A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
PerSense assesses personality traits from text for commonsense reasoning.
Study assesses whether RL algorithm personalizes treatment sequences.
IPGP framework improves psychological assessment by integrating shared and unique traits.
Improved assessment of knee osteoarthritis using geodesic B-score.
Model uses smartphone data to assess MS trajectories.
AI models assess psychological risks in currency trading.
Deep learning methods for person identification based on electroencephalographic (EEG) brain activity encounters the problem of exploiting the temporally correlated structures or recording session specific variability within EEG. Furthermore, recent methods have mostly trained and evaluated based on single session EEG …
The increasing impact of algorithmic decisions on people's lives compels us to scrutinize their fairness and, in particular, the disparate impacts that ostensibly-color-blind algorithms can have on different groups. Examples include credit decisioning, hiring, advertising, criminal justice, personalized medicine, and t…
Proposes auditing for envy-freeness in recommender systems to assess individual preferences.
Develops algorithms to balance personalization and statistical power in mobile health studies.
LLMs can simulate human investment attitudes based on personality traits.
ML system reduces overdraft fees for Mint users.
Bayesian Supervised Causal Clustering identifies patient subgroups for personalized decision-making.
The study reduces a personality measurement instrument to 10 features with minimal loss of accuracy.
The Black-Litterman model combines investors' personal views with historical data and gives optimal portfolio weights. In this paper we will introduce the original Black-Litterman model (section 1), we will modify the model such that it fits in a Bayesian framework by considering the investors' personal views to be a d…
Causal ML predicts treatment outcomes, aiding personalized medicine.
RLMM extends psychometric models to larger tasks.
Radio-frequency dosimetry is an important process in human safety and for compliance of related products. Recently, computational human models generated from medical images have often been used for such assessment, especially to consider the inter-variability of subjects. However, the common procedure to develop person…
The prevalence of wearable sensors (e.g., smart wristband) is creating unprecedented opportunities to not only inform health and wellness states of individuals, but also assess and infer personal attributes, including demographic and personality attributes. However, the data captured from wearables, such as heart rate …
We study the problem of learning conditional average treatment effects (CATE) from observational data with unobserved confounders. The CATE function maps baseline covariates to individual causal effect predictions and is key for personalized assessments. Recent work has focused on how to learn CATE under unconfoundedne…
Recent studies have shown that information disclosed on social network sites (such as Facebook) can be used to predict personal characteristics with surprisingly high accuracy. In this paper we examine a method to give online users transparency into why certain inferences are made about them by statistical models, and …
Retrospective and prospective analysis of Diebold-Yilmaz connectedness research.
AI platforms disrupt investment by personalizing deal sourcing and insights.
AI enhances personalized drug development and decision-making in pharma.
The prediction of workers' safety behaviour can help identify vulnerable workers who intend to undertake unsafe behaviours and be useful in the design of management practices to minimise the occurrence of accidents. The latest literature has evidenced that there is within-population diversity that leads people's intend…
Mobile technologies offer opportunities for higher resolution monitoring of health conditions. This opportunity seems of particular promise in psychiatry where diagnoses often rely on retrospective and subjective recall of mood states. However, getting actionable information from these rather complex time series is cha…
Personalized interventions in social services, education, and healthcare leverage individual-level causal effect predictions in order to give the best treatment to each individual or to prioritize program interventions for the individuals most likely to benefit. While the sensitivity of these domains compels us to eval…
Adversarial transfer learning improves stress assessment across users.
Infinite hierarchical contrastive clustering identifies personal environments linked to health outcomes.
PhysioMTL learns personalized HRV rhythms from wearable data, improving prediction and counterfactual analysis.
Causal ML methods failed to validate their personalized treatment effects in two large trials.
We develop a personalized real time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs. Heterogeneity of the patients population is captured via a hierarchical latent class model. The proposed algorithm aims t…
New deep learning model generates accurate personalized human head models for electromagnetic dosimetry.
Managing patients with chronic diseases is a major and growing healthcare challenge in several countries. A chronic condition, such as diabetes, is an illness that lasts a long time and does not go away, and often leads to the patient's health gradually getting worse. While recent works involve raw electronic health re…
With the industry trend of shifting from a traditional hierarchical approach to flatter management structure, crowdsourced performance assessment gained mainstream popularity. One fundamental challenge of crowdsourced performance assessment is the risks that personal interest can introduce distortions of facts, especia…
DNA sequencing to identify genetic variants is becoming increasingly valuable in clinical settings. Assessment of variants in such sequencing data is commonly implemented through Bayesian heuristic algorithms. Machine learning has shown great promise in improving on these variant calls, but the input for these is still…
Developmental Dyslexia (DD) is a learning disability related to the acquisition of reading skills that affects about 5% of the population. DD can have an enormous impact on the intellectual and personal development of affected children, so early detection is key to implementing preventive strategies for teaching langua…
The paper introduces ESE scores for farmers to assess climate change risks.
We study the problem of learning personalized decision policies from observational data while accounting for possible unobserved confounding. Previous approaches, which assume unconfoundedness, i.e., that no unobserved confounders affect both the treatment assignment as well as outcome, can lead to policies that introd…
Multimodal analysis assesses job interview performance and provides feedback.
PO-Flow models potential and counterfactual outcomes for personalized treatment decisions.
Paper proposes a new approach to GDPR compliance using data protection analytics.
New algorithm assesses credit risk in multilayer networks over time.
Model predicts cannabis use disorder risk for adolescents and young adults.
Voice-enabled interactions provide more human-like experiences in many popular IoT systems. Cloud-based speech analysis services extract useful information from voice input using speech recognition techniques. The voice signal is a rich resource that discloses several possible states of a speaker, such as emotional sta…
Though used extensively, the concept and process of machine learning (ML) personalization have generally received little attention from academics, practitioners, and the general public. We describe the ML approach as relying on the metaphor of the person as a feature vector and contrast this with humanistic views of th…
Bipolar disorder (BPD) is a chronic mental illness characterized by extreme mood and energy changes from mania to depression. These changes drive behaviors that often lead to devastating personal or social consequences. BPD is managed clinically with regular interactions with care providers, who assess mood, energy lev…
Paper uses TDA for automated Parkinson's disease classification and severity assessment.