AI prevails in financial fraud detection and decision making. Yet, due to concerns about biased automated decision making or profiling, regulations mandate that final decisions are made by humans. Financial fraud investigators face the challenge of manually synthesizing vast amounts of unstructured information, including AI alerts, transaction histories, social media insights, and governmental laws. Current Visual Analytics (VA) systems primarily support isolated aspects of this process, such as explaining binary AI alerts and visualizing transaction patterns, thus adding yet another layer of information to the overall complexity. In this work, we propose a framework where the VA system supports decision makers throughout all stages of financial fraud investigation, including data collection, information synthesis, and human criteria iteration. We illustrate how VA can claim a central role in AI-aided decision making, ensuring that human judgment remains in control while minimizing potential biases and labor-intensive tasks.

Modern machine learning models require large labelled datasets to achieve good performance, but manually labelling large datasets is expensive and time-consuming. The data programming paradigm enables users to label large datasets efficiently but produces noisy labels, which deteriorates the downstream model's performance. The active learning paradigm, on the other hand, can acquire accurate labels but only for a small fraction of instances. In this paper, we propose ActiveDP, an interactive framework bridging active learning and data programming together to generate labels with both high accuracy and coverage, combining the strengths of both paradigms. Experiments show that ActiveDP outperforms previous weak supervision and active learning approaches and consistently performs well under different labelling budgets.

In the last post on high-dimensional learning, we saw that learning in high dimensions is impossible without assumptions due to the curse of dimensionality, i.e., the number of samples required in our learning problem grows exponentially with dimensions. We also introduced the main geometric function spaces, in which our points in high-dimensional space can be considered as signals over the low-dimensional geometric domain. From this assumption, and to make learning tractable, I will present symmetry (in this post) and scale separation (in the next one). In addition, we also discussed the three kinds of errors we need to be aware of, namely, approximation error, statistical error, and optimization error. The approximation error increases if our function class decreases (the true function that we are trying to estimate is far outside of this class), which suggests having a large function class. In contrast, the statistical error implies we are unlikely to find the true function based on a finite number of data points. This error increases as the function class grows.

Graph Domain Adaptation with Localized Graph Signal Representations Yusuf Yi git Pilavcı, Eylem Tu g ce G uneyi, Cemil Cengiz and Elif Vural Abstract In this paper we propose a domain adaptation algorithm designed for graph domains. Given a source graph with many labeled nodes and a target graph with few or no labeled nodes, we aim to estimate the target labels by making use of the similarity between the characteristics of the variation of the label functions on the two graphs. Our assumption about the source and the target domains is that the local behaviour of the label function, such as its spread and speed of variation on the graph, bears resemblance between the two graphs. We estimate the unknown target labels by solving an optimization problem where the label information is transferred from the source graph to the target graph based on the prior that the projections of the label functions onto localized graph bases be similar between the source and the target graphs. In order to efficiently capture the local variation of the label functions on the graphs, spectral graph wavelets are used as the graph bases. Experimentation on various data sets shows that the proposed method yields quite satisfactory classification accuracy compared to reference domain adaptation methods. Keywords: Domain adaptation, spectral graph theory, graph signal processing, spectral graph wavelets, graph Laplacian 1 Introduction A common assumption in machine learning is that the training and the test data are sampled from the same distribution. Domain adaptation methods aim to provide solutions to machine learning problems by dealing with this distribution discrepancy. In domain adaptation, a source domain and a target domain are considered where the label information is mostly available for the data samples in the source domain, and few or none of the class labels are known in the target domain. The purpose is then to improve the learning performance in the target domain by making use Y. Y. Pilavcı is with the GIPSA Lab at Universit e Grenoble Alpes, Grenoble. C. Cengiz is with the Dept. of Computer Science and Engineering at Ko c University, Istanbul. Most part of this work was performed while the authors were at METU. 1 arXiv:1911.02883v1 A variety of approaches have been proposed so far for the domain adaptation problem. Some methods are based on reweighing the samples for removing the sample selection bias [1, 2]. Another common solution is to align the source and the target domains through feature space mappings.

