In recent years, there has been growing interest in leveraging machine learning for homeless service assignment. However, the categorical nature of administrative data recorded for homeless individuals hinders the development of accurate machine learning methods for this task. This work asserts that deriving latent representations of such features, while at the same time leveraging underlying relationships between instances is crucial in algorithmically enhancing the existing assignment decision-making process. Our proposed approach learns temporal and functional relationships between services from historical data, as well as unobserved but relevant relationships between individuals to generate features that significantly improve the prediction of the next service assignment compared to the state-of-the-art.

A burgeoning body of research seeks to develop ever more accurate automated credit evaluation systems. At the same time, reject inference can help financial institutions identify applicants who are mistakenly deemed non–creditworthy, or whose applications are approved even though they end up defaulting. However, both machine learning models and reject inference methods assume perfect decisions, the former for training and the latter for inference. In this work, we challenge this assumption, and explore the feasibility of identifying erroneously rejected (or accepted) loan applications using noisy and counterfactual learning. Experiments on a small benchmark and a large, real– world dataset, demonstrate the effectiveness of our approach.

Homelessness, a long-standing societal problem, appears to be on the rise, fueled in part by the Covid-19 pandemic. Looking at the homelessness system as a network of interconnected services which individuals traverse over time, we seek to shed light on their progression toward securing stable housing. We formalize the concept of stability upon exit and show that regardless of starting conditions, the ultimate goal is either reached quickly or not at all, indicating the importance of addressing the homeless’ needs early on to avoid them “giving up.” To better understand the causes that may contribute to positive outcomes for certain individuals versus others, we computationally analyze their pathways through the network of homeless services. We confirm the intuition that some individuals face more challenges than others based on their initial living conditions and initial placement to homelessness services. At the same time, we discover that simple signals can act as good indicators of individuals at risk of “falling through the cracks.” Being able to predict such outcomes is critical to design assistive technology that can retain individuals who would otherwise falter.

Administrative data collected by homeless service providers offer a unique opportunity to understand how homeless individuals navigate the homeless system towards securing stable housing. However, the literature on predictive models in the context of homeless service provision has neglected the sequential nature of services that an individual receives over time. Our work addresses this gap by learning, from administrative data, a Bayesian network, which in turn can be used to accurately predict whether an individual will exit the system, or alternatively, the service she would be assigned to the next time she experiences homelessness. Experimental evaluation shows that the proposed approach outperforms prior art not only at predicting exit, but also the less frequent services (and thus more challenging to predict).

This study infers the unobserved underlying network of homeless services from administrative data collected by homeless service providers. Both the structure of the inferred network, and historical observations, are used to identify individuals with similar trajectories so that their next assignments can be predicted. Experimental evaluation shows that the proposed approach performs well not only on predicting exit from the system, or simply guessing high frequency services (as most baselines), but is also successful in less frequent scenarios.

This paper models, for the first time, the homelessness system as a network of interconnected services which individuals traverse over time towards securing stable housing, and formalizes the concept of stability upon exit of the system. A computational analysis of individual-level longitudinal homelessness data shows that the ultimate goal is either reached quickly or not at all, regardless of starting conditions, indicating the importance of addressing the homeless' needs early on.