Factorization machines (FMs) are a class of general predictors for sparse data. One major benefit of FMs is their ability to capture the interactions across features when making recommendations. In this paper, we note that the interactions captured by existing FMs generally represent correlations in the data and we argue that such correlations, unless informed by the true causality structures underlying the data, may be spurious and may result in unwanted bias. To tackle this challenge, we propose a Causally-Informed Factorization Machine (CIFM) model that introduces a novel causal injection mechanism. CIFM leverages a priori causal knowledge, described in the form of a causal graph, to boost the representational ability of FMs and achieve better predictions. Specifically, given a (potentially learned) causal graph which describes the causal relationships among features, CIFM distills this structural information into a pairwise causal impact matrix and guides the learning process to ensure that the learned representations capture those relationships that are consistent with the causal relationships. Extensive evaluations of CIFM, along with its integrations with NeuralFM and DeepFM, conducted with synthetic and real-world data sets, demonstrate the effectiveness of causal injection in generating better recommendations.

Tensor Train (TT) is a tensor decomposition technique designed to resolve the curse of dimensionality and the intermediate memory blow-up problems in traditional techniques for high-dimensional data analysis. Tensor train process provides linear space complexity by creating a sequential tensor network of low modalities. However, the selected sequence of decomposition order can have a significant impact on the accuracy and representativeness of the final decomposition and, unfortunately, choosing a good order for the TT representation is not a trivial task. In this paper, we observe that the causal structure underlying the data can impact the tensor train process and that a rough estimate of causality can be used to inform the order of the latent spaces to consider. Enlightened by this observation, we propose a novel causally informed tensor train decomposition (CTT) approach to tackle the sequence selection problem in TT-decomposition. CTT leverages the structural information in a given causal graph and recommends a suitable causally-informed decomposition sequence for TT-decomposition.

With many applications relying on multi-dimensional datasets for decision making, matrix factorization (or decomposition) is becoming the basis for many knowledge discoveries and machine learning tasks, from clustering, trend detection, anomaly detection, to correlation analysis. Unfortunately, a major shortcoming of matrix analysis operations is that, despite their effectiveness when the data is scalar, these operations become difficult to apply in the presence of non-scalar data, as they are not designed for data that include non-scalar observations, such as intervals. Yet, in many applications, the available data are inherently non-scalar for various reasons, including imprecision in data collection, conflicts in aggregated data, data summarization, or privacy issues, where one is provided with a reduced, clustered, or intentionally noisy and obfuscated version of the data to hide information. In this paper, we propose matrix decomposition techniques that consider the existence of interval-valued data. We show that naive ways to deal with such imperfect data may introduce errors in analysis and present factorization techniques that are especially effective when the amount of imprecise information is large.

With many applications relying on multi-dimensional datasets for decision making, matrix factorization (or decomposition) is becoming the basis for many knowledge discovery and machine learning tasks, from clustering, trend detection, anomaly detection, to correlation analysis. Unfortunately, a major shortcoming of matrix analysis operations is that, despite their effectiveness when the data is scalar, these operations become difficult to apply in the presence of non-scalar data, as they are not designed for data that include non-scalar observations, such as intervals. In this paper, we propose matrix decomposition techniques that consider the existence of interval-valued data. We show that naive ways to deal with such imperfect data may introduce errors in analysis and present factorization techniques that are especially effective when the amount of imprecise information is large.

Data- and model-driven computer simulations are increasingly critical in many application domains. Yet, several critical data challenges remain in obtaining and leveraging simulations in decision making. Simulations may track 100s of parameters, spanning multiple layers and spatial-temporal frames, affected by complex inter-dependent dynamic processes. Moreover, due to the large numbers of unknowns, decision makers usually need to generate ensembles of stochastic realizations, requiring 10s-1000s of individual simulation instances. The situation on the ground evolves unpredictably, requiring continuously adaptive simulation ensembles. We introduce the DataStorm framework for simulation ensemble management, and demonstrate its DataStorm-FE data- and decision-flow and coordination engine for creating and maintaining coupled, multi-model simulation ensembles. DataStorm-FE enables end-to-end ensemble planning and optimization, including parameter-space sampling, output aggregation and alignment, and state and provenance data management, to improve the overall simulation process. It also aims to work efficiently, producing results while working within a limited simulation budget, and incorporates a multivariate, spatiotemporal data browser to empower decision-making based on these improved results.

Measures of node ranking, such as personalized PageRank, are utilized in many web and social-network based prediction and recommendation applications. Despite their effectiveness when the underlying graph is certain, however, these measures become difficult to apply in the presence of uncertainties, as they are not designed for graphs that include uncertain information, such as edges that mutually exclude each other. While there are several ways to naively extend existing techniques (such as trying to encode uncertainties as edge weights or computing all possible scenarios), as we discuss in this paper, these either lead to large degrees of errors or are very expensive to compute, as the number of possible worlds can grow exponentially with the amount of uncertainty. To tackle with this challenge, in this paper, we propose an efficient Uncertain Personalized PageRank (UPPR) algorithm to approximately compute personalized PageRank values on an uncertain graph with edge uncertainties. UPPR avoids enumeration of all possible worlds, yet it is able to achieve comparable accuracy by carefully encoding edge uncertainties in a data structure that leads to fast approximations. Experimental results show that UPPR is very efficient in terms of execution time and its accuracy is comparable or better than more costly alternatives.