Modeling short selling risks to quantify losses.
problem Short selling constraints and associated risks.
method Optimal stopping model with margin risk and recall risk.
result Realistic short selling constraints lead to significant value loss.
On-device research index
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.
169,042 papers · 148 categories
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48 results for “recall risk”
Paper proposes a human-algorithm approach to reduce medical device recall risk and workload.
problem High recall rate and regulatory workload in FDA's 510(k) pathway.
method Developed machine learning models to estimate recall risk and proposed a data-driven clearance policy.
result Conservative evaluation of policy shows a 32.9% improvement in recall rate and 40.5% reduction in workload.
Transformers recall from long distributions with statistical guarantees.
problem Designing Transformers that can recall from arbitrarily long, distributional contexts.
method Recast associative memory as probability measures, decomposing the task into recall and prediction.
result A shallow measure-theoretic Transformer learns the recall-and-predict map under spectral assumptions.
Optimal model improves AUC, recall, and F1 score for class-imbalanced business risk.
problem Improving prediction of class-imbalanced business risk.
method Resampling, regularization, and model ensembling techniques.
result Boosting on DT with SMOTE oversampling achieves AUC, recall, and F1 score of 0.8633, 0.9260, and 0.8907, respectively.
Machine learning detects NASH patients from medical claims data.
problem Detecting undiagnosed NASH patients for screening and management.
method Gradient-boosted decision trees trained on administrative medical claims data.
result Model precision for NASH detection is significantly higher than NASH incidence.
Study predicts high school dropout risk in Louisiana using imbalanced learning techniques.
problem Predicting high school dropout risk in Louisiana.
method Applied imbalanced learning techniques including resampling, case weighting, and cost-sensitive learning.
result Imbalanced learning techniques improve recall but decrease precision.
AI models predict loan rejection and default risk, reducing default risk by 70%.
problem Predicting loan rejection and default risk to reduce default risk.
method Applied Logistic Regression, Support Vector Machine, and Deep Neural Networks to lending data.
result Deep Neural Networks achieved best performance for default prediction, reducing default risk by 70%.
New framework learns from partial feedback in multi-label tasks.
problem Learning from one-sided feedback in multi-label tasks.
method Probably Approximately Correct (PAC) framework for set functions.
result Achieves optimal sample complexity in realizable case, multiplicative approximation guarantees in agnostic case.
Study develops and improves risk models using machine learning methods.
problem Classifying business delinquency using machine learning.
method Exploring several machine learning methods including regularization, hyper-parameter optimization, and model ensembling.
result Bagging on KNN with K=9 is the optimal model for risk classification.
We study combinations of risk measures under no restrictive assumption on the set of alternatives. We develop and discuss results regarding the preservation of properties and acceptance sets for the combinations of risk measures. One of the main results is the representation of resulting risk measures from the properti…
New framework for optimizing machine learning risks.
problem Optimizing non-decomposable machine learning objectives.
method Empirical X-risk minimization (EXM) framework with algorithmic techniques.
result Developed algorithms for solving EXM with smooth non-convex objectives.
Method minimizes total cost of classification by acquiring covariates efficiently.
problem Minimizing total cost of classification in applications with covariate acquisition costs.
method Formalizes optimization goal using Bayes risk, introduces assumptions for computable solution.
result Proposed method achieves lowest total costs compared to previous methods on medical datasets.
The Acoustic Camouflage Phenomenon: Re-evaluating Speech Features for Financial Risk Predictioncs.SD
Study shows integrating acoustic features in financial forecasting models can degrade performance.
problem Predicting stock market volatility from corporate earnings calls using speech features.
method Empirical investigation of acoustic feature extraction in teleconference environments using a two-stream late-fusion architecture.
result Integrating acoustic features via late fusion significantly degraded performance, reducing recall to 47.08%.
AI random forest model improves credit risk scoring for Azerbaijani SMEs.
problem Improving accuracy in identifying defaulters for SME loans.
method Used Python to compare a Delphi model with a random forest model, measuring accuracy, precision, recall, and F-1 scores.
result Significant improvements in model performance (e.g., from 0.69 to 0.83 in accuracy).
