Out-Of-Bag Anomaly Detection

Data anomalies are ubiquitous in real world datasets, and can have an adverse impact on machine learning (ML) systems, such as automated home valuation. Detecting anomalies could make ML applications more responsible and trustworthy. However, the lack of labels for anomalies and the complex nature of real-world datasets make anomaly detection a challenging unsupervised learning problem. In this paper, we propose a novel model-based anomaly detection method, that we call Out-of- Bag anomaly detection, which handles multi-dimensional datasets consisting of numerical and categorical features. The proposed method decomposes the unsupervised problem into the training of a set of ensemble models. Out-of-Bag estimates are leveraged to derive an effective measure for anomaly detection. We not only demonstrate the state-of-the-art performance of our method through comprehensive experiments on benchmark datasets, but also show our model can improve the accuracy and reliability of an ML system as data pre-processing step via a case study on home valuation.

A General Framework for Fairness in Multistakeholder Recommendations

Contemporary recommender systems act as intermediaries on multi-sided platforms serving high utility recommendations from sellers to buyers. Such systems attempt to balance the objectives of multiple stakeholders including sellers, buyers, and the platform itself. The difficulty in providing recommendations that maximize the utility for a buyer, while simultaneously representing all the sellers on the platform has lead to many interesting research problems.Traditionally, they have been formulated as integer linear programs which compute recommendations for all the buyers together in an \emph{offline} fashion, by incorporating coverage constraints so that the individual sellers are proportionally represented across all the recommended items. Such approaches can lead to unforeseen biases wherein certain buyers consistently receive low utility recommendations in order to meet the global seller coverage constraints. To remedy this situation, we propose a general formulation that incorporates seller coverage objectives alongside individual buyer objectives in a real-time personalized recommender system. In addition, we leverage highly scalable submodular optimization algorithms to provide recommendations to each buyer with provable theoretical quality bounds. Furthermore, we empirically evaluate the efficacy of our approach using data from an online real-estate marketplace.