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arXiv research

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.

168,982 papers · 148 categories

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8 results for “counseling”

Conversation Model Fine-Tuning for Classifying Client Utterances in Counseling Dialoguescs.CL

ConvMFiT classifies client utterances in counseling dialogues.

problem Classifying client utterances for better counseling outcomes.

method Fine-tuning a pre-trained conversation model on counselor-client dialogues.

result ConvMFiT outperforms state-of-the-art models in classification.

counseling client utterances classification neural networks attention weights

2019-03-31abs ↗pdf ↗

Learning the progression and clinical subtypes of Alzheimer's disease from longitudinal clinical datacs.LG

Alzheimer's disease (AD) is a degenerative brain disease impairing a person's ability to perform day to day activities. The clinical manifestations of Alzheimer's disease are characterized by heterogeneity in age, disease span, progression rate, impairment of memory and cognitive abilities. Due to these variabilities, …

2018-12-03abs ↗pdf ↗

What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatmentstat.ME

Sharp bounds on negative impact identified from observational data.

problem Identifying the fraction of users negatively affected by a treatment.

method Developed robust inference algorithm to derive tightest-possible bounds on negative impact.

result Valid conservative bounds on the fraction negatively affected, even when functions are mislearned.

causal inference sharp bounds observational data robust inference career counseling

2022-05-20abs ↗pdf ↗

More Efficient Policy Learning via Optimal Retargetingstat.ML

Retargeting improves policy learning from observational data.

problem Lack of overlap in data for different actions leads to poor policy performance.

method Optimal retargeting to induce little to no bias at the population level.

result Retargeting significantly improves policy learning procedures applied to observational data.

policy learning retargeting observational data bias reduction asymptotic efficient estimation

2019-06-20abs ↗pdf ↗

Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessingstat.ML

The paper proposes a fair reinforcement learning framework to prevent healthcare disparities.

problem Unfair reinforcement learning policies in healthcare can lead to socioeconomically-disadvantaged subgroups being underprivileged.

method The paper introduces a counterfactual fairness framework and a sequential data preprocessing algorithm to achieve fair sequential decision making.

result The proposed approach greatly enhances fair access to counseling in a digital health dataset designed to reduce opioid misuse.

reinforcement learning fairness healthcare counterfactual sequential decision making

2025-01-10abs ↗pdf ↗

Transient-evoked otoacoustic emission signals predicting outcomes of acute sensorineural hearing loss in patients with Meniere's Diseaseeess.SP

Study predicts hearing recovery in MD patients using TEOAE signals.

problem Predicting hearing recovery in MD patients during acute episodes.

method Applied machine learning to TEOAE signals from MD patients, using SVM for classification.

result Baseline TEOAE parameters can predict hearing recovery in MD patients.

Meniere's Disease Transient-Evoked Otoacoustic Emission Machine Learning Hearing Recovery Support Vector Machine

2019-05-30abs ↗pdf ↗

Treatment Effect Risk: Bounds and Inferencestat.ME

The paper assesses the risk of negative treatment effects using bounds and inference.

problem Risk of negative treatment effects on a significant portion of the population.

method Characterizes tight bounds on the conditional value at risk (CVaR) of the individual treatment effect (ITE) distribution using covariate-conditional average treatment effect (CATE) function.

result Developed a debiasing method to estimate these bounds efficiently from data and construct confidence intervals, even in complex scenarios.

Treatment Effect Risk Assessment Conditional Value at Risk CATE Function Debiasing Method

2022-01-15abs ↗pdf ↗

Automatic Detection and Classification of Cognitive Distortions in Mental Health Textcs.HC

Machine learning detects and classifies cognitive distortions in mental health texts.

problem Detect and classify cognitive distortions in mental health texts to improve treatment.

method Machine learning framework using crowdsourced and therapy dataset.

result Model achieved high F1 scores for detecting and classifying cognitive distortions.

cognitive distortions mental health machine learning text analysis anxiety

2019-09-16abs ↗pdf ↗