Knowledge graph embedding (KGE) aims to map entities and relations of a knowledge graph (KG) into a low-dimensional and dense vector space via contrasting the positive and negative triples. In the training process of KGEs, negative sampling is essential to find high-quality negative triples since KGs only contain positive triples. Most existing negative sampling methods assume that non-existent triples with high scores are high-quality negative triples. However, negative triples sampled by these methods are likely to contain noise. Specifically, they ignore that non-existent triples with high scores might also be true facts due to the incompleteness of KGs, which are usually called false negative triples. To alleviate the above issue, we propose an easily pluggable denoising mixup method called DeMix, which generates high-quality triples by refining sampled negative triples in a self-supervised manner. Given a sampled unlabeled triple, DeMix firstly classifies it into a marginal pseudo-negative triple or a negative triple based on the judgment of the KGE model itself. Secondly, it selects an appropriate mixup partner for the current triple to synthesize a partially positive or a harder negative triple. Experimental results on the knowledge graph completion task show that the proposed DeMix is superior to other negative sampling techniques, ensuring corresponding KGEs a faster convergence and better link prediction results.

When machine learning models are trained on synthetic data and then deployed on real data, there is often a performance drop due to the distribution shift between synthetic and real data. In this paper, we introduce a new ensemble strategy for training downstream models, with the goal of enhancing their performance when used on real data. We generate multiple synthetic datasets by applying a differential privacy (DP) mechanism several times in parallel and then ensemble the downstream models trained on these datasets. While each synthetic dataset might deviate more from the real data distribution, they collectively increase sample diversity. This may enhance the robustness of downstream models against distribution shifts. Our extensive experiments reveal that while ensembling does not enhance downstream performance (compared with training a single model) for models trained on synthetic data generated by marginal-based or workload-based DP mechanisms, our proposed ensemble strategy does improve the performance for models trained using GAN-based DP mechanisms in terms of both accuracy and calibration of downstream models.

The application of transfer learning, an approach utilizing knowledge from a source domain to enhance model performance in a target domain, has seen a tremendous rise in recent years, underpinning many real-world scenarios. The key to its success lies in the shared common knowledge between the domains, a prerequisite in most transfer learning methodologies. These methods typically presuppose identical feature spaces and label spaces in both domains, known as homogeneous transfer learning, which, however, is not always a practical assumption. Oftentimes, the source and target domains vary in feature spaces, data distributions, and label spaces, making it challenging or costly to secure source domain data with identical feature and label spaces as the target domain. Arbitrary elimination of these differences is not always feasible or optimal. Thus, heterogeneous transfer learning, acknowledging and dealing with such disparities, has emerged as a promising approach for a variety of tasks. Despite the existence of a survey in 2017 on this topic, the fast-paced advances post-2017 necessitate an updated, in-depth review. We therefore present a comprehensive survey of recent developments in heterogeneous transfer learning methods, offering a systematic guide for future research. Our paper reviews methodologies for diverse learning scenarios, discusses the limitations of current studies, and covers various application contexts, including Natural Language Processing, Computer Vision, Multimodality, and Biomedicine, to foster a deeper understanding and spur future research.

In the realm of machine learning (ML) systems featuring client-host connections, the enhancement of privacy security can be effectively achieved through federated learning (FL) as a secure distributed ML methodology. FL effectively integrates cloud infrastructure to transfer ML models onto edge servers using blockchain technology. Through this mechanism, it guarantees the streamlined processing and data storage requirements of both centralized and decentralized systems, with an emphasis on scalability, privacy considerations, and cost-effective communication. In current FL implementations, data owners locally train their models, and subsequently upload the outcomes in the form of weights, gradients, and parameters to the cloud for overall model aggregation. This innovation obviates the necessity of engaging Internet of Things (IoT) clients and participants to communicate raw and potentially confidential data directly with a cloud center. This not only reduces the costs associated with communication networks but also enhances the protection of private data. This survey conducts an analysis and comparison of recent FL applications, aiming to assess their efficiency, accuracy, and privacy protection. However, in light of the complex and evolving nature of FL, it becomes evident that additional research is imperative to address lingering knowledge gaps and effectively confront the forthcoming challenges in this field. In this study, we categorize recent literature into the following clusters: privacy protection, resource allocation, case study analysis, and applications. Furthermore, at the end of each section, we tabulate the open areas and future directions presented in the referenced literature, affording researchers and scholars an insightful view of the evolution of the field.

The use of credit cards has recently increased, creating an essential need for credit card assessment methods to minimize potential risks. This study investigates the utilization of machine learning (ML) models for credit card default prediction system. The main goal here is to investigate the best-performing ML model for new proposed credit card scoring dataset. This new dataset includes credit card transaction histories and customer profiles, is proposed and tested using a variety of machine learning algorithms, including logistic regression, decision trees, random forests, multi-layer perceptron (MLP) neural network, XGBoost, and LightGBM. To prepare the data for machine learning models, we perform data pre-processing, feature extraction, feature selection, and data balancing techniques. Experimental results demonstrate that MLP outperforms logistic regression, decision trees, random forests, LightGBM, and XGBoost in terms of predictive performance in true positive rate, achieving an impressive area under the curve (AUC) of 86.7% and an accuracy rate of 91.6%, with a recall rate exceeding 80%. These results indicate the superiority of MLP in predicting the default customers and assessing the potential risks. Furthermore, they help banks and other financial institutions in predicting loan defaults at an earlier stage.

