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An efficient likelihood-free Bayesian inference method based on sequential neural posterior estimation

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2025年

作者： Xiong, Yifei Yang, Xiliang Zhang, Sanguo He, Zhijian Purdue Univ Dept Stat W Lafayette IN USA South China Univ Technol Sch Math Guangzhou 510641 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

Sequential neural posterior estimation (SNPE) techniques have been recently proposed for dealing with simulation-based models with intractable likelihoods. Unlike approximate Bayesian computation, SNPE techniques learn the posterior from sequential simulation using neural network-based conditional density estimators by minimizing a specific loss function. The SNPE method proposed by Lueckmann et al. (NeurIPS 2017) used a calibration kernel to boost the sample weights around the observed data, resulting in a concentrated loss function. However, the use of calibration kernels may increase the variances of both the empirical loss and its gradient, making the training inefficient. To improve the stability of SNPE, this paper proposes to use an adaptive calibration kernel and several variance reduction techniques. The proposed method greatly speeds up the process of training and provides a better approximation of the posterior than the original SNPE method and some existing competitors as confirmed by numerical experiments. We also managed to demonstrate the superiority of the proposed method for a high-dimensional model with a real-world dataset.

关键词： Approximate Bayesian computation Normalizing flows Simulator-based inference

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Social media and capital markets: an interdisciplinary bibliometric analysis

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FINANCIAL INNOVATION 2025年第1期11卷 1-32页

作者： Long, Wen Guo, Man Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Social media platforms collect information from complex human social behaviors. It has been shown that they significantly impact capital markets. This article conducts a bibliometric analysis of 363 high-quality articles from the Web of Science on social media and capital markets. First, a descriptive analysis was performed, including discipline distribution, growth trends, institutions, publication sources, influential authors, articles, and countries. Second, collaborative networks identify cooperation between countries and institutions in the network analysis section. Bibliometric coupling analysis determines the relationship between journals of different disciplines. Cluster analysis and keyword co-occurrence networks identify hot research topics. Burst detection and keyword time zone analysis track field development. Finally, keyword analysis and clustering analysis are conducted on the research of different disciplines. These sample papers mainly include seven hot topics, including sentiment analysis and financial market prediction. Articles in business and finance focus more on social media's impact on capital markets. In contrast, information technology articles focus more on extracting information from social media. Each field also has its unique research directions. This paper contributes to a comprehensive understanding of the research on social media and capital markets. It offers perspectives on disciplinary differences, serving as a valuable scholarly reference for conducting in-depth research.

关键词： Social media Capital markets Bibliometric analysis Interdisciplinary analysis G12 G41 C55

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Fintech, technological innovation and regional economic growth: Theoretical modeling and empirical evidence

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CHINA ECONOMIC REVIEW 2025年 91卷

作者： Huang, Xirong Dong, Jichang Li, Xiuting Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China MOE Social Sci Lab Digital Econ Forecasts & Policy Hefei Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

This study aims to investigate the role of fintech in stimulating innovation and promoting regional economic growth. A Multi-Sector Endogenous Growth Model (MEGM) is constructed by incorporating fintech while internalizing technological progress and human capital accumulation within the framework of creative destruction. Based on this model, we theoretically analyze how fintech influences innovation and regional economic growth, followed by empirical testing using panel data from 246 prefecture-level cities in China during the period from 2012 to 2020. Furthermore, we contextualize these findings within China's fintech development landscape, trends in technological innovation, and patterns of regional economic growth. The results demonstrate that fintech exerts a significant incentive effect on regional innovation and effectively promotes economic growth. Heterogeneity analysis based on cities' endowments reveals that the impact of fintech is more pronounced in financially developed cities, those with high digital endowments, as well as in first- and second-tier cities. The analyses presented herein elucidate how fintech facilitates technology-driven regional economic growth in China, providing valuable insights for fostering innovation-driven development through fintech.