Study optimizes classifiers for credit card mail campaigns and default prediction.
problem Optimizing classifiers for credit card mail campaigns and default prediction.
method Three distinct models: response, risk, and response-risk. Optimized various performance metrics.
result Random Forest classifier achieves highest accuracy (83.2%) in multi-class response-risk model.
Surveying risk measures for handling uncertainty in various fields.
problem Handling uncertainty in engineering and data-driven problems.
method Review of risk measures and their applications.
result Rapid development and widespread use of risk measures.
New algorithm reduces online learning regret for bounded recall games.
problem Reducing regret in online learning with limited past information.
method Constructing a stationary bounded-recall algorithm with regret.
result Any low regret bounded-recall algorithm must be aware of past losses' order.
This paper introduces a new method to train normalizing flows using precision-recall divergences.
problem Training generative models with mode dropping and low-quality samples.
method Introduces PR-divergences and proposes a novel generative model to minimize precision-recall trade-offs.
result Normalizing flows can be trained to achieve specific precision-recall trade-offs using PR-divergences.
Improved algorithm reduces excess risk in selective learning.
problem Selective learning with windowed model selection.
method Hybrid Exponential Weights Algorithm and bounded-recall ERM.
result Achieves expected excess risk of O((log log |L| + log log n) / log n).
This paper aims to explore models based on the extreme gradient boosting (XGBoost) approach for business risk classification. Feature selection (FS) algorithms and hyper-parameter optimizations are simultaneously considered during model training. The five most commonly used FS methods including weight by Gini, weight b…
Model predicts wound and episode-level readmission risk and time to re-admit.
problem Identify patients at high risk of re-admission to prevent wound recurrences and reduce healthcare costs.
method Data-driven analysis of wound care and episode-level patient data.
result Model achieves high recall and precision for predicting re-admission risk and time.
The study examines how class imbalance affects precision-recall curves.
problem Understanding how precision changes with class imbalance ratios.
method Analyzes the relationship between precision, class imbalance ratio, and true/false positive rates.
result Predicts changes in precision-recall curves and other measures with class imbalance ratios.
Revises precision-recall curves for generative models.
problem Improves evaluation of generative models by distinguishing mode-collapse and quality issues.
method Generalizes PR curve formulation to arbitrary measures, exposes a bridge to error rates, proposes a new algorithm to approximate precision-recall curves.
result Demonstrates the interest of the new formulation over the original approach on multi-modal datasets.
Validation of a recommender system for prompting omitted foods in online dietary assessment surveyscs.CY
Recommender system improves recall of omitted foods in online dietary surveys.
problem Improving accuracy of online dietary assessment surveys through recall assistance.
method Developed a recommender algorithm to remind respondents of omitted foods based on past survey data.
result The recommender system captures more omitted foods than hand-coded prompts, but with lower precision.
Bayesian framework improves financial risk management and compliance.
problem Uncertainty in financial risk forecasting and compliance.
method Integrated Bayesian analytics framework for precise uncertainty quantification.
result Proposed DLM model produces more accurate VaR estimates compared to baseline models.
The study formalizes temporal precision and recall for anomaly detection in sequences.
problem Insufficient understanding of precision and recall in sequential anomaly detection.
method Formalized temporal precision and recall measures, developed time-tolerant confusion matrices, and demonstrated statistical significance.
result Precision and recall may overestimate performance with temporal tolerance.
Unified and extended precision-recall metrics for generative models.
problem Evaluation of generative models using scalar metrics like FID and IS.
method Unified various precision-recall approaches under a common framework.
result Recovered entire precision-recall curves and exposed metric pitfalls.
For information retrieval and binary classification, we show that precision at the top (or precision at k) and recall at the top (or recall at k) are maximised by thresholding the posterior probability of the positive class. This finding is a consequence of a result on constrained minimisation of the cost-sensitive exp…
Transformers can store facts efficiently using associative memories.
problem Understanding how transformers store and recall factual information.
method Proved linