Task adapted reconstruction for inverse problems

The paper considers the problem of performing a task defined on a model parameter that is only observed indirectly through noisy data in an ill-posed inverse problem. A key aspect is to formalize the steps of reconstruction and task as appropriate estimators (non-randomized decision rules) in statistical estimation problems. The implementation makes use of (deep) neural networks to provide a differentiable parametrization of the family of estimators for both steps. These networks are combined and jointly trained against suitable supervised training data in order to minimize a joint differentiable loss function, resulting in an end-to-end task adapted reconstruction method. The suggested framework is generic, yet adaptable, with a plug-and-play structure for adjusting both the inverse problem and the task at hand. More precisely, the data model (forward operator and statistical model of the noise) associated with the inverse problem is exchangeable, e.g., by using neural network architecture given by a learned iterative method. Furthermore, any task that is encodable as a trainable neural network can be used. The approach is demonstrated on joint tomographic image reconstruction, classification and joint tomographic image reconstruction segmentation

The impact of alcohol and drug use on employment: A labor market study using the National Longitudinal Survey of Youth

The purpose of this study was, first, to estimate of the impact of alcohol and drug use on the employment status of men and women, and second, to examine whether a history of past use, as opposed to current use, adversely affects the propensity to be employed. Using data from the National Longitudinal Survey of Youth we conducted a cross-sectional and a longitudinal analysis with logistic regression estimation to model the probability that a person was employed in 1992. In addition to usual regressors, interactions between substance use measures, between substance use measures and human capital variables, and between substance use measures and race dummies were included in the equation. The longitudinal analysis utilized a conditional likelihood method based on employment data in 1992 and 1988 and included the difference between 1992 regressors and their 1988 counterparts. A comparison was made between the prediction accuracy of the logit choice model, linear discriminant analysis, k-nearest neighbor analysis, and three modern classification methods that are used extensively in the area of machine learning. Results showed that the logit model performs relatively well in classifying individuals into employed and unemployed categories based on individual attributes. Results of the cross-sectional and longitudinal analysis were mixed, but not inconsistent with our prior expectations that use of alcohol or drug has a negative impact on a person's propensity to be employed. Cross-sectional results show a clear negative impact of past substance use on a person's employment probability among all demographic groups examined (by gender: all persons, blacks, Hispanics, families with income below the poverty line, and high users of alcohol or drugs). However, when current and past use are considered together, only women seem to experience negative impacts. The results of the longitudinal analysis are less clear, although they do indicate that negative impacts are associated with the interaction between substance use measures and human capital variables. Limitations of the study are pointed out and suggestions are made for future research.

Preterm Birth Prediction: Deriving Stable and Interpretable Rules from High Dimensional Data

Preterm births occur at an alarming rate of 10-15%. Preemies have a higher risk of infant mortality, developmental retardation and long-term disabilities. Predicting preterm birth is difficult, even for the most experienced clinicians. The most well-designed clinical study thus far reaches a modest sensitivity of 18.2-24.2% at specificity of 28.6-33.3%. We take a different approach by exploiting databases of normal hospital operations. We aims are twofold: (i) to derive an easy-to-use, interpretable prediction rule with quantified uncertainties, and (ii) to construct accurate classifiers for preterm birth prediction. Our approach is to automatically generate and select from hundreds (if not thousands) of possible predictors using stability-aware techniques. Derived from a large database of 15,814 women, our simplified prediction rule with only 10 items has sensitivity of 62.3% at specificity of 81.5%.Comment: Presented at 2016 Machine Learning and Healthcare Conference (MLHC 2016), Los Angeles, C

Hybrid model using logit and nonparametric methods for predicting micro-entity failure

Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods (Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic variables complement financial ratios for bankruptcy prediction