Implied Volatility Surface (IVS) prediction is critical for options hedging, portfolio management, and risk control. These applications encounter significant challenges, including imbalanced data distributions and inherent uncertainties in forecasting. Recent advances relying on deep learning have led to significant progress in addressing these issues. However, several key problems have not yet been solved: (i) Moneyness and maturities of traded options change over time. Therefore, proper spatio-temporal alignment is an essential prerequisite for the downstream forecasting exercise. (ii) Different regions of the IVS are unevenly informed because of liquidity constraints and therefore should not be modeled uniformly. (iii) The complex interconnections among data points in the IVS from various perspectives-such as the well-known 'smirk' patterns across dimensions-are neither explicitly addressed nor effectively captured by existing models. To address these issues, we propose a novel end-to-end heterogeneous cross(x)-view aligned graph attention network (Hexagon-Net), which aligns historical IVS data, learns distinctive IVS patterns, propagates predictive information, and forecasts future IVS movements simultaneously. Extensive experiments on stock index options datasets demonstrate that Hexagon-Net significantly and consistently outperforms the previous approaches in IVS modeling and deep learning. Additionally, we present further experiments-such as ablation studies, sensitivity analyses, and alternative configurations-to explore the reasons behind its superior performance.

Recent developments in the applications of deep reinforcement learning methods to portfolio selection have achieved superior performance to conventional methods. However, two major challenges remain unaddressed in these models and inevitably lead to the deterioration of model performance. First, asset characteristics often suffer from low and unstable signal-to-noise ratios, leading to poor learning robustness of the predictive feature representations. Second, existing literature fails to consider the complexity and diversity in long-term and short-term spatio-temporal predictive relations between the feature sequences and portfolio objectives. To tackle these problems, we propose a novel Context-Aware Frequency-Embedding Graph Convolution Network (Cafe-GCN) for spatio-temporal portfolio selection. It contains three important modules: (1) frequency-embedding block that explicitly captures the short-term and long-term predictive information embedded in asset characteristics meanwhile filtering out noise; (2) context-aware block that learns multiscale temporal dependencies in the feature space; and (3) multi-relation graph convolutional block that exploits both static and dynamic spatial relations among assets. Extensive experiments on two real-world datasets demonstrate that Cafe-GCN consistently outperforms proposed techniques in the literature.

Missing values and unreleased figures are common but highly important for backtesting and real-time analysis in the financial industry, yet underexploited in the existing literature. In this paper, we focus on the issue of empirical asset pricing, where the cross-section of future asset returns is a function of lagged firm characteristics that vary in time frequencies and missing ratios. Most of the existing imputation methods cannot fully capture the complex and evolving spatio-temporal relations among firm-level characteristics. In particular, these methods fail to explicitly consider the spatial relations and feature structure in the stock network where we have to process granular data of thousands of stocks and hundreds of characteristics for each stock. To address these challenges, we propose a spatio-temporal diffusion model (STDM) that gradually recovers the masked financial data conditioning on high-dimensional stock-and-characteristics historical data. We propose characteristic-specific projection to construct characteristic-level features at both ends of the STDM, meanwhile maintaining firm-level features in the middle of the STDM to largely reduce the computational memory. Moreover, along with the temporal attention, we design a spatial graph convolutional network, making it computationally efficient and effective to learn time-varying spatio-temporal interdependence across firms. We further employ an implicit sampler that greatly accelerates the inference procedure so that the STDM is able to produce high-quality point and density estimates of missing and real-time firm characteristics within a few steps. We evaluate our model on the most comprehensive open-source dataset 'OSAP' and generate state-of-the-art performance in extensive experiments.

The surge in merchant fraud poses a significant threat to market order and consumer security. Effective security monitoring for merchants is crucial in safeguarding the digital life ecosystem and users' financial well-being. Detecting daily fraudulent payment transactions, a challenging task for current methods, requires efficient transformation of transactions into embeddings, especially in representing merchants based on their behavioral transactions. To address this, we propose the Grouping Sampling-based Sequence Generation (GSSG) method to generate meaningful sequences, enabling interactions among correlated transactions. We introduce Hierarchical Embedding Learning (HEL) and Hierarchical Masking pre-training (HMP) for the effective representation of hierarchical structures within flat transaction sequences. Pretrained on WeChat Pay data, our model, PTP, demonstrates superior performance in downstream fraud transaction detection, especially in few-shot learning scenarios, showcasing great potential in payment transaction scenarios.