The problem of system identification for the Kalman filter, relying on the expectation-maximization (EM) procedure to learn the underlying parameters of a dynamical system, has largely been studied assuming that observations are sampled at equally-spaced time points. However, in many applications this is a restrictive and unrealistic assumption. This paper addresses system identification for the continuous-discrete filter, with the aim of generalizing learning for the Kalman filter by relying on a solution to a continuous-time It\^o stochastic differential equation (SDE) for the latent state and covariance dynamics. We introduce a novel two-filter, analytical form for the posterior with a Bayesian derivation, which yields analytical updates which do not require the forward-pass to be pre-computed. Using this analytical and efficient computation of the posterior, we provide an EM procedure which estimates the parameters of the SDE, naturally incorporating irregularly sampled measurements. Generalizing the learning of latent linear dynamical systems (LDS) to continuous-time may extend the use of the hybrid Kalman filter to data which is not regularly sampled or has intermittent missing values, and can extend the power of non-linear system identification methods such as switching LDS (SLDS), which rely on EM for the linear discrete-time Kalman filter as a sub-unit for learning locally linearized behavior of a non-linear system. We apply the method by learning the parameters of a latent, multivariate Fokker-Planck SDE representing a toggle-switch genetic circuit using biologically realistic parameters, and compare the efficacy of learning relative to the discrete-time Kalman filter as the step-size irregularity and spectral-radius of the dynamics-matrix increases.

Detecting anomalies in a graph typically involves identifying nodes that deviate significantly from the normal behavior A key component of many graph neural networks (GNNs) is the of the graph, either in terms of their structural characteristics pooling operation, which seeks to reduce the size of a graph and/or their feature attributes [3]. However, graphs can often be while preserving important structural information. However, very large and complex, making it challenging to identify such most existing graph pooling strategies rely on an assignment anomalies. To address this problem, graph pooling can be used matrix obtained by employing a GNN layer, which is characterized to reduce the size of the graph while preserving its important by trainable parameters, often leading to significant computational structural features [4-6]. The aim is to produce a coarse representation complexity and a lack of interpretability in the pooling of the graph structure by summarizing the information process. In this paper, we propose an unsupervised graph contained in the nodes of the graph into a fixed-size vector or encoder-decoder model to detect abnormal nodes from graphs matrix while preserving the salient features of the graph. By by learning an anomaly scoring function to rank nodes based producing a coarse representation of the graph structure, graph on their degree of abnormality. In the encoding stage, we design pooling can help abstract away irrelevant or noisy details, and a novel pooling mechanism, named LCPool, which leverages focus on the most important structural properties of the graph.

Robust Markov Decision Processes (MDPs) and risk-sensitive MDPs are both powerful tools for making decisions in the presence of uncertainties. Previous efforts have aimed to establish their connections, revealing equivalences in specific formulations. This paper introduces a new formulation for risk-sensitive MDPs, which assesses risk in a slightly different manner compared to the classical Markov risk measure (Ruszczy\'nski 2010), and establishes its equivalence with a class of regularized robust MDP (RMDP) problems, including the standard RMDP as a special case. Leveraging this equivalence, we further derive the policy gradient theorem for both problems, proving gradient domination and global convergence of the exact policy gradient method under the tabular setting with direct parameterization. This forms a sharp contrast to the Markov risk measure, known to be potentially non-gradient-dominant (Huang et al. 2021). We also propose a sample-based offline learning algorithm, namely the robust fitted-Z iteration (RFZI), for a specific regularized RMDP problem with a KL-divergence regularization term (or equivalently the risk-sensitive MDP with an entropy risk measure). We showcase its streamlined design and less stringent assumptions due to the equivalence and analyze its sample complexity.

Assessing the potential impact of papers is of great significance to both academia and industry (Wang, Song and Barabási, 2013), especially given the exponential annual growth in the number of papers (Lo, Wang, Neumann, Kinney and Weld, 2020; Chu and Evans, 2021; Xue, He, Liu, Jiang, Zhao and Lu, 2023). As the numerical value of the scientific impact could be difficult to determine, citation count is frequently employed as a rough estimate (Evans and Reimer, 2009; Sinatra, Wang, Deville, Song and Barabási, 2016; Jiang, Koch and Sun, 2021). Actually, the dynamics in citation networks cannot be ignored. For example, the "sleeping beauties" (Van Raan, 2004) phenomenon indicates that the citations of a paper can vary considerably in different time periods. Besides the content quality, the future citations of a paper will be influenced by newly published papers (Funk and Owen-Smith, 2017; Park, Leahey and Funk, 2023). New papers may be successors to older ones, discovering the importance of previous works, thereby drawing more citations for them; or new papers may be competing with older ones, correcting or improving the previous works, thus making them lose potential citations. Therefore, it's imperative to capture dynamics of the citation network to accurately predict the future citations of a target paper. Previous studies within informetrics have primarily concentrated on content information or citation networks of papers.

Although we have witnessed great success of pre-trained models in natural language processing (NLP) and computer vision (CV), limited progress has been made for general time series analysis. Unlike NLP and CV where a unified model can be used to perform different tasks, specially designed approach still dominates in each time series analysis task such as classification, anomaly detection, forecasting, and few-shot learning. The main challenge that blocks the development of pre-trained model for time series analysis is the lack of a large amount of data for training. In this work, we address this challenge by leveraging language or CV models, pre-trained from billions of tokens, for time series analysis. Specifically, we refrain from altering the self-attention and feedforward layers of the residual blocks in the pre-trained language or image model. This model, known as the Frozen Pretrained Transformer (FPT), is evaluated through fine-tuning on all major types of tasks involving time series. Our results demonstrate that pre-trained models on natural language or images can lead to a comparable or state-of-the-art performance in all main time series analysis tasks, as illustrated in Figure 1. We also found both theoretically and empirically that the self-attention module behaviors similarly to principle component analysis (PCA), an observation that helps explains how transformer bridges the domain gap and a crucial step towards understanding the universality of a pre-trained transformer.