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Many decision-making scenarios, e.g., public policy, healthcare, business, and disaster response, require accommodating the preferences of multiple stakeholders. We offer the first formal treatment of reasoning with multi-stakeholder qualitative preferences in a setting where stakeholders express their preferences in a qualitative preference language, e.g., CP-net, CI-net, TCP-net, CP-Theory. We introduce a query language for expressing queries against such preferences over sets of outcomes that satisfy specified criteria, e.g., ψ1PAψ2 (read loosely as the set of outcomes satisfying ψ1 that are preferred over outcomes satisfying ψ2 by a set of stakeholders A). Motivated by practical application scenarios, we introduce and analyze several alternative semantics for such queries, and examine their interrelationships. We provide a provably correct algorithm for answering multi-stakeholder qualitative preference queries using model checking in alternation-free μ-calculus. We present experimental results that demonstrate the feasibility of our approach. 

As an alternative to resource-intensive deep learning approaches to the continual learning problem, we propose a simple, fast algorithm inspired by adaptive resonance theory (ART). To cope with the curse of dimensionality and avoid catastrophic forgetting, we apply incremental principal component analysis (IPCA) to the model's previously learned weights. Experiments show that this approach approximates the performance achieved using static PCA and is competitive with continual deep learning methods. Our implementation is available on https://github.com/neil-ash/ART-IPCA 

In many real-world applications, e.g., monitoring of individual health, climate, brain activity, environmental exposures, among others, the data of interest change smoothly over a continuum, e.g., time, yielding multi-dimensional functional data. Solving clustering, classification, and regression problems with functional data calls for effective methods for learning compact representations of functional data. Existing methods for representation learning from functional data, e.g., functional principal component analysis, are generally limited to learning linear mappings from the data space to the representation space. However, in many applications, such linear methods do not suffice. Hence, we study the novel problem of learning non-linear representations of functional data. Specifically, we propose functional autoencoders, which generalize neural network autoencoders so as to learn non-linear representations of functional data. We derive from first principles, a functional gradient based algorithm for training functional autoencoders. We present results of experiments which demonstrate that the functional autoencoders outperform the state-of-the-art baseline methods. 

This study explored how population mobility flows form commuting networks across US counties and influence the spread of COVID-19. We utilized 3-level mixed effects negative binomial regression models to estimate the impact of network COVID-19 exposure on county confirmed cases and deaths over time. We also conducted weighting-based analyses to estimate the causal effect of network exposure. Results showed that commuting networks matter for COVID-19 deaths and cases, net of spatial proximity, socioeconomic, and demographic factors. Different local racial and ethnic concentrations are also associated with unequal outcomes. These findings suggest that commuting is an important causal mechanism in the spread of COVID-19 and highlight the significance of interconnected of communities. The results suggest that local level mitigation and prevention efforts are more effective when complemented by similar efforts in the network of connected places. Implications for research on inequality in health and flexible work arrangements are discussed. 

Gaussian processes offer an attractive framework for predictive modeling from longitudinal data, i.e., irregularly sampled, sparse observations from a set of individuals over time. However, such methods have two key shortcomings: (i) They rely on ad hoc heuristics or expensive trial and error to choose the effective kernels, and (ii) They fail to handle multilevel correlation structure in the data. We introduce Longitudinal deep kernel Gaussian process regression (L-DKGPR) to overcome these limitations by fully automating the discovery of complex multilevel correlation structure from longitudinal data. Specifically, L-DKGPR eliminates the need for ad hoc heuristics or trial and error using a novel adaptation of deep kernel learning that combines the expressive power of deep neural networks with the flexibility of non-parametric kernel methods. L-DKGPR effectively learns the multilevel correlation with a novel additive kernel that simultaneously accommodates both time-varying and the time-invariant effects. We derive an efficient algorithm to train L-DKGPR using latent space inducing points and variational inference. Results of extensive experiments on several benchmark data sets demonstrate that L-DKGPR significantly outperforms the state-of-the-art longitudinal data analysis (LDA) methods.