Heterogeneous Information Network(HIN) collective classification aims to classify one type of node, which is associated with multiple types of nodes through multiple types of relations. Previous studies have revealed that exploiting the relative importance of relation types is quite useful for improving node classification performance. We propose a Tensor-based Markov chain (T-Mark) model to improve the nodes classification accuracy by predicting the labels for unlabeled nodes and the importance ranking of relationship types automatically and simultaneously. Specifically, we build two tensor equations according to the HIN structure and content similarities among nodes of both labeled and unlabeled data. Consequently, We solve the semi-supervised T-Mark model by using an iterative process until obtaining two stationary distributions for labels and relation types. Experimental results on several real-world datasets demonstrate the effectiveness of T-Mark.

Multi-source domain adaptation (MSDA) aims to leverage the knowledge in multiple source domains to assist the prediction in a target domain, where the source and target domains have different data distributions. This paper presents a MSDA model to investigate both domain discrepancy and domain relevance, whose interactions are also exploited to gradually refine the learning performance. Particularly, the proposed model contains two components, i.e., feature spaces learning and transferred weights learning. The former one minimizes the domain discrepancy and the latter one evaluates the domain relevance. Experimental results on several real-world datasets demonstrate the effectiveness of the proposed model.

Domain adaptation aims at extracting knowledge from auxiliary source domains to assist the learning task in a target domain. Since the distributions of the source and target domains are different, directly using source data to build a classifier for the target domain may hamper the classification performance on the target data. In this paper, we propose to find a feature subset that is both transferable and discriminative, so that both the domain discrepancy and the classification loss measured on the selected features can be reduced. To achieve this, we formulate a new sparse learning model that is able to jointly reduce the domain discrepancy and select informative features for classification. Extensive experiments on real-world data sets demonstrate the effectiveness of the proposed method.

Portfolio Selection is an important real-world financial task and has attracted extensive attention in artificial intelligence communities. This task, however, has two main difficulties: (i) the non-stationary price series and complex asset correlations make the learning of feature representation very hard; (ii) the practicality principle in financial markets requires controlling both transaction and risk costs. Most existing methods adopt handcraft features and/or consider no constraints for the costs, which may make them perform unsatisfactorily and fail to control both costs in practice. In this paper, we propose a cost-sensitive portfolio selection method with deep reinforcement learning. Specifically, a novel two-stream portfolio policy network is devised to extract both price series patterns and asset correlations, while a new cost-sensitive reward function is developed to maximize the accumulated return and constrain both costs via reinforcement learning. We theoretically analyze the near-optimality of the proposed reward, which shows that the growth rate of the policy regarding this reward function can approach the theoretical optimum. We also empirically evaluate the proposed method on real-world datasets. Promising results demonstrate the effectiveness and superiority of the proposed method in terms of profitability, cost-sensitivity and representation abilities.

Heterogeneous Information Network (HIN) collecitve classification studies the problem of predicting labels for one type of nodes in a HIN which contains multiple types of nodes multiple types of links among them. Previous studies have revealed that exploiting relative importance of links is quite useful to improve node classification performance as connected nodes tend to have similar labels. Most existing approaches exploit the relative importance of links either by directly counting the number of connections among nodes or by learning the weight of each type of link from labeled data only. However, these approaches either neglect the importance of types of links to the class labels or may lead to overfitting problem. We propose aTensor-basedMarkov chain (T-Mark) approach, which is able to automatically and simultaneously predict the labels for unlabeled nodes and give the relative importance of types of links that actually improve the classification accuracy. Specifically, we build two tensor equations by using the HIN and features of nodes from both labeled and unlabeled data. A Markov chain-based model is proposed and it is solved by an iterative process to obtain the stationary distributions. Theoretical analyses of the existence and uniqueness of such probability distributions are given. Extensive experimental results demonstrate that T-Mark is able to achieve superior performance in the comparison and obtain reasonable relative importance of links.

