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arXiv 2025年

作者： Wang, Qingmei Wang, Fanmeng Su, Bing Xu, Hongteng Gaoling School of Artificial Intelligence Renmin University of China China Gaoling School of Artificial Intelligence Beijing Key Laboratory of Big Data Management and Analysis Methods Renmin University of China China

Real-world event sequences are often generated by different temporal point processes (TPPs) and thus have clustering structures. Nonetheless, in the modeling and prediction of event sequences, most existing TPPs ignore the inherent clustering structures of the event sequences, leading to the models with unsatisfactory interpretability. In this study, we learn structure-enhanced TPPs with the help of Gromov-Wasserstein (GW) regularization, which imposes clustering structures on the sequence-level embeddings of the TPPs in the maximum likelihood estimation framework. In the training phase, the proposed method leverages a nonparametric TPP kernel to regularize the similarity matrix derived based on the sequence embeddings. In large-scale applications, we sample the kernel matrix and implement the regularization as a Gromov-Wasserstein (GW) discrepancy term, which achieves a trade-off between regularity and computational efficiency. The TPPs learned through this method result in clustered sequence embeddings and demonstrate competitive predictive and clustering performance, significantly improving the model interpretability without compromising prediction *** Codes 60G55, 62M10 Copyright © 2025, The Authors. All rights reserved.

关键词： Digital arithmetic

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Accelerating exploitation and integration of global renewable energy

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Innovation 2025年

作者： Wang, Jianxiao Chen, Xinjiang Zhuang, Minghao Li, Yan Ruan, Ziwen Wang, Yuhan Zhang, Ning Song, Jie He, Kebin Lu, Xi National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100871 China Department of Industrial Engineering and Management College of Engineering Peking University Beijing100871 China Peking University Ordos Research Institute of Energy Ordos017000 China State Key Laboratory of Nutrient Use and Management College of Resources and Environmental Sciences China Agricultural University Beijing100193 China College of Environmental Science and Engineering Tongji University Shanghai200092 China School of Environment State Key Joint Laboratory of Environment Simulation and Pollution Control Tsinghua University Beijing100084 China State Key Lab of Power Systems Department of Electrical Engineering Tsinghua University Beijing100084 China Institute for Carbon Neutrality Tsinghua University Beijing100084 China

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Generating timeline summaries with social media attention

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Frontiers of Computer Science 2016年第4期10卷 702-716页

作者： Wayne Xin ZHAO Ji-Rong WEN Xiaoming LI School of Information Renmin University of China Beijing 100872 China Beijing Key Laboratory of Big Data Management and Analysis Methods Renmin University of China Beijing 100872 China School of Electronics Engineering and Computer Science Peking University Beijing 100871 China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing 100144 China

Timeline generation is an important research task which can help users to have a quick understanding of the overall evolution of one given topic. Previous methods simply split the time span into fixed, equal time intervals without studying the role of the evolutionary patterns of the underlying topic in timeline generation. In addition, few of these methods take users＇ collective interests into considerations to generate timelines. We consider utilizing social media attention to address these two problems due to the facts： 1） social media is an important pool of real users＇ collective interests; 2） the information cascades generated in it might be good indicators for boundaries of topic phases. Employing Twitter as a basis, we propose to incorporate topic phases and user＇s collective interests which are learnt from social media into a unified timeline generation algorithm. We construct both one informativeness-oriented and three interestingness-oriented evaluation sets over five topics. We demonstrate that it is very effective to generate both informative and interesting timelines. In addition, our idea naturally leads to a novel presen- tation of timelines, i.e., phase based timelines, which can potentially improve user experience.

关键词： timeline social media attention phase users'collective interests

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Ranking and tagging bursty features in text streams with context language models

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Frontiers of Computer Science 2017年第5期11卷 852-862页

作者： Wayne Xin ZHAO Chen LIU Ji-Rong WEN Xiaoming LI School of Information Renmin University of China Beijing 100872 China Beijing Key Laboratory of Big Data Management and Analysis Methods Renmin University of China Beijing 100872 China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing 100144 China School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

Detecting and using bursty pattems to analyze text streams has been one of the fundamental approaches in many temporal text mining applications. So far, most existing studies have focused on developing methods to detect bursty features based purely on term frequency changes. Few have taken the semantic contexts of bursty features into consideration, and as a result the detected bursty features may not always be interesting and can be hard to interpret. In this article, we propose to model the contexts of bursty features using a language modeling approach. We propose two methods to estimate the context language models based on sentence-level context and document-level context. We then propose a novel topic diversity-based metric using the context models to find newsworthy bursty features. We also propose to use the context models to automatically assign meaningful tags to bursty features. Using a large corpus of news articles, we quantitatively show that the proposed context language models for bursty features can effectively help rank bursty features based on their newsworthiness and to assign meaningful tags to annotate bursty features. We also use two example text mining applications to qualitatively demonstrate the usefulness of bursty feature ranking and tagging.

