no code implementations • 22 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.
no code implementations • 16 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.
no code implementations • 28 Nov 2019 • Zheyan Shen, Peng Cui, Tong Zhang, Kun Kuang
We consider the problem of learning linear prediction models with model misspecification bias.
no code implementations • 31 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.
no code implementations • 8 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.
no code implementations • 8 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.
no code implementations • 9 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.
1 code implementation • 12 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.
1 code implementation • 18 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.
1 code implementation • 24 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.
4 code implementations • 27 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.
1 code implementation • 29 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.
no code implementations • 13 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.
1 code implementation • 16 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.
1 code implementation • 16 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.
no code implementations • 18 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.
no code implementations • 7 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.
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.
no code implementations • 1 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.
1 code implementation • 12 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.
Ranked #1 on Text Classification on 20 Newsgroups
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.
no code implementations • 29 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.
no code implementations • 1 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.
no code implementations • 30 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.
1 code implementation • 13 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.
no code implementations • 3 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.
no code implementations • 29 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.
no code implementations • 29 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.
1 code implementation • 2 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.
no code implementations • 4 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.
no code implementations • 8 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.
1 code implementation • 13 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.
no code implementations • 5 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.
no code implementations • 9 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.
1 code implementation • 11 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.
no code implementations • 20 Dec 2021 • Qi Tian, Kun Kuang, Baoxiang Wang, Furui Liu, Fei Wu
The node information compression aims to address the problem of what to communicate via learning compact node representations.
Multi-agent Reinforcement Learning reinforcement-learning +3
no code implementations • 19 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.
no code implementations • 7 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.
no code implementations • 20 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
no code implementations • 7 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.
no code implementations • 19 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.
1 code implementation • 23 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).
1 code implementation • 12 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.
no code implementations • 6 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.
2 code implementations • 16 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.
no code implementations • 25 Oct 2022 • Ziyu Zhao, Yuqi Bai, 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).
1 code implementation • 15 Nov 2022 • Leilei Gan, Baokui Li, Kun Kuang, Yating Zhang, Lei Wang, Luu Anh Tuan, 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.
no code implementations • 18 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.
no code implementations • 28 Nov 2022 • Qi Tian, Kun Kuang, Furui Liu, Baoxiang Wang
e. g., an agent is a random policy while other agents are medium policies.
no code implementations • 4 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.
1 code implementation • 12 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.
1 code implementation • ICCV 2023 • Min Zhang, Junkun Yuan, Yue He, Wenbin Li, Zhengyu Chen, Kun Kuang
To achieve this goal, we apply a bilevel optimization to explicitly model and optimize the coupling relationship between the OOD model and auxiliary adapter layers.
1 code implementation • 14 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.
no code implementations • 14 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.
no code implementations • ICCV 2023 • Didi Zhu, Yincuan Li, Junkun Yuan, Zexi Li, 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.
Ranked #1 on Universal Domain Adaptation on Office-Home
no code implementations • 28 Apr 2023 • Chengyuan Liu, Fubang Zhao, Yangyang Kang, Jingyuan Zhang, Xiang Zhou, Changlong Sun, Kun Kuang, Fei Wu
In addition, we propose RexUIE, which is a Recursive Method with Explicit Schema Instructor for UIE.
no code implementations • 8 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.
no code implementations • 25 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.
no code implementations • 28 Jun 2023 • Didi Zhu, Zexi Li, Min Zhang, Junkun Yuan, Yunfeng Shao, Jiashuo Liu, Kun Kuang, Yinchuan Li, Chao Wu
It is found that NC optimality of text-to-image representations shows a positive correlation with downstream generalizability, which is more severe under class imbalance settings.
no code implementations • 16 Aug 2023 • Junao Shen, Long Chen, Kun Kuang, Fei Wu, Tian Feng, Wei zhang
The proposed two-sage framework comprises a multi-expert decoder (MED) and a multi-expert output ensemble (MOE).
no code implementations • 16 Aug 2023 • Anpeng Wu, Haoxuan Li, Kun Kuang, Keli Zhang, Fei Wu
Learning directed acyclic graphs (DAGs) to identify causal relations underlying observational data is crucial but also poses significant challenges.
1 code implementation • 21 Sep 2023 • Chengyuan Liu, Fubang Zhao, Lizhi Qing, Yangyang Kang, Changlong Sun, Kun Kuang, Fei Wu
There are several black-box attack methods, such as Prompt Attack, which can change the behaviour of LLMs and induce LLMs to generate unexpected answers with harmful contents.
no code implementations • 13 Oct 2023 • Yiquan Wu, Siying Zhou, Yifei Liu, Weiming Lu, Xiaozhong Liu, Yating Zhang, Changlong Sun, Fei Wu, Kun Kuang
Precedents are the previous legal cases with similar facts, which are the basis for the judgment of the subsequent case in national legal systems.
no code implementations • 19 Dec 2023 • Zhengyu Chen, Teng Xiao, Kun Kuang, Zheqi Lv, Min Zhang, Jinluan Yang, Chengqiang Lu, Hongxia Yang, Fei Wu
In this paper, we study the problem of the generalization ability of GNNs in Out-Of-Distribution (OOD) settings.