One of the main challenges in multi-source domain adaptation is how to reduce the domain discrepancy between each source domain and a target domain, and then evaluate the domain relevance to determine how much knowledge should be transferred from different source domains to the target domain. However, most prior approaches barely consider both discrepancies and relevance among domains. In this paper, we propose an algorithm, called Iterative Refinement based on Feature Selection and the Wasserstein distance (IRFSW), to solve semi-supervised domain adaptation with multiple sources. Specifically, IRFSW aims to explore both the discrepancies and relevance among domains in an iterative learning procedure, which gradually refines the learning performance until the algorithm stops. In each iteration, for each source domain and the target domain, we develop a sparse model to select features in which the domain discrepancy and training loss are reduced simultaneously. Then a classifier is constructed with the selected features of the source and labeled target data. After that, we exploit optimal transport over the selected features to calculate the transferred weights. The weight values are taken as the ensemble weights to combine the learned classifiers to control the amount of knowledge transferred from source domains to the target domain. Experimental results validate the effectiveness of the proposed method.

Domain adaptation aims at extracting knowledge from auxiliary source domains to assist the learning task in a target domain. In classification problems, since the distributions of the source and target domains are different, directly using source data to build a classifier for the target domain may hamper the classification performance on the target data. Fortunately, in many tasks, there can be some features that are transferable, i.e., the source and target domains share similar properties. On the other hand, it is common that the source data contain noisy features which may degrade the learning performance in the target domain. This issue, however, is barely studied in existing works. In this paper, we propose to find a feature subset that is transferable across the source and target domains. As a result, the domain discrepancy measured on the selected features can be reduced. Moreover, we seek to find the most discriminative features for classification. To achieve the above goals, we formulate a new sparse learning model that is able to jointly reduce the domain discrepancy and select informative features for classification. We develop two optimization algorithms to address the derived learning problem. Extensive experiments on real-world data sets demonstrate the effectiveness of the proposed method.

Portfolio Selection is an important real-world financial task and has attracted extensive attention in artificial intelligence communities. This task, however, has two main difficulties: (i) the non-stationary price series and complex asset correlations make the learning of feature representation very hard; (ii) the practicality principle in financial markets requires controlling both transaction and risk costs. Most existing methods adopt handcraft features and/or consider no constraints for the costs, which may make them perform unsatisfactorily and fail to control both costs in practice. In this paper, we propose a cost-sensitive portfolio selection method with deep reinforcement learning. Specifically, a novel two-stream portfolio policy network is devised to extract both price series patterns and asset correlations, while a new cost-sensitive reward function is developed to maximize the accumulated return and constrain both costs via reinforcement learning. We theoretically analyze the near-optimality of the proposed reward, which shows that the growth rate of the policy regarding this reward function can approach the theoretical optimum. We also empirically evaluate the proposed method on real-world datasets. Promising results demonstrate the effectiveness and superiority of the proposed method in terms of profitability, cost-sensitivity and representation abilities.

Domain adaptation aims at improving the performance of learning tasks in a target domain by leveraging the knowledge extracted from a source domain. To this end, one can perform knowledge transfer between these two domains. However, this problem becomes extremely challenging when the data of these two domains are characterized by different types of features, i.e., the feature spaces of the source and target domains are different, which is referred to as heterogeneous domain adaptation (HDA). To solve this problem, we propose a novel model called Knowledge Preserving and Distribution Alignment (KPDA), which learns an augmented target space by jointly minimizing information loss and maximizing domain distribution alignment. Specifically, we seek to discover a latent space, where the knowledge is preserved by exploiting the Laplacian graph terms and reconstruction regularizations. Moreover, we adopt the Maximum Mean Discrepancy to align the distributions of the source and target domains in the latent space. Mathematically, KPDA is formulated as a minimization problem with orthogonal constraints, which involves two projection variables. Then, we develop an algorithm based on the Gauss–Seidel iteration scheme and split the problem into two subproblems, which are solved by searching algorithms based on the Barzilai–Borwein (BB) stepsize. Promising results demonstrate the effectiveness of the proposed method.