关键词： bursty features bursty features ranking bursty feature tagging context modeling

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Large scale sparse clustering 25

Large scale sparse clustering

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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Ruqi, Zhang Lu, Zhiwu Beijing Key Laboratory of Big Data Management and Analysis Methods School of Information Renmin University of China Beijing100872 China

Large-scale clustering has found wide applications in many fields and received much attention in recent years. However, most existing large-scale clustering methods can only achieve mediocre performance, because they are sensitive to the unavoidable presence of noise in the large-scale data. To address this challenging problem, we thus propose a large-scale sparse clustering (LSSC) algorithm. In this paper, we choose a two-step optimization strategy for large-scale sparse clustering: 1) k-means clustering over the large-scale data to obtain the initial clustering results;2) clustering refinement over the initial results by developing a spare coding algorithm. To guarantee the scalability of the second step for large-scale data, we also utilize nonlinear approximation and dimension reduction techniques to speed up the sparse coding algorithm. Experimental results on both synthetic and real-world datasets demonstrate the promising performance of our LSSC algorithm.

关键词： Clustering algorithms

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InsightGAN: Semi-supervised feature learning with generative adversarial network for drug abuse detection 25th

InsightGAN: Semi-supervised feature learning with generative...

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25th International Conference on Neural Information Processing, ICONIP 2018

作者： Liu, Guangzhen Hu, Jun Zhao, An Ding, Mingyu Huo, Yuqi Lu, Zhiwu Beijing Key Laboratory of Big Data Management and Analysis Methods School of Information Renmin University of China Beijing100872 China

ISBN: (纸本)9783030041816

We present a novel generative adversarial network (GAN) model, called InsightGAN, for drug abuse detection. Our model is inspired by two closely related works on machine learning for healthcare applications: (1) drug abuse detection has been solved by machine learning with plentiful data from social media (where face pictures can be easily obtained);(2) facial characteristics have been explored in mental disorder diagnosis (drug addiction is also a mental disorder). In this paper, we adopt deep learning to extract discriminative facial features for drug abuse detection. However, in this application, the face pictures with ground-truth labels are far from sufficient for training a deep learning model. To alleviate the scarcity of labelled data, we thus propose a semi-supervised facial feature learning model based on GAN. Moreover, we also develop a robust algorithm for training our InsightGAN. Experimental results show the promising performance of our InsightGAN. © Springer Nature Switzerland AG 2018.

关键词： Generative adversarial networks

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Zero-shot learning with superclasses 25th

Zero-shot learning with superclasses

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25th International Conference on Neural Information Processing, ICONIP 2018

作者： Huo, Yuqi Ding, Mingyu Zhao, An Hu, Jun Wen, Ji-Rong Lu, Zhiwu Beijing Key Laboratory of Big Data Management and Analysis Methods School of Information Renmin University of China Beijing100872 China

ISBN: (纸本)9783030041816

Zero-shot learning (ZSL) can be regarded as transfer learning from seen classes to unseen ones so that the later can be recognized without any training samples. Its main difficulty lies in that there often exists a large domain gap between the seen and unseen class domains. Inspired by the fact that an unseen class is not strictly ‘zero-shot’ (thus easier to recognize) if it falls into a superclass that consists of one or more seen classes, we propose a new ZSL model, termed ZSL with superclasses (ZSLS), that leverages the superclasses as the bridge between seen and unseen classes to narrow the domain gap. By generating the superclasses with k-means clustering over all seen and unseen class prototypes, we formulate ZSLS as a min-min optimization problem. An efficient iterative algorithm is also developed for model optimization. Extensive experiments show that our model achieves the state-of-the-art results. © Springer Nature Switzerland AG 2018.

