ADGym: Design Choices for Deep Anomaly Detection

Deep learning (DL) techniques have recently been applied to anomaly detection (AD), yielding successful outcomes in areas such as finance, medical services, and cloud computing. However, much of the current research evaluates a deep AD algorithm holistically, failing to understand the contributions of individual design choices like loss functions and network architectures. Consequently, the importance of prerequisite steps, such as preprocessing, might be overshadowed by the spotlight on novel loss functions and architectures. In this paper, we address these oversights by posing two questions: (i) Which components (i.e., design choices) of deep AD methods are pivotal in detecting anomalies? (ii) How can we construct tailored AD algorithms for specific datasets by selecting the best design choices automatically, rather than relying on generic, pre-existing solutions? To this end, we introduce ADGym, the first platform designed for comprehensive evaluation and automatic selection of AD design elements in deep methods. Extensive experiments reveal that merely adopting existing leading methods is not ideal. Models crafted using ADGym markedly surpass current state-of-the-art techniques.

Inclusive FinTech Lending via Contrastive Learning and Domain Adaptation

FinTech lending (e.g., micro-lending) has played a significant role in facilitating financial inclusion. It has reduced processing times and costs, enhanced the user experience, and made it possible for people to obtain loans who may not have qualified for credit from traditional lenders. However, there are concerns about the potentially biased algorithmic decision-making during loan screening. Machine learning algorithms used to evaluate credit quality can be influenced by representation bias in the training data, as we only have access to the default outcome labels of approved loan applications, for which the borrowers' socioeconomic characteristics are better than those of rejected ones. In this case, the model trained on the labeled data performs well on the historically approved population, but does not generalize well to borrowers of low socioeconomic background. In this paper, we investigate the problem of representation bias in loan screening for a real-world FinTech lending platform. We propose a new Transformer-based sequential loan screening model with self-supervised contrastive learning and domain adaptation to tackle this challenging issue. We use contrastive learning to train our feature extractor on unapproved (unlabeled) loan applications and use domain adaptation to generalize the performance of our label predictor. We demonstrate the effectiveness of our model through extensive experimentation in the real-world micro-lending setting. Our results show that our model significantly promotes the inclusiveness of funding decisions, while also improving loan screening accuracy and profit by 7.10% and 8.95%, respectively. We also show that incorporating the test data into contrastive learning and domain adaptation and labeling a small ratio of test data can further boost model performance.

Weakly Supervised Anomaly Detection: A Survey

Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news. However, obtaining complete, accurate, and precise labels for AD tasks can be expensive and challenging due to the cost and difficulties in data annotation. To address this issue, researchers have developed AD methods that can work with incomplete, inexact, and inaccurate supervision, collectively summarized as weakly supervised anomaly detection (WSAD) methods. In this study, we present the first comprehensive survey of WSAD methods by categorizing them into the above three weak supervision settings across four data modalities (i.e., tabular, graph, time-series, and image/video data). For each setting, we provide formal definitions, key algorithms, and potential future directions. To support future research, we conduct experiments on a selected setting and release the source code, along with a collection of WSAD methods and data.

Language Agnostic Multilingual Information Retrieval with Contrastive Learning

Multilingual information retrieval is challenging due to the lack of training datasets for many low-resource languages. We present an effective method by leveraging parallel and non-parallel corpora to improve the pretrained multilingual language models' cross-lingual transfer ability for information retrieval. We design the semantic contrastive loss as regular contrastive learning to improve the cross-lingual alignment of parallel sentence pairs, and we propose a new contrastive loss, the language contrastive loss, to leverage both parallel corpora and non-parallel corpora to further improve multilingual representation learning. We train our model on an English information retrieval dataset, and test its zero-shot transfer ability to other languages. Our experiment results show that our method brings significant improvement to prior work on retrieval performance, while it requires much less computational effort. Our model can work well even with a small number of parallel corpora. And it can be used as an add-on module to any backbone and other tasks. Our code is available at: https://github.com/xiyanghu/multilingualIR.

Benchmarking Node Outlier Detection on Graphs

Graph outlier detection is an emerging but crucial machine learning task with numerous applications. Despite the proliferation of algorithms developed in recent years, the lack of a standard and unified setting for performance evaluation limits their advancement and usage in real-world applications. To tap the gap, we present, (to our best knowledge) the first comprehensive unsupervised node outlier detection benchmark for graphs called UNOD, with the following highlights: (1) evaluating fourteen methods with backbone spanning from classical matrix factorization to the latest graph neural networks; (2) benchmarking the method performance with different types of injected outliers and organic outliers on real-world datasets; (3) comparing the efficiency and scalability of the algorithms by runtime and GPU memory usage on synthetic graphs at different scales. Based on the analyses of extensive experimental results, we discuss the pros and cons of current UNOD methods, and point out multiple crucial and promising future research directions.