Deep learning has brought great progress for the sequential recommendation (SR) tasks. With advanced network architectures, sequential recommender models can be stacked with many hidden layers, e.g., up to 100 layers on real-world recommendation datasets. Training such a deep network is difficult because it can be computationally very expensive and takes much longer time, especially in situations where there are tens of billions of user-item interactions. To deal with such a challenge, we present StackRec, a simple, yet very effective and efficient training framework for deep SR models by iterative layer stacking. Specifically, we first offer an important insight that hidden layers/blocks in a well-trained deep SR model have very similar distributions. Enlightened by this, we propose the stacking operation on the pre-trained layers/blocks to transfer knowledge from a shallower model to a deep model, then we perform iterative stacking so as to yield a much deeper but easier-to-train SR model. We validate the performance of StackRec by instantiating it with four state-of-the-art SR models in three practical scenarios with real-world datasets. Extensive experiments show that StackRec achieves not only comparable performance, but also substantial acceleration in training time, compared to SR models that are trained from scratch. Codes are available at https://github.com/wangjiachun0426/StackRec.

Sparse principal component analysis (SPCA) produces principal components with sparse loadings, which is very important for handling data with many irrelevant features and also critical to interpret the results. To deal with orthogonal constraints, most previous approaches address SPCA with several components using techniques such as deflation technique and convex relaxations. However, the deflation technique usually suffers from suboptimal solutions due to poor approximations. On the other hand, the convex relaxations are often computationally expensive. To address the above issues, in this paper, we propose to address SPCA over the Stiefel manifold directly, and develop a stabilized Alternating Direction Method of Multipliers (SADMM) to handle the nonconvex orthogonal constraints. Compared to traditional ADMM, the proposed SADMM method converges well with a wide range of parameters and obtains a better solution. We also theoretically study the convergence property of the proposed SADMM method. Furthermore, most existing methods ignore an inherent drawback of SPCA - the importance of different components is not considered when doing feature selection, which often makes the selected features nonoptimal. To address this, we further propose a two-stage method which considers the importance of different components to select the most important features. Empirical studies on both synthetic and real-world datasets show that the proposed algorithms achieve better performance compared to existing state-of-the-art methods.

Online Active Learning (OAL) aims to manage unlabeled datastream by selectively querying the label of data. OAL is applicable to many real-world problems, such as anomaly detection in health-care and finance. In these problems, there are two key challenges: the query budget is often limited; the ratio between classes is highly imbalanced. In practice, it is quite difficult to handle imbalanced unlabeled datastream when only a limited budget of labels can be queried for training. To solve this, previous OAL studies adopt either asymmetric losses or queries (an isolated asymmetric strategy) to tackle the imbalance, and use first-order methods to optimize the cost-sensitive measure. However, the isolated strategy limits their performance in class imbalance, while first-order methods restrict their optimization performance. In this article, we propose a novel Online Adaptive Asymmetric Active learning algorithm, based on a new asymmetric strategy (merging both asymmetric losses and queries strategies), and second-order optimization. We theoretically analyze its mistake bound and cost-sensitive metric bounds. Moreover, to better balance performance and efficiency, we enhance our algorithm via a sketching technique, which significantly accelerates the computational speed with quite slight performance degradation. Promising results demonstrate the effectiveness and efficiency of the proposed methods.

Multi-source domain adaptation has received considerable attention due to its effectiveness of leveraging the knowledge from multiple related sources with different distributions to enhance the learning performance. One of the fundamental challenges in multi-source domain adaptation is how to determine the amount of knowledge transferred from each source domain to the target domain. To address this issue, we propose a new algorithm, called Domain-attention Conditional Wasserstein Distance (DCWD), to learn transferred weights for evaluating the relatedness across the source and target domains. In DCWD, we design a new conditional Wasserstein distance objective function by taking the label information into consideration to measure the distance between a given source domain and the target domain. We also develop an attention scheme to compute the transferred weights of different source domains based on their conditional Wasserstein distances to the target domain. After that, the transferred weights can be used to reweight the source data to determine their importance in knowledge transfer. We conduct comprehensive experiments on several real-world data sets, and the results demonstrate the effectiveness and efficiency of the proposed method.