关键词： Zero-shot learning

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Crowd-Guided Entity Matching with Consolidated Textual data

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Journal of Computer Science & Technology 2017年第5期32卷 858-876页

作者： Zhi-Xu Li Qiang Yang An Liu Guan-Feng Liu Jia Zhu Jia-Jie Xu Kai Zheng Min Zhang School of Computer Science and Technology Soochow University Suzhou 215006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou 510006 China School of Computer Science and Technology Soochow University Suzhou 215006 China School of Computer South China Normal University Guangzhou 510631 China School of Computer Science and Technology Soochow University Suzhou 215006 China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing 100872 China

Entity matching (EM) identifies records referring to the same entity within or across databases. Existing methods using structured attribute values (such as digital, date or short string values) may fail when the structured information is not enough to reflect the matching relationships between records. Nowadays more and more databases may have some unstructured textual attribute containing extra consolidated textual information (CText) of the record, but seldom work has been done on using the CText for EM. Conventional string similarity metrics such as edit distance or bag-of-words are unsuitable for measuring the similarities between CText since there are hundreds or thousands of words with each piece of CText, while existing topic models either cannot work well since there are no obvious gaps between topics in CText. In this paper, we propose a novel cooccurrence-based topic model to identify various sub-topics from each piece of CText, and then measure the similarity between CText on the multiple sub-topic dimensions. To avoid ignoring some hidden important sub-topics, we let the crowd help us decide weights of different sub-topics in doing EM. Our empirical study on two real-world datasets based on Amzon Mechanical Turk Crowdsourcing Platform shows that our method outperforms the state-of-the-art EM methods and Text Understanding models.

关键词： entity matching consolidated textual data crowdsourcing

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SRAP-Agent: Simulating and Optimizing Scarce Resource Allocation Policy with LLM-based Agent

SRAP-Agent: Simulating and Optimizing Scarce Resource Alloca...

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2024 Conference on Empirical methods in Natural Language Processing, EMNLP 2024

作者： Ji, Jiarui Li, Yang Liu, Hongtao Du, Zhicheng Wei, Zhewei Shen, Weiran Qi, Qi Lin, Yankai Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

ISBN: (纸本)9798891761681

Public scarce resource allocation plays a crucial role in economics as it directly influences the efficiency and equity in society. Traditional studies including theoretical model-based, empirical study-based and simulation-based methods encounter limitations due to the idealized assumption of complete information and individual rationality, as well as constraints posed by limited available data. In this work, we propose an innovative framework, SRAP-Agent (Simulating and Optimizing Scarce Resource Allocation Policy with LLM-based Agent), which integrates Large Language Models (LLMs) into economic simulations, aiming to bridge the gap between theoretical models and real-world dynamics. Using public housing allocation scenarios as a case study, we conduct extensive policy simulation experiments to verify the feasibility and effectiveness of the SRAP-Agent and employ the Policy Optimization Algorithm with certain optimization objectives. The source code can be found in https://***/jijiarui-cather/SRAPAgent_Framework. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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MMPareto: Boosting Multimodal Learning with Innocent Unimodal Assistance 41

MMPareto: Boosting Multimodal Learning with Innocent Unimoda...

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41st International Conference on Machine Learning, ICML 2024

作者： Wei, Yake Hu, Di Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

Multimodal learning methods with targeted unimodal learning objectives have exhibited their superior efficacy in alleviating the imbalanced multimodal learning problem. However, in this paper, we identify the previously ignored gradient conflict between multimodal and unimodal learning objectives, potentially misleading the unimodal encoder optimization. To well diminish these conflicts, we observe the discrepancy between multimodal loss and unimodal loss, where both gradient magnitude and covariance of the easier-to-learn multimodal loss are smaller than the unimodal one. With this property, we analyze Pareto integration under our multimodal scenario and propose MMPareto algorithm, which could ensure a final gradient with direction that is common to all learning objectives and enhanced magnitude to improve generalization, providing innocent unimodal assistance. Finally, experiments across multiple types of modalities and frameworks with dense cross-modal interaction indicate our superior and extendable method performance. Our method is also expected to facilitate multi-task cases with a clear discrepancy in task difficulty, demonstrating its ideal scalability. The source code and dataset are available at https://***/GeWu-Lab/MMPareto_ICML2024. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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