Study identifies risk factors for subsequent suicide attempts in youth.
problem Uncertainty in suicide attempt identification from medical claims data.
method Integrative Cox cure model with regularization for survival analysis with uncertain events.
result Identifies risk factors for subsequent suicide attempts and distinguishes susceptibility from timing.
New analysis shows rational actors will deploy AGI despite negative social value due to catastrophic risk.
problem Rational actors will deploy AGI despite negative social value due to shared catastrophic risk.
method Continuous-time preemption game with shared catastrophic externalities, showing suicide region and welfare distortion.
result The suicide region widens as catastrophic risk grows, and two mechanisms can close it.
Study identifies key health behaviors linked to adolescent suicide attempts.
problem Understanding health behaviors associated with increased risk of adolescent suicide attempts.
method Cross-sectional data analysis using machine learning algorithms and logistic regression.
result Non-parametric Bayesian tree ensemble model outperforms other models, achieving 80.0% accuracy in goodness-of-fit and 78.2% in predictive accuracy.
New method uses surrogate outcomes and single-record data to improve suicide risk modeling.
problem Lack of historical information in single-record patients hinders modeling rare medical events.
method Hybrid framework combining supervised and unsupervised learning to integrate concurrent and single-record data.
result Single-record data and concurrent diagnoses provide valuable information for improving suicide risk modeling.
Neural SDEs model suicide risk with compact state space constraints.
problem Modeling suicide risk with irregular, noisy, and partially observed data.
method Developed neural SDEs confined to compact state spaces, addressing domain constraints and numerical stability.
result Improved forecasts and optimization dynamics over standard models on EMA datasets.
Accurate prediction of suicide risk in mental health patients remains an open problem. Existing methods including clinician judgments have acceptable sensitivity, but yield many false positives. Exploiting administrative data has a great potential, but the data has high dimensionality and redundancies in the recording …
Though suicide is a major public health problem in the US, machine learning methods are not commonly used to predict an individual's risk of attempting/committing suicide. In the present work, starting with an anonymized collection of electronic health records for 522,056 unique, California-resident adolescents, we dev…
Trans-GLMC tackles source heterogeneity in transfer learning for structured clusters.
problem Source heterogeneity makes it hard to use multiple related auxiliary sources effectively.
method Trans-GLMC constructs clusters of sources, then combines global fusion, within-cluster refinement, and target debiasing.
result Improves facility-specific prediction and identifies interpretable communities of hospitals with mutual transferability.
Study uses DNA methylation data to predict suicidal and non-suicidal deaths.
problem Predicting suicidal and non-suicidal deaths from DNA methylation data.
method Support Vector Machines (SVM) with dimensionality reduction using PCA and t-SNE.
result t-SNE outperforms PCA in reducing data dimensionality.
Study finds fMRI can predict suicidal ideation, but analysis questions results.
problem Predicting suicidal ideation using fMRI data.
method Naive Bayes classifier trained on fMRI responses to words related to mortality.
result Classification accuracy of 91% for predicting suicidal ideation, but analysis calls into question the accuracy of the findings.
Approach for modeling EHR data with rare features, improving prediction and interpretation.
problem Challenges in modeling rare binary features in EHR data.
method Tree-guided feature selection and logic aggregation for large-scale regression.
result Improved prediction and model interpretation of suicide risk in EHR data.
Improved forecasting of suicide attempts using LSGPs for patients with little data.
problem Challenges in predicting suicide attempts due to their rarity and patient heterogeneity.
method Introduced Latent Similarity Gaussian Processes (LSGPs) to capture patient heterogeneity.
result LSGPs outperform baseline models, even without kernel-design, and offer new insights into patient similarity.
This article presents a novel method for predicting suicidal ideation from Electronic Health Records (EHR) and Ecological Momentary Assessment (EMA) data using deep sequential models. Both EHR longitudinal data and EMA question forms are defined by asynchronous, variable length, randomly-sampled data sequences. In our …
This paper proposes a novel profile likelihood method for estimating the covariance parameters in exploratory factor analysis of high-dimensional Gaussian datasets with fewer observations than number of variables. An implicitly restarted Lanczos algorithm and a limited-memory quasi-Newton method are implemented to deve…
Equity-Directed Bootstrapping improves model performance across groups in imbalanced datasets.
problem Improving model performance across different groups in imbalanced datasets.
method Equity-Directed Bootstrapping to balance training data with respect to both labels and group identity.
result The equity-directed bootstrap brings test set sensitivities and specificities closer to satisfying the equal odds criterion.