In this article, we study the problem of online heterogeneous transfer learning, where the objective is to make predictions for a target data sequence arriving in an online fashion, and some offline labeled instances from a heterogeneous source domain are provided as auxiliary data. The feature spaces of the source and target domains are completely different, thus the source data cannot be used directly to assist the learning task in the target domain. To address this issue, we take advantage of unlabeled co-occurrence instances as intermediate supplementary data to connect the source and target domains, and perform knowledge transition from the source domain into the target domain. We propose a novel online heterogeneous transfer learning algorithm called O nline H eterogeneous K nowledge T ransition (OHKT) for this purpose. In OHKT, we first seek to generate pseudo labels for the co-occurrence data based on the labeled source data, and then develop an online learning algorithm to classify the target sequence by leveraging the co-occurrence data with pseudo labels. Experimental results on real-world data sets demonstrate the effectiveness and efficiency of the proposed algorithm.

This paper investigates Online Active Learning (OAL) for imbalanced unlabeled datastream, where only a budget of labels can be queried to optimize some cost-sensitive performance measure. OAL can solve many real-world problems, such as anomaly detection in healthcare, finance and network security. In these problems, there are two key challenges: the query budget is often limited; the ratio between two classes is highly imbalanced. To address these challenges, existing work of OAL adopts either asymmetric losses or queries (an isolated asymmetric strategy) to tackle the imbalance, and uses first-order methods to optimize the cost-sensitive measure. However, they may incur two deficiencies: (1) the poor ability in handling imbalanced data due to the isolated asymmetric strategy; (2) relative slow convergence rate due to the first-order optimization. In this paper, we propose a novel Online Adaptive Asymmetric Active (OA3) learning algorithm, which is based on a new asymmetric strategy (merging both the asymmetric losses and queries strategies), and second-order optimization. We theoretically analyze its bounds, and also empirically evaluate it on four real-world online anomaly detection tasks. Promising results confirm the effectiveness and robustness of the proposed algorithm in various application domains.

In this paper, we study relations ranking and object classification for multi-relational data where objects are interconnected by multiple relations. The relations among objects should be exploited for achieving a good classification. While most existing approaches exploit either by directly counting the number of connections among objects or by learning the weight of each relation from labeled data only. In this paper, we propose an algorithm, TensorRRCC, which is able to determine the ranking of relations and the labels of objects simultaneously. Our basic idea is that highly ranked relations within a class should play more important roles in object classification, and class membership information is important for determining a ranking quality over the relations w.r.t. a specific learning task. TensorRRCC implements the idea by modeling a Markov chain on transition probability graphs from connection and feature information with both labeled and unlabeled objects and propagates the ranking scores of relations and relevant classes of objects. An iterative progress is proposed to solve a set of tensor equations to obtain the stationary distribution of relations and objects. We compared our algorithm with current collective classification algorithms on two real-world data sets and the experimental results show the superiority of our method.

Transfer learning techniques have been broadly applied in applications where labeled data in a target domain are difficult to obtain while a lot of labeled data are available in related source domains. In practice, there can be multiple source domains that are related to the target domain, and how to combine them is still an open problem. In this paper, we seek to leverage labeled data from multiple source domains to enhance classification performance in a target domain where the target data are received in an online fashion. This problem is known as the online transfer learning problem. To achieve this, we propose novel online transfer learning paradigms in which the source and target domains are leveraged adaptively. We consider two different problem settings: homogeneous transfer learning and heterogeneous transfer learning. The proposed methods work in an online manner, where the weights of the source domains are adjusted dynamically. We provide the mistake bounds of the proposed methods and perform comprehensive experiments on real-world data sets to demonstrate the effectiveness of the proposed algorithms.