Traditional machine learning algorithms assume that the training and test data have the same distribution, while this assumption can be easily violated in real applications. Learning by taking into account the changes in the data distribution is called domain adaptation. In this work, we treat the domain adaptation problem in a graph setting. We consider a source and a target data graph that are constructed with samples drawn from a source and a target data manifold. We study the problem of estimating the unknown labels on the target graph by employing the label information in the source graph and the similarity between the two graphs. We particularly focus on a setting where the target label function is learnt such that its spectrum (frequency content when regarded as a graph signal) is similar to that of the source label function. We first present an overview of the recent field of graph signal processing and introduce concepts such as the Fourier transform on graphs. We then propose a theoretical analysis of domain adaptation over graphs, and present performance bounds relating the target classification error to the properties of the graph topologies and the manifold geometries. Finally, we propose a graph domain adaptation algorithm inspired by our theoretical findings, which estimates the label functions while learning the source and target graph topologies at the same time. Experiments on synthetic and real data sets suggest that the proposed method outperforms baseline approaches.

Classical clustering algorithms typically either lack an underlying probability framework to make them predictive or focus on parameter estimation rather than defining and minimizing a notion of error. Recent work addresses these issues by developing a probabilistic framework based on the theory of random labeled point processes and characterizing a Bayes clusterer that minimizes the number of misclustered points. The Bayes clusterer is analogous to the Bayes classifier. Whereas determining a Bayes classifier requires full knowledge of the feature-label distribution, deriving a Bayes clusterer requires full knowledge of the point process. When uncertain of the point process, one would like to find a robust clusterer that is optimal over the uncertainty, just as one may find optimal robust classifiers with uncertain feature-label distributions. Herein, we derive an optimal robust clusterer by first finding an effective random point process that incorporates all randomness within its own probabilistic structure and from which a Bayes clusterer can be derived that provides an optimal robust clusterer relative to the uncertainty. This is analogous to the use of effective class-conditional distributions in robust classification. After evaluating the performance of robust clusterers in synthetic mixtures of Gaussians models, we apply the framework to granular imaging, where we make use of the asymptotic granulometric moment theory for granular images to relate robust clustering theory to the application.

We propose a method for domain adaptation on graphs. Given sufficiently many observations of the label function on a source graph, we study the problem of transferring the label information from the source graph to a target graph for estimating the target label function. Our assumption about the relation between the two domains is that the frequency content of the label function, regarded as a graph signal, has similar characteristics over the source and the target graphs. We propose a method to learn a pair of coherent bases on the two graphs, such that the corresponding source and target graph basis vectors have similar spectral content, while "aligning" the two graphs at the same time so that the reconstructed source and target label functions have similar coefficients over the bases. Experiments on several types of data sets suggest that the proposed method compares quite favorably to reference domain adaptation methods. To the best of our knowledge, our treatment is the first to study the domain adaptation problem in a purely graph-based setting with no need for embedding the data in an ambient space. This feature is particularly convenient for many problems of interest concerning learning on graphs or networks.

Feature learning forms the cornerstone for tackling challenging learning problems in domains such as speech, computer vision and natural language processing. In this paper, we consider a novel class of matrix and tensor-valued features, which can be pre-trained using unlabeled samples. We present efficient algorithms for extracting discriminative information, given these pre-trained features and labeled samples for any related task. Our class of features are based on higher-order score functions, which capture local variations in the probability density function of the input. We establish a theoretical framework to characterize the nature of discriminative information that can be extracted from score-function features, when used in conjunction with labeled samples. We employ efficient spectral decomposition algorithms (on matrices and tensors) for extracting discriminative components. The advantage of employing tensor-valued features is that we can extract richer discriminative information in the form of an overcomplete representations. Thus, we present a novel framework for employing generative models of the input for discriminative learning.