Detecting depression early from social media texts.
problem Early diagnosis and prevention of depression.
method Topic analysis and learned confidence scores.
result Achieved good results compared to state of the art.
Bayesian model predicts mental health symptoms from IAT data, improving accuracy over D-score.
problem Limited predictive performance of D-score method for mental health assessment.
method Sparse hierarchical Bayesian model leveraging multi-modal data.
result AUCs of 0.73 (E-IAT) and 0.76 (PSY-IAT) in best modality configurations, significant after FDR correction.
A framework predicts employment status for students considering unconscious biases.
problem Unconscious biases hinder college students' job hunting.
method Developed a framework MAYA using GAN, LSTM, and bias regularization.
result Framework effectively predicts employment status with bias consideration.
Extends multivariate regression for tensor-variate data, identifying brain regions and facial characteristics.
problem Challenges in fitting regression models with multivariate responses and covariates.
method Low-rank tensor formats on regression coefficients and tensor-variate normal distribution for errors.
result Maximum likelihood estimators for tensor-on-tensor regression via block-relaxation algorithms.
Depression is one of the most common mental health disorders, and a large number of depressed people commit suicide each year. Potential depression sufferers usually do not consult psychological doctors because they feel ashamed or are unaware of any depression, which may result in severe delay of diagnosis and treatme…
PerSense assesses personality traits from text for commonsense reasoning.
problem Estimating human personality traits from text for mental health analysis.
method Aggregated Probability Density Functions (PDF) and Machine Learning (ML) models.
result PerSense algorithms achieve comparable results to ground truth data, with high accuracy for personality assessment and commonsense prediction.
Cost-effective models detect depression from speech.
problem Detecting depression from speech with limited resources.
method Comparison of conventional and deep acoustic features for depression prediction.
result Conventional acoustic features perform as well as deep representations at lower cost.
Enhanced framework selects features for unbiased causal inference.
problem Unbiased estimation of causal quantities in causal inference.
method Three-stage computational framework balancing treatment and non-treatment variables.
result Significantly reduces bias and variance in estimating causal quantities.
Improved security of smart contracts by classifying them into four categories.
problem Detecting and classifying vulnerabilities in smart contracts efficiently.
method Used AWD-LSTM for multi-class classification, addressing class imbalance.
result Achieved a weighted average Fbeta score of 90.0%.
AUC is unreliable in rare event settings but stable with moderate numbers of events.
problem Misleading performance metrics in rare event settings.
method Simulation study varying dataset sizes and event rates.
result AUC is unreliable in rare event settings but stable with moderate numbers of events.
Introduces factor risk measures to assess risk relative to multiple factors.
problem Measuring risk relative to multiple factors.
method Introduces a double-argument mapping as a risk measure to assess risk relative to a vector of factors.
result Characterizes various types of factor risk measures including distortion, quantile, linear, and coherent measures.
Paper characterizes star-shaped risk measures and their properties.
problem Characterizing risk measures in the presence of liquidity risk and competitive delegation.
method Characterization of star-shaped risk measures, study of their properties.
result Star-shaped risk measures include all practically used risk measures.
Develops a new method for risk diversification using dynamic risk measures.
problem Dynamic risk diversification in investment portfolios.
method Introduces dynamic risk contributions and a recursive optimization approach for coherent dynamic distortion risk measures.
result Dynamic risk budgeting strategies can be solved using deep learning.
New risk measure considers horizon risk and interest rate uncertainty.
problem Dynamic risk evaluation considering horizon risk and interest rate uncertainty.
method Introduced a risk measure based on generalized Tsallis entropy.
result New q-entropic risk measure quantifies capital requirement.
Study examines risk premium convergence rates in risk sharing contracts.
problem Analyzing risk premium convergence rates in risk sharing contracts.
method Examines the limiting behavior of risk premium associated with Pareto optimal risk sharing contracts under general law-invariant risk measures.
result Risk premium convergence rate is typically n1/2, not n. Optimal risk sharing found for heterogeneous risk attitudes using distortion risk measures.
problem Risk sharing in economies with diverse risk attitudes.
method Modeling preferences with distortion risk measures, using comonotonic and counter-monotonic principles.
result Optimal risk sharing strategies identified based on risk attitudes, reducing the n-agent problem to a two-agent formulation. This paper extends risk parity to continuous-time, solving risk budgeting problems.
problem Achieving robust risk across different assets in continuous-time.
method Characterizing risk contributions and solving risk budgeting problems using continuous-time terminal variance.
result Risk contributions and risk budgets can be represented as predictable processes in continuous-time.