ADBench: Anomaly Detection Benchmark

Given a long list of anomaly detection algorithms developed in the last few decades, how do they perform with regard to (i) varying levels of supervision, (ii) different types of anomalies, and (iii) noisy and corrupted data? In this work, we answer these key questions by conducting (to our best knowledge) the most comprehensive anomaly detection benchmark with 30 algorithms on 55 benchmark datasets, named ADBench. Our extensive experiments (93,654 in total) identify meaningful insights into the role of supervision and anomaly types, and unlock future directions for researchers in algorithm selection and design. With ADBench, researchers can easily conduct comprehensive and fair evaluations for newly proposed methods on the datasets (including our contributed ones from natural language and computer vision domains) against the existing baselines. To foster accessibility and reproducibility, we fully open-source ADBench and the corresponding results.

PyGOD: A Python Library for Graph Outlier Detection

PyGOD is an open-source Python library for detecting outliers on graph data. As the first comprehensive library of its kind, PyGOD supports a wide array of leading graph-based methods for node-, edge-, subgraph-, and graph-level outlier detection, under a unified, well-documented API designed for use by both researchers and practitioners. To overcome the scalability issue in large graphs, we provide advanced functionalities for selected models, including mini-batch and sampling. PyGOD is equipped with best practices to foster code reliability and maintainability, including unit testing, continuous integration, and code coverage. To foster accessibility, PyGOD is released under a permissive BSD-license at https://github.com/pygod-team/pygod/ and the Python Package Index (PyPI).

Uncovering the Source of Machine Bias

We develop a structural econometric model to capture the decision dynamics of human evaluators on an online micro-lending platform, and estimate the model parameters using a real-world dataset. We find two types of biases in gender, preference-based bias and belief-based bias, are present in human evaluators' decisions. Both types of biases are in favor of female applicants. Through counterfactual simulations, we quantify the effect of gender bias on loan granting outcomes and the welfare of the company and the borrowers. Our results imply that both the existence of the preference-based bias and that of the belief-based bias reduce the company's profits. When the preference-based bias is removed, the company earns more profits. When the belief-based bias is removed, the company's profits also increase. Both increases result from raising the approval probability for borrowers, especially male borrowers, who eventually pay back loans. For borrowers, the elimination of either bias decreases the gender gap of the true positive rates in the credit risk evaluation. We also train machine learning algorithms on both the real-world data and the data from the counterfactual simulations. We compare the decisions made by those algorithms to see how evaluators' biases are inherited by the algorithms and reflected in machine-based decisions. We find that machine learning algorithms can mitigate both the preference-based bias and the belief-based bias.

ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions

Outlier detection refers to the identification of data points that deviate from a general data distribution. Existing unsupervised approaches often suffer from high computational cost, complex hyperparameter tuning, and limited interpretability, especially when working with large, high-dimensional datasets. To address these issues, we present a simple yet effective algorithm called ECOD (Empirical-Cumulative-distribution-based Outlier Detection), which is inspired by the fact that outliers are often the "rare events" that appear in the tails of a distribution. In a nutshell, ECOD first estimates the underlying distribution of the input data in a nonparametric fashion by computing the empirical cumulative distribution per dimension of the data. ECOD then uses these empirical distributions to estimate tail probabilities per dimension for each data point. Finally, ECOD computes an outlier score of each data point by aggregating estimated tail probabilities across dimensions. Our contributions are as follows: (1) we propose a novel outlier detection method called ECOD, which is both parameter-free and easy to interpret; (2) we perform extensive experiments on 30 benchmark datasets, where we find that ECOD outperforms 11 state-of-the-art baselines in terms of accuracy, efficiency, and scalability; and (3) we release an easy-to-use and scalable (with distributed support) Python implementation for accessibility and reproducibility.

COPOD: Copula-Based Outlier Detection

Outlier detection refers to the identification of rare items that are deviant from the general data distribution. Existing approaches suffer from high computational complexity, low predictive capability, and limited interpretability. As a remedy, we present a novel outlier detection algorithm called COPOD, which is inspired by copulas for modeling multivariate data distribution. COPOD first constructs an empirical copula, and then uses it to predict tail probabilities of each given data point to determine its level of "extremeness". Intuitively, we think of this as calculating an anomalous p-value. This makes COPOD both parameter-free, highly interpretable, and computationally efficient. In this work, we make three key contributions, 1) propose a novel, parameter-free outlier detection algorithm with both great performance and interpretability, 2) perform extensive experiments on 30 benchmark datasets to show that COPOD outperforms in most cases and is also one of the fastest algorithms, and 3) release an easy-to-use Python implementation for reproducibility.