Contextual Outlier Interpretation

Outlier detection plays an essential role in many data-driven applications to identify isolated instances that are different from the majority. While many statistical learning and data mining techniques have been used for developing more effective outlier detection algorithms, the interpretation of detected outliers does not receive much attention. Interpretation is becoming increasingly important to help people trust and evaluate the developed models through providing intrinsic reasons why the certain outliers are chosen. It is difficult, if not impossible, to simply apply feature selection for explaining outliers due to the distinct characteristics of various detection models, complicated structures of data in certain applications, and imbalanced distribution of outliers and normal instances. In addition, the role of contrastive contexts where outliers locate, as well as the relation between outliers and contexts, are usually overlooked in interpretation. To tackle the issues above, in this paper, we propose a novel Contextual Outlier INterpretation (COIN) method to explain the abnormality of existing outliers spotted by detectors. The interpretability for an outlier is achieved from three aspects: outlierness score, attributes that contribute to the abnormality, and contextual description of its neighborhoods. Experimental results on various types of datasets demonstrate the flexibility and effectiveness of the proposed framework compared with existing interpretation approaches

Extracting and Harnessing Interpretation in Data Mining

Machine learning, especially the recent deep learning technique, has aroused significant development to various data mining applications, including recommender systems, misinformation detection, outlier detection, and health informatics. Unfortunately, while complex models have achieved unprecedented prediction capability, they are often criticized as ``black boxes'' due to multiple layers of non-linear transformation and the hardly understandable working mechanism. To tackle the opacity issue, interpretable machine learning has attracted increasing attentions. Traditional interpretation methods mainly focus on explaining predictions of classification models with gradient based methods or local approximation methods. However, the natural characteristics of data mining applications are not considered, and the internal mechanisms of models are not fully explored. Meanwhile, it is unknown how to utilize interpretation to improve models. To bridge the gap, I developed a series of interpretation methods that gradually increase the transparency of data mining models. First, a fundamental goal of interpretation is providing the attribution of input features to model outputs. To adapt feature attribution to explaining outlier detection, I propose Contextual Outlier Interpretation (COIN). Second, to overcome the limitation of attribution methods that do not explain internal information inside models, I further propose representation interpretation methods to extract knowledge as a taxonomy. However, these post-hoc methods may suffer from interpretation accuracy and the inability to directly control model training process. Therefore, I propose an interpretable network embedding framework to explicitly control the meaning of latent dimensions. Finally, besides obtaining explanation, I propose to use interpretation to discover the vulnerability of models in adversarial circumstances, and then actively prepare models using adversarial training to improve their robustness against potential threats. My research of interpretable machine learning enables data scientists to better understand their models and discover defects for further improvement, as well as improves the experiences of customers who benefit from data mining systems. It broadly impacts fields such as Information Retrieval, Information Security, Social Computing, and Health Informatics

A Theoretical Approach to Characterize the Accuracy-Fairness Trade-off Pareto Frontier

While the accuracy-fairness trade-off has been frequently observed in the literature of fair machine learning, rigorous theoretical analyses have been scarce. To demystify this long-standing challenge, this work seeks to develop a theoretical framework by characterizing the shape of the accuracy-fairness trade-off Pareto frontier (FairFrontier), determined by a set of all optimal Pareto classifiers that no other classifiers can dominate. Specifically, we first demonstrate the existence of the trade-off in real-world scenarios and then propose four potential categories to characterize the important properties of the accuracy-fairness Pareto frontier. For each category, we identify the necessary conditions that lead to corresponding trade-offs. Experimental results on synthetic data suggest insightful findings of the proposed framework: (1) When sensitive attributes can be fully interpreted by non-sensitive attributes, FairFrontier is mostly continuous. (2) Accuracy can suffer a \textit{sharp} decline when over-pursuing fairness. (3) Eliminate the trade-off via a two-step streamlined approach. The proposed research enables an in-depth understanding of the accuracy-fairness trade-off, pushing current fair machine-learning research to a new frontier

Interactive System-wise Anomaly Detection

Anomaly detection, where data instances are discovered containing feature patterns different from the majority, plays a fundamental role in various applications. However, it is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data. Appropriate interactions are needed to interact with the systems and identify those with abnormal responses. Detecting system-wise anomalies is a challenging task due to several reasons including: how to formally define the system-wise anomaly detection problem; how to find the effective activation signal for interacting with systems to progressively collect the data and learn the detector; how to guarantee stable training in such a non-stationary scenario with real-time interactions? To address the challenges, we propose InterSAD (Interactive System-wise Anomaly Detection). Specifically, first, we adopt Markov decision process to model the interactive systems, and define anomalous systems as anomalous transition and anomalous reward systems. Then, we develop an end-to-end approach which includes an encoder-decoder module that learns system embeddings, and a policy network to generate effective activation for separating embeddings of normal and anomaly systems. Finally, we design a training method to stabilize the learning process, which includes a replay buffer to store historical interaction data and allow them to be re-sampled. Experiments on two benchmark environments, including identifying the anomalous robotic systems and detecting user data poisoning in recommendation models, demonstrate the superiority of InterSAD compared with state-of-the-art baselines methods