Approximate Incremental Value-at-Risk formulae provide an easy-to-use preliminary guideline for risk allocation. Both the cases of risk adding and risk pooling are examined and beta-based formulae achieved. Results highlight how much the conditions for adding new risky positions are stronger than those required for ris…
Paper tackles complex risk in deep neural networks.
problem Complex risk in deep neural networks.
method Developed new approach for complex risk statistics.
result Derived dual representation for complex risk.
New set-valued star-shaped risk measures introduced for better risk assessment.
problem Improving risk assessment in financial contexts.
method Developed new set-valued star-shaped risk measures and proved their representation theorems.
result Set-valued star-shaped risk measures can be represented as unions of set-valued convex risk measures.
New risk measures for financial and ESG risks using utility functions.
problem Assessing financial and ESG risks using traditional risk measures.
method Developed new risk measures based on utility functions.
result Properties of utility functions translate into properties of risk measures.
Study risk sharing among agents with varying risk preferences.
problem Risk sharing among agents with heterogeneous risk measures.
method Derive explicit solutions for inf-convolution and counter-monotonic inf-convolution under varying risk seeking.
result Explicit solutions for inf-convolution and counter-monotonic inf-convolution can be represented by a generalization of distortion risk measures.
The article develops a model for skewness risk in risk parity portfolios.
problem Managing skewness risk in asset allocation models.
method Modeling asset returns with skewness and jumps, deriving analytical formulas for risk contributions.
result Skewness-based risk parity portfolios outperform volatility-based portfolios in managing jump risks.
The paper establishes a connection between different risk measures and their risk contributions.
problem Understanding the relationship between conditional coherent and deviation risk measures.
method Axiomatic framework and continuous-time risk contribution analysis.
result Risk contributions of time-consistent risk measures are also time-consistent.
Enhances financial risk quantification in classical models.
problem Risk quantification in classical finance models.
method Nested risk measures, limiting behavior analysis.
result Uniqueness of risk-averse limit in classical models.
CERM calculates climate risks in bank loans.
problem Estimating climate risks in bank credit portfolios.
method Adapts credit risk models to include physical and transition risks.
result Calculates incremental credit losses due to climate risks.
The study reveals unspanned risks in equity option risk premiums, explaining negative premiums for certain options.
problem Explaining negative risk premiums for certain equity option types.
method Developed a decomposition of equity option risk premiums, operationalized the pricing kernel process, and incorporated unspanned risks.
result Empirical evidence supports the presence of unspanned risks, explaining negative risk premiums for certain options.
Paper introduces new risk measures for default risk and model uncertainty.
problem Model uncertainty and default risk in rating systems.
method Introduces default risk measures and discusses their properties and impacts.
result Different default risk measures and margins of conservatism affect risk-weighted assets.
Diversified risk parity strategies outperform equally-weighted portfolios in various asset universes.
problem Finding optimal portfolio allocations that balance risk and reward.
method Integrates various reward-risk measures and generic allocation rules into diversified risk parity.
result Diversified reward-risk parity strategies exhibit higher average returns, Sharpe ratios, and Calmar ratios compared to equally-weighted risk portfolios.
Study risk-sensitive reinforcement learning with Lipschitz dynamic risk measures, establishing regret bounds.
problem Risk-sensitive reinforcement learning in Markov decision processes.
method Two model-based algorithms for Lipschitz dynamic risk measures, focusing on regret bounds.
result Upper bounds demonstrate optimal dependencies on actions and episodes, reflecting risk sensitivity vs. sample complexity trade-off.
A new measure quantifies how risk-averse different risk measures are.
problem Measuring the degree of risk aversion among different risk measures.
method Two axioms: normalization and linearity. Two formulas for the functional.
result Quantifies the degree of risk aversion among spectral risk measures.
Examines optimal risk sharing with realistic risk attitudes, finding risk seeking in certain subdomains.
problem Optimal risk sharing with empirically realistic risk attitudes.
method Allows for risk-seeking agents, generalizes expected utility, and uses counter-monotonic improvement theorem.
result First empirical results on optimal risk sharing with realistic risk attitudes.
The paper models and prices cyber insurance risks, distinguishing idiosyncratic, systematic, and systemic risks.
problem Modeling and pricing cyber insurance policies, especially for systemic risks.
method Distinguishes three types of cyber risks and proposes methods for their valuation.
result Complex methods are needed for systemic cyber risks, including risk-neutral valuation and monetary risk measures.