Transfer learning has been proven to be effective for the problems where training data from a source domain and test data from a target domain are drawn from different distributions. To reduce the distribution divergence between the source domain and the target domain, many previous studies have been focused on designing and optimizing objective functions with the Euclidean distance to measure dissimilarity between instances. However, in some real-world applications, the Euclidean distance may be inappropriate to capture the intrinsic similarity or dissimilarity between instances. To deal with this issue, in this paper, we propose a metric transfer learning framework (MTLF) to encode metric learning in transfer learning. In MTLF, instance weights are learned and exploited to bridge the distributions of different domains, while Mahalanobis distance is learned simultaneously to maximize the intra-class distances and minimize the inter-class distances for the target domain. Unlike previous work where instance weights and Mahalanobis distance are trained in a pipelined framework that potentially leads to error propagation across different components, MTLF attempts to learn instance weights and a Mahalanobis distance in a parallel framework to make knowledge transfer across domains more effective. Furthermore, we develop general solutions to both classification and regression problems on top of MTLF, respectively. We conduct extensive experiments on several real-world datasets on object recognition, handwriting recognition, and WiFi location to verify the effectiveness of MTLF compared with a number of state-of-the-art methods.

Multi-label classification deals with the problem where each example is associated with multiple class labels. Since the labels are often dependent to other labels, exploiting label dependencies can significantly improve the multi-label classification performance. The label dependency in existing studies is often given as prior knowledge or learned from the labels only. However, in many real applications, such prior knowledge may not be available, or labeled information might be very limited. In this paper, we propose a new algorithm, called Ml-Forest , to learn an ensemble of hierarchical multi-label classifier trees to reveal the intrinsic label dependencies. In Ml-Forest, we construct a set of hierarchical trees, and develop a label transfer mechanism to identify the multiple relevant labels in a hierarchical way. In general, the relevant labels at higher levels of the trees capture more discriminable label concepts, and they will be transferred into lower level children nodes that are harder to discriminate. The relevant labels in the hierarchy are then aggregated to compute label dependency and make the final prediction. Our empirical study shows encouraging results of the proposed algorithm in comparison with the state-of-the-art multi-label classification algorithms under Friedman test and post-hoc Nemenyi test.

Query-URL relevance, measuring the relevance of each retrieved URL with respect to a given query, is one of the fundamental criteria to evaluate the performance of commercial search engines. The traditional way to collect reliable and accurate query-URL relevance requires multiple annotators to provide their individual judgments based on their subjective expertise (e.g., understanding of user intents). In this case, the annotators’ subjectivity reflected in each annotator individual judgment (AIJ) inevitably affects the quality of the ground truth relevance (GTR). But to the best of our knowledge, the potential impact of AIJs on estimating GTRs has not been studied and exploited quantitatively by existing work. This article first studies how multiple AIJs and GTRs are correlated. Our empirical studies find that the multiple AIJs possibly provide more cues to improve the accuracy of estimating GTRs. Inspired by this finding, we then propose a novel approach to integrating the multiple AIJs with the features characterizing query-URL pairs for estimating GTRs more accurately. Furthermore, we conduct experiments in a commercial search engine—Baidu.com—and report significant gains in terms of the normalized discounted cumulative gains.

In recent years much effort has been devoted to Collective Classification (CC) techniques for predicting labels of linked instances. Given a large number of labeled data, conventional CC algorithms make use of local labeled neighbours to increase accuracy. However, in many real-world applications, labeled data are limited and very expensive to obtain. In this situation, most of the data have no connection to labeled data, and supervision knowledge cannot be obtained from the local connections. Recently, Semi-Supervised Collective Classification (SSCC) has been examined to leverage unlabeled data for enhancing the classification performance of CC. In this paper we propose a probabilistic generative model with network regularization (GMNR) for SSCC. Our main idea is to compute label probability distributions for unlabeled instances by maximizing both the log-likelihood in the generative model and the label smoothness on the network topology of data. The proposed generative model is based on the Probabilistic Latent Semantic Analysis (PLSA) method using attribute features of all instances. A network regularizer is employed to smooth the label probability distributions on the network topology of data. Finally, we develop an effective EM algorithm to compute the label probability distributions for label prediction. Experimental results on three real sparsely-labeled network datasets show that the proposed model GMNR outperforms state-of-the-art CC algorithms and other SSCC algorithms.