1 code implementation • 10 Jan 2024 • Xueyu Hu, Ziyu Zhao, Shuang Wei, Ziwei Chai, Qianli Ma, Guoyin Wang, Xuwu Wang, Jing Su, Jingjing Xu, Ming Zhu, Yao Cheng, Jianbo Yuan, Jiwei Li, Kun Kuang, Yang Yang, Hongxia Yang, Fei Wu
In this paper, we introduce InfiAgent-DABench, the first benchmark specifically designed to evaluate LLM-based agents on data analysis tasks.
no code implementations • 10 Jan 2024 • Xueyu Hu, Kun Kuang, Jiankai Sun, Hongxia Yang, Fei Wu
Large language models (LLMs) have made significant progress in code generation tasks, but their performance in tackling programming problems with complex data structures and algorithms remains suboptimal.
1 code implementation • 14 Jan 2024 • Zhengqing Fang, Shuowen Zhou, Zhouhang Yuan, Yuxuan Si, Mengze Li, Jinxu Li, Yesheng Xu, Wenjia Xie, Kun Kuang, Yingming Li, Fei Wu, Yu-Feng Yao
This study investigates the performance, interpretability, and clinical utility of KGDM in the diagnosis of infectious keratitis (IK), which is the leading cause of corneal blindness.
no code implementations • 15 Feb 2024 • Ziyu Zhao, Leilei Gan, Guoyin Wang, Wangchunshu Zhou, Hongxia Yang, Kun Kuang, Fei Wu
Low-Rank Adaptation (LoRA) provides an effective yet efficient solution for fine-tuning large language models (LLM).
1 code implementation • 18 Feb 2024 • Zihao Tang, Zheqi Lv, Shengyu Zhang, Fei Wu, Kun Kuang
The rapid advancement of Large Language Models (LLMs) has revolutionized various sectors by automating routine tasks, marking a step toward the realization of Artificial General Intelligence (AGI).
no code implementations • 19 Feb 2024 • Didi Zhu, Zhongyi Sun, Zexi Li, Tao Shen, Ke Yan, Shouhong Ding, Kun Kuang, Chao Wu
Catastrophic forgetting emerges as a critical challenge when fine-tuning multi-modal large language models (MLLMs), where improving performance on unseen tasks often leads to a significant performance drop on the original tasks.
no code implementations • 5 Mar 2024 • Yingrong Wang, Anpeng Wu, Haoxuan Li, Weiming Liu, Qiaowei Miao, Ruoxuan Xiong, Fei Wu, Kun Kuang
This paper focuses on developing Pareto-optimal estimation and policy learning to identify the most effective treatment that maximizes the total reward from both short-term and long-term effects, which might conflict with each other.
1 code implementation • 7 Mar 2024 • Ang Li, Qiangchao Chen, Yiquan Wu, Ming Cai, Xiang Zhou, Fei Wu, Kun Kuang
In this paper, we introduce a novel From Graph to Word Bag (FWGB) approach, which introduces domain knowledge regarding constituent elements to guide the model in making judgments on confusing charges, much like a judge's reasoning process.
1 code implementation • 7 Mar 2024 • Ang Li, Yiquan Wu, Yifei Liu, Fei Wu, Ming Cai, Kun Kuang
Court View Generation (CVG) is a challenging task in the field of Legal Artificial Intelligence (LegalAI), which aims to generate court views based on the plaintiff claims and the fact descriptions.
1 code implementation • 11 Mar 2024 • Fengda Zhang, Qianpei He, Kun Kuang, Jiashuo Liu, Long Chen, Chao Wu, Jun Xiao, Hanwang Zhang
This work proposes a novel, generation-based two-stage framework to train a fair FAC model on biased data without additional annotation.
no code implementations • 11 Mar 2024 • Dingyuan Zhu, Daixin Wang, Zhiqiang Zhang, Kun Kuang, Yan Zhang, Yulin kang, Jun Zhou
The estimator is general for all types of outcomes, and is able to comprehensively model the treatment and control group data together to approach the uplift.
1 code implementation • 11 Mar 2024 • Zihao Tang, Zheqi Lv, Shengyu Zhang, Yifan Zhou, Xinyu Duan, Fei Wu, Kun Kuang
However, simply adopting models derived from DFKD for real-world applications suffers significant performance degradation, due to the discrepancy between teachers' training data and real-world scenarios (student domain).
no code implementations • 11 Mar 2024 • Chengyuan Liu, Yangyang Kang, Fubang Zhao, Kun Kuang, Zhuoren Jiang, Changlong Sun, Fei Wu
In this paper, we propose EvoKD: Evolving Knowledge Distillation, which leverages the concept of active learning to interactively enhance the process of data generation using large language models, simultaneously improving the task capabilities of small domain model (student model).
no code implementations • 15 Mar 2024 • Ruihao Zhang, Zhengyu Chen, Teng Xiao, Yueyang Wang, Kun Kuang
We propose a novel Invariant Neighborhood Pattern Learning (INPL) to alleviate the distribution shifts problem on non-homophilous graphs.
1 code implementation • 21 Mar 2024 • Minqin Zhu, Anpeng Wu, Haoxuan Li, Ruoxuan Xiong, Bo Li, Xiaoqing Yang, Xuan Qin, Peng Zhen, Jiecheng Guo, Fei Wu, Kun Kuang
Estimating the individuals' potential response to varying treatment doses is crucial for decision-making in areas such as precision medicine and management science.
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.