关键词： Fintech Technological innovation Economic growth Endogenous growth model Panel data analysis

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TGformer: A Graph Transformer Framework for knowledge Graph Embedding

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IEEE TRANSACTIONS ON knowledge AND data ENGINEERING 2025年第1期37卷 526-541页

作者： Shi, Fobo Li, Duantengchuan Wang, Xiaoguang Li, Bing Wu, Xindong Wuhan Univ Sch Informat Management Wuhan 430072 Peoples R China Wuhan Univ Intelligent Comp Lab Cultural Heritage Wuhan 430072 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ China Hefei 230009 Peoples R China

knowledge graph embedding is efficient method for reasoning over known facts and inferring missing links. Existing methods are mainly triplet-based or graph-based. Triplet-based approaches learn the embedding of missing entities by a single triple only. They ignore the fact that the knowledge graph is essentially a graph structure. Graph-based methods consider graph structure information but ignore the contextual information of nodes in the knowledge graph, making them unable to discern valuable entity (relation) information. In response to the above limitations, we propose a general graph transformer framework for knowledge graph embedding (TGformer). It is the first to use a graph transformer to build knowledge embeddings with triplet-level and graph-level structural features in the static and temporal knowledge graph. Specifically, a context-level subgraph is constructed for each predicted triplet, which models the relation between triplets with the same entity. Afterward, we design a knowledge graph transformer network (KGTN) to fully explore multi-structural features in knowledge graphs, including triplet-level and graph-level, boosting the model to understand entities (relations) in different contexts. Finally, semantic matching is adopted to select the entity with the highest score. Experimental results on several public knowledge graph datasets show that our method can achieve state-of-the-art performance in link prediction.

关键词： knowledge graph graph transformer link prediction knowledge graph graph transformer link prediction

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Robust one-class support vector machine

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NEURAL NETWORKS 2025年 188卷 107416页

作者： Zhao, Xiaoxi Tian, Yingjie Zheng, Chonghua Hangzhou Dianzi Univ Sch Management Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Expt Ctr Data Sci & Intelligent Decis Making Hangzhou 310018 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China UCAS MOE Social Sci Lab Digital Econ Forecasts & Policy Beijing 100190 Peoples R China

One-Class Support Vector Machine (OCSVM) is an effective algorithm in one-class classification task. However, it exhibits sensitivity to noise and outliers. Current solutions often employ bounded loss functions that impose finite but relatively large penalties on noise or outliers, and these loss functions suffer from limitations of discontinuity and non-differentiability. To address these issues, this paper introduces a novel, continuous, smooth, and differentiable loss function, namely Quadratic Type Squared Error Loss Function (QTSELF), and proposes a more robust OCSVM (Q-OCSVM). Q-OCSVM not only differentiates samples based on their positions and applies distinct treatments accordingly but also enhances model robustness by imposing minimal penalties on noise and outliers. Moreover, the elegant mathematical properties of the loss function facilitate model optimization. Theoretical analysis utilizes Rademacher complexity theory to conduct the generalization error bound of the model. Momentum method is used to optimize Q-OCSVM. Extensive experiments convincingly demonstrate that Q-OCSVM outperforms the benchmark techniques.

关键词： One-class support vector machine Loss function Robustness Momentum

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A universal network strategy for lightspeed computation of entropy-regularized optimal transport

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NEURAL NETWORKS 2025年 184卷 107038页

作者： Shi, Yong Zheng, Lei Quan, Pei Xiao, Yang Niu, Lingfeng Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Beijing Univ Technol Coll Econ & Management Beijing 100124 Peoples R China Beijing Univ Technol Coll Comp Sci Beijing 100124 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Optimal transport (OT) is an effective tool for measuring discrepancies in probability distributions and histograms of features. To reduce its high computational complexity, entropy-regularized OT is proposed, which is computed through Sinkhorn algorithm and can be readily integrated into neural networks. However, each time the parameters of networks are updated, both the value and derivative of OT need to be calculated. When there is a relatively high demand for solving accuracy, the number of layers in the computation graph for Sinkhorn algorithm is fairly large, requiring plenty of time and memory. To address this problem, we propose a novel network strategy to estimate the transport matrix instead of Sinkhorn algorithm, which significantly reduces the computation graph size. Compared with other neural OT, our method is suitable for arbitrary cost functions and varying marginal distributions. To avoid numerical instability induced by a small regularization coefficient, we devise the new method in log domain with the dual form. We estimate the error bound of the resulting algorithm for approximate inputs in theory, and extend our approach to robust OT and barycenter computation in practice. Extensive experiments show that our method outperforms baselines on both required computation cost and accuracy significantly.

