) contrastive similarity learning which clusters the features of images that belong to the same class and provides the quantification of similarity.

Domain Generalization

DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization

no code implementations12 Sep 2022 Zheqi Lv, Wenqiao Zhang, Shengyu Zhang, Kun Kuang, Feng Wang, Yongwei Wang, Zhengyu Chen, Tao Shen, Hongxia Yang, Beng Chin Ooi, Fei Wu

DUET is deployed on a powerful cloud server that only requires the low cost of forwarding propagation and low time delay of data transmission between the device and the cloud.

Model Compression

Learning Instrumental Variable from Data Fusion for Treatment Effect Estimation

1 code implementation23 Aug 2022 Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Minqing Zhu, Yuxuan Liu, Bo Li, Furui Liu, Zhihua Wang, Fei Wu

The advent of the big data era brought new opportunities and challenges to draw treatment effect in data fusion, that is, a mixed dataset collected from multiple sources (each source with an independent treatment assignment mechanism).


Personalizing Intervened Network for Long-tailed Sequential User Behavior Modeling

no code implementations19 Aug 2022 Zheqi Lv, Feng Wang, Shengyu Zhang, Kun Kuang, Hongxia Yang, Fei Wu

In this paper, we propose a novel approach that significantly improves the recommendation performance of the tail users while achieving at least comparable performance for the head users over the base model.

Recommendation Systems

Label-Efficient Domain Generalization via Collaborative Exploration and Generalization

no code implementations7 Aug 2022 Junkun Yuan, Xu Ma, Defang Chen, Kun Kuang, Fei Wu, Lanfen Lin

To escape from the dilemma between domain generalization and annotation costs, in this paper, we introduce a novel task named label-efficient domain generalization (LEDG) to enable model generalization with label-limited source domains.

Domain Generalization

S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?

no code implementations20 Jun 2022 Shuang Luo, Yinchuan Li, Jiahui Li, Kun Kuang, Furui Liu, Yunfeng Shao, Chao Wu

To this end, we propose a sparse state based MARL (S2RL) framework, which utilizes a sparse attention mechanism to discard irrelevant information in local observations.

Multi-agent Reinforcement Learning Reinforcement Learning (RL) +2

Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning

no code implementations7 Jun 2022 Jiannan Guo, Yangyang Kang, Yu Duan, Xiaozhong Liu, Siliang Tang, Wenqiao Zhang, Kun Kuang, Changlong Sun, Fei Wu

Motivated by the industry practice of labeling data, we propose an innovative Inconsistency-based virtual aDvErsarial Active Learning (IDEAL) algorithm to further investigate SSL-AL's potential superiority and achieve mutual enhancement of AL and SSL, i. e., SSL propagates label information to unlabeled samples and provides smoothed embeddings for AL, while AL excludes samples with inconsistent predictions and considerable uncertainty for SSL.

Active Learning

Debiased Graph Neural Networks with Agnostic Label Selection Bias

no code implementations19 Jan 2022 Shaohua Fan, Xiao Wang, Chuan Shi, Kun Kuang, Nian Liu, Bai Wang

Then to remove the bias in GNN estimation, we propose a novel Debiased Graph Neural Networks (DGNN) with a differentiated decorrelation regularizer.

Selection bias

Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI

1 code implementation11 Nov 2021 Jiangchao Yao, Shengyu Zhang, Yang Yao, Feng Wang, Jianxin Ma, Jianwei Zhang, Yunfei Chu, Luo Ji, Kunyang Jia, Tao Shen, Anpeng Wu, Fengda Zhang, Ziqi Tan, Kun Kuang, Chao Wu, Fei Wu, Jingren Zhou, Hongxia Yang

However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed.


Unified Group Fairness on Federated Learning

no code implementations9 Nov 2021 Fengda Zhang, Kun Kuang, Yuxuan Liu, Long Chen, Chao Wu, Fei Wu, Jiaxun Lu, Yunfeng Shao, Jun Xiao

We validate the advantages of the FMDA-M algorithm with various kinds of distribution shift settings in experiments, and the results show that FMDA-M algorithm outperforms the existing fair FL algorithms on unified group fairness.

Fairness Federated Learning

Dialogue Inspectional Summarization with Factual Inconsistency Awareness

no code implementations5 Nov 2021 Leilei Gan, Yating Zhang, Kun Kuang, Lin Yuan, Shuo Li, Changlong Sun, Xiaozhong Liu, Fei Wu

Dialogue summarization has been extensively studied and applied, where the prior works mainly focused on exploring superior model structures to align the input dialogue and the output summary.

dialogue summary Medical Diagnosis

Collaborative Semantic Aggregation and Calibration for Federated Domain Generalization

1 code implementation13 Oct 2021 Junkun Yuan, Xu Ma, Defang Chen, Fei Wu, Lanfen Lin, Kun Kuang

Domain generalization (DG) aims to learn from multiple known source domains a model that can generalize well to unknown target domains.

Domain Generalization

Stable Prediction on Graphs with Agnostic Distribution Shift

no code implementations8 Oct 2021 Shengyu Zhang, Kun Kuang, Jiezhong Qiu, Jin Yu, Zhou Zhao, Hongxia Yang, Zhongfei Zhang, Fei Wu

The results demonstrate that our method outperforms various SOTA GNNs for stable prediction on graphs with agnostic distribution shift, including shift caused by node labels and attributes.

Graph Learning Recommendation Systems

Instrumental Variable-Driven Domain Generalization with Unobserved Confounders

no code implementations4 Oct 2021 Junkun Yuan, Xu Ma, Ruoxuan Xiong, Mingming Gong, Xiangyu Liu, Fei Wu, Lanfen Lin, Kun Kuang

Meanwhile, the existing of unobserved confounders which affect the input features and labels simultaneously cause spurious correlation and hinder the learning of the invariant relationship contained in the conditional distribution.

Domain Generalization

Domain-Specific Bias Filtering for Single Labeled Domain Generalization

1 code implementation2 Oct 2021 Junkun Yuan, Xu Ma, Defang Chen, Kun Kuang, Fei Wu, Lanfen Lin

In this paper, we investigate a Single Labeled Domain Generalization (SLDG) task with only one source domain being labeled, which is more practical and challenging than the CDG task.

Domain Generalization

Minimizing Memorization in Meta-learning: A Causal Perspective

no code implementations29 Sep 2021 Yinjie Jiang, Zhengyu Chen, Luotian Yuan, Ying WEI, Kun Kuang, Xinhai Ye, Zhihua Wang, Fei Wu

Meta-learning has emerged as a potent paradigm for quick learning of few-shot tasks, by leveraging the meta-knowledge learned from meta-training tasks.

Causal Inference Memorization +1

Treatment effect estimation with confounder balanced instrumental variable regression

no code implementations29 Sep 2021 Anpeng Wu, Kun Kuang, Fei Wu

In this paper, we propose a Confounder Balanced IV Regression (CB-IV) algorithm to jointly remove the bias from the unmeasured confounders with IV regression and reduce the bias from the observed confounders by balancing for treatment effect estimation.


Instance-wise or Class-wise? A Tale of Neighbor Shapley for Concept-based Explanation

no code implementations3 Sep 2021 Jiahui Li, Kun Kuang, Lin Li, Long Chen, Songyang Zhang, Jian Shao, Jun Xiao

Deep neural networks have demonstrated remarkable performance in many data-driven and prediction-oriented applications, and sometimes even perform better than humans.

Medical Diagnosis

Auto IV: Counterfactual Prediction via Automatic Instrumental Variable Decomposition

1 code implementation13 Jul 2021 Junkun Yuan, Anpeng Wu, Kun Kuang, Bo Li, Runze Wu, Fei Wu, Lanfen Lin

We also learn confounder representations by encouraging them to be relevant to both the treatment and the outcome.

Causal Inference

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.

Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning

no code implementations1 Jun 2021 Jiahui Li, Kun Kuang, Baoxiang Wang, Furui Liu, Long Chen, Fei Wu, Jun Xiao

Specifically, Shapley Value and its desired properties are leveraged in deep MARL to credit any combinations of agents, which grants us the capability to estimate the individual credit for each agent.

Multi-agent Reinforcement Learning reinforcement-learning +3

Analysis and Applications of Class-wise Robustness in Adversarial Training

no code implementations29 May 2021 Qi Tian, Kun Kuang, Kelu Jiang, Fei Wu, Yisen Wang

Adversarial training is one of the most effective approaches to improve model robustness against adversarial examples.

Dependency Parsing as MRC-based Span-Span Prediction

2 code implementations ACL 2022 Leilei Gan, Yuxian Meng, Kun Kuang, Xiaofei Sun, Chun Fan, Fei Wu, Jiwei Li

The proposed method has the following merits: (1) it addresses the fundamental problem that edges in a dependency tree should be constructed between subtrees; (2) the MRC framework allows the method to retrieve missing spans in the span proposal stage, which leads to higher recall for eligible spans.

Dependency Parsing Machine Reading Comprehension

BertGCN: Transductive Text Classification by Combining GCN and BERT

1 code implementation12 May 2021 Yuxiao Lin, Yuxian Meng, Xiaofei Sun, Qinghong Han, Kun Kuang, Jiwei Li, Fei Wu

In this work, we propose BertGCN, a model that combines large scale pretraining and transductive learning for text classification.

text-classification Text Classification +1

Intriguing class-wise properties of adversarial training

no code implementations1 Jan 2021 Qi Tian, Kun Kuang, Fei Wu, Yisen Wang

Adversarial training is one of the most effective approaches to improve model robustness against adversarial examples.

Adversarial Robustness

Semi-Supervised Active Learning for Semi-Supervised Models: Exploit Adversarial Examples With Graph-Based Virtual Labels

no code implementations ICCV 2021 Jiannan Guo, Haochen Shi, Yangyang Kang, Kun Kuang, Siliang Tang, Zhuoren Jiang, Changlong Sun, Fei Wu, Yueting Zhuang

Although current mainstream methods begin to combine SSL and AL (SSL-AL) to excavate the diverse expressions of unlabeled samples, these methods' fully supervised task models are still trained only with labeled data.

Active Learning

Interventional Domain Adaptation

no code implementations7 Nov 2020 Jun Wen, Changjian Shui, Kun Kuang, Junsong Yuan, Zenan Huang, Zhefeng Gong, Nenggan Zheng

To address this issue, we intervene in the learning of feature discriminability using unlabeled target data to guide it to get rid of the domain-specific part and be safely transferable.

Unsupervised Domain Adaptation

Federated Unsupervised Representation Learning

no code implementations18 Oct 2020 Fengda Zhang, Kun Kuang, Zhaoyang You, Tao Shen, Jun Xiao, Yin Zhang, Chao Wu, Yueting Zhuang, Xiaolin Li

FURL poses two new challenges: (1) data distribution shift (Non-IID distribution) among clients would make local models focus on different categories, leading to the inconsistency of representation spaces.

Federated Learning Representation Learning

DeVLBert: Learning Deconfounded Visio-Linguistic Representations

1 code implementation16 Aug 2020 Shengyu Zhang, Tan Jiang, Tan Wang, Kun Kuang, Zhou Zhao, Jianke Zhu, Jin Yu, Hongxia Yang, Fei Wu

In this paper, we propose to investigate the problem of out-of-domain visio-linguistic pretraining, where the pretraining data distribution differs from that of downstream data on which the pretrained model will be fine-tuned.

Image Retrieval Question Answering +2

Poet: Product-oriented Video Captioner for E-commerce

1 code implementation16 Aug 2020 Shengyu Zhang, Ziqi Tan, Jin Yu, Zhou Zhao, Kun Kuang, Jie Liu, Jingren Zhou, Hongxia Yang, Fei Wu

Then, based on the aspects of the video-associated product, we perform knowledge-enhanced spatial-temporal inference on those graphs for capturing the dynamic change of fine-grained product-part characteristics.

Video Captioning

MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction

no code implementations13 Aug 2020 Yufei Feng, Fuyu Lv, Binbin Hu, Fei Sun, Kun Kuang, Yang Liu, Qingwen Liu, Wenwu Ou

In this paper, we propose a new framework named Multiplex Target-Behavior Relation enhanced Network (MTBRN) to leverage multiplex relations between user behaviors and target item to enhance CTR prediction.

Click-Through Rate Prediction Recommendation Systems

Decorrelated Clustering with Data Selection Bias

1 code implementation29 Jun 2020 Xiao Wang, Shaohua Fan, Kun Kuang, Chuan Shi, Jiawei Liu, Bai Wang

Most of existing clustering algorithms are proposed without considering the selection bias in data.

Selection bias

Federated Mutual Learning

1 code implementation27 Jun 2020 Tao Shen, Jie Zhang, Xinkang Jia, Fengda Zhang, Gang Huang, Pan Zhou, Kun Kuang, Fei Wu, Chao Wu

The experiments show that FML can achieve better performance than alternatives in typical FL setting, and clients can be benefited from FML with different models and tasks.

Federated Learning

Comprehensive Information Integration Modeling Framework for Video Titling

1 code implementation24 Jun 2020 Shengyu Zhang, Ziqi Tan, Jin Yu, Zhou Zhao, Kun Kuang, Tan Jiang, Jingren Zhou, Hongxia Yang, Fei Wu

In e-commerce, consumer-generated videos, which in general deliver consumers' individual preferences for the different aspects of certain products, are massive in volume.

Video Captioning

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

Learning Decomposed Representation for Counterfactual Inference

1 code implementation12 Jun 2020 Anpeng Wu, Kun Kuang, Junkun Yuan, Bo Li, Runze Wu, Qiang Zhu, Yueting Zhuang, Fei Wu

The fundamental problem in treatment effect estimation from observational data is confounder identification and balancing.

Counterfactual Inference

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.

Balance-Subsampled Stable Prediction

no code implementations8 Jun 2020 Kun Kuang, Hengtao Zhang, Fei Wu, Yueting Zhuang, Aijun Zhang

However, this assumption is often violated in practice because the sample selection bias may induce the distribution shift from training data to test data.

Selection bias

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.

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.


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

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

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

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