Research
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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.

169,051 papers · 148 categories

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10 results for PPV

KANEL combines models for early hit enrichment in virtual screening.

problem Assessing model accuracy in chemical bioactivity predictions.
method Ensemble workflow using Kolmogorov-Arnold Networks (KANs) and other models.
result Improves early hit enrichment metrics like PPV@N.

The study explores fairness in classifier post-processing methods.

problem Achieving fairness in binary decision-making classifiers with imperfect information.
method Examines fairness properties of post-processing calibrated scores and deferring decisions.
result Deferring decisions can help achieve fairness in PPV, NPV, FPR, and FNR across protected groups.

Study membership inference under skewed priors and adaptive thresholds, improving attack accuracy.

problem Membership inference in imbalanced settings with selective thresholding.
method Developed PPV metric for skewed priors, threshold selection procedure, and a new inference attack.
result Improved inference attack accuracy in imbalanced settings.

Support vector machines predict cryptocurrency price movements with high accuracy.

problem Predicting short-term price movements in cryptocurrencies.
method Developed technical indicators, tested various classification methods, including SVM.
result Support vector machines yield the most profitable trading strategies.

Objective: We investigate whether deep learning techniques for natural language processing (NLP) can be used efficiently for patient phenotyping. Patient phenotyping is a classification task for determining whether a patient has a medical condition, and is a crucial part of secondary analysis of healthcare data. We ass…

2017-03-25abs ↗pdf ↗

Deep learning predicts diabetic macular edema from fundus photos.

problem Diabetic macular edema diagnosis from fundus photos is inaccurate.
method Trained deep learning model on color fundus photographs.
result Deep learning model has higher sensitivity and PPV than human specialists.

Method predicts ODX scores for breast cancer patients based on clinical data.

problem Predicting ODX scores for breast cancer patients to aid decision-making.
method Distributional random forest approach using 9 clinico-pathological characteristics.
result Correctly predicted 92% of low risk and 40.2% of high risk patients.