关键词： Optimal transport Entropy regularization Lightspeed computation Neural networks

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CSL-CTEA: a systematic method for evaluating novel intelligent cognitive assessment tools

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HEALTH INFORMATION SCIENCE AND SYSTEMS 2025年第1期13卷 1-17页

作者： Li, Aihua Chen, Sifan Liu, Jianbing Chen, Ting Shi, Yong Cent Univ Finance & Econ Sch Management Sci & Engn Beijing 102206 Peoples R China Beijing Acad Sci & Technol Res Inst Smart Aging Beijing 100050 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Nebraska Omaha Coll Informat Sci & Technol Omaha NE 68182 USA

With the intensification of global population aging, the incidence of cognitive disorders such as dementia continues to rise. The Mini-Mental State Examination (MMSE) and other alternative tools can help doctors detect subtle changes in cognitive function at an early stage. These assessment tools can make a diagnosis before symptoms become severe, providing opportunities for early intervention, which is crucial for delaying disease progression and improving the quality of life of patients. However, traditional cognitive assessment methods are overly complex and affected by various factors. With the development of artificial intelligence technology, many new assessment tools are constantly being developed and improved. How to evaluate the effectiveness of intelligent electronic cognitive assessment tools is particularly important. We have proposed the Correlation and Supervised Learning-based Cognitive Tool Effectiveness Assessment Method (CSL-CTEA) to evaluate the effectiveness of intelligent electronic cognitive assessment tools, including: (1) experimental design and data collection based on traditional scales and intelligent electronic assessment tools, (2) consistency and correlation tests;(3) accuracy analysis of assessment results based on supervised learning. We used CSL-CTEA to explore the effectiveness of a certain electronic assessment. This intelligent electronic cognitive assessment tool includes voice tests, orientation tests, and picture recognition tests to assess cognitive abilities from multiple perspectives. The results show that the electronic assessment is in good agreement with traditional cognitive assessment methods. The various indicators of the electronic assessment can explain the changes in MMSE scores to some extent. The study also found that the electronic assessment performs well in determining whether the subject is at cognitive risk. To some extent, the electronic assessment can replace traditional cognitive assessment methods such as

关键词： Cognitive assessment MMSE Electronic assessment Regression Random Forest

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Self-improved multi-view interactive knowledge transfer

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INFORMATION FUSION 2025年 114卷

作者： Fu, Saiji Wen, Haonan Wang, Xiaoxiao Tian, Yingjie Beijing Univ Posts & Telecommun Sch Econ & Management Beijing 100876 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China UCAS Social Sci Lab Digital Econ Forecasts & Policy Sim MOE Beijing 100190 Peoples R China

Multi-view learning (MVL) is a promising data fusion technique based on the principles of consensus and complementarity. Despite significant advancements in this field, several challenges persist. First, scalability remains an issue, as many existing approaches are limited to two-view scenarios, making them difficult to extend to more complex multi-view settings. Second, implementing consensus principles in current techniques often requires adding extra terms to the model's objective function or constraints, leading to increased complexity. Additionally, when applying complementarity principles, most studies focus on pairwise interactions between views, overlooking the benefits of deeper and broader multi-view interactions. To address these challenges, this paper proposes the multi-view interactive knowledge transfer (MVIKT) model, which enhances scalability by effectively managing interactions across multiple views, thereby overcoming the limitations of traditional two-view models. More importantly, MVIKT introduces a novel interactive knowledge transfer strategy that simplifies the application of the consensus principle by eliminating the need for additional terms. By treating margin distances as transferable knowledge and facilitating multiple rounds of interaction, MVIKT uncovers deeper complementary information, thereby improving the overall effectiveness of MVL. Theoretical analysis further supports the MVIKT model, demonstrating that transferring knowledge through margin distance is capable of lowering the upper bound of the generalization error. Extensive experiments across diverse datasets validate MVIKT's superiority, showing statistically significant improvements over benchmark methods.

关键词： data fusion Interactive knowledge transfer Multi-view learning Support vector machine Margin distance

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Subpart Suppression Network for Few-Shot Object Counting

Subpart Suppression Network for Few-Shot Object Counting

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Lanxin Liu Xinyan Liu Guorong Li School of Computer Science and Technology Key Lab of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences School of Computer Science and Technology Harbin Institute of Technology (Weihai)

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Few-shot object counting and detection aim to count objects along with their bounding boxes specified by exemplar bounding boxes. Current mainstream methods predict density maps by applying similarity between exemplar and image features to get counting results and detecting peak points from the density map as the positions of objects. However, the sub-parts of objects can also have high similarity to the exemplars, harming the counting and detection performance. To address these issues, we propose a two-stage Subpart Suppression Network (SSN), consisting of a Subpart Suppression Density map Predictor (SSDP), which enforces the model focus on whole objects rather than subparts, and a SAM-based Detector and Verifier, which refines the final prediction by a clustering method. Extensive experiments on two popular datasets show an advantage in performance over the state-of-the-art methods and prove the components’ effectiveness.

关键词： Accuracy Filtering Convolution Clustering methods Detectors Predictive models Feature extraction Acoustics Speech processing Kernel

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Practically Unbiased Pairwise Loss for Recommendation With Implicit Feedback

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2025年第4期47卷 2460-2474页

作者： Cao, Tianwei Xu, Qianqian Yang, Zhiyong Ma, Zhanyu Huang, Qingming Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 101408 Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Pattern Recognit & Intelligent Syst Lab Beijing 100876 Peoples R China Chinese Acad Sci Key Lab Intelligent Informat Proc Inst Comp Technol Beijing 100190 Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Pattern Recognit & Intelligent Syst Lab Beijing Peoples R China Univ Chinese Acad Sci Key Lab Big Data Min & Knowledge Management BDKM Beijing 101408 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China

Recommender systems have been widely employed on various online platforms to improve user experience. In these systems, recommendation models are often learned from the users' historical behaviors that are automatically collected. Notably, recommender systems differ slightly from ordinary supervised learning tasks. In recommender systems, there is an exposure mechanism that decides which items could be presented to each specific user, which breaks the i.i.d assumption of supervised learning and brings biases into the recommendation models. In this paper, we focus on unbiased ranking loss weighted by inversed propensity scores (IPS), which are widely used in recommendations with implicit feedback labels. More specifically, we first highlight the fact that there is a gap between theory and practice in IPS-weighted unbiased loss. The existing pairwise loss could be theoretically unbiased by adopting an IPS weighting scheme. Unfortunately, the propensity scores are hard to estimate due to the inaccessibility of each user-item pair's true exposure status. In practical scenarios, we can only approximate the propensity scores. In this way, the theoretically unbiased loss would be still practically biased. To solve this problem, we first construct a theoretical framework to obtain a generalization upper bound of the current theoretically unbiased loss. The bound illustrates that we can ensure the theoretically unbiased loss's generalization ability if we lower its implementation loss and practical bias at the same time. To that aim, we suggest treating feedback label Y-ui as a noisy proxy for exposure result for O-ui each user-item pair (u,i). Here we assume the noise rate meets the condition that (P) over cap O-ui=1, Y-ui not equal O-ui) < 1/2. According to our analysis, this is a mild assumption that can be satisfied by many real-world applications. Based on this, we could train an accurate propensity model directly by leveraging a noise-resistant loss function. Then we c

关键词： Estimation Imputation Training data IP networks Accuracy Supervised learning Recommender systems Noise data models Computer science Recommender system collaborative filtering debias noise-robust learning pairwise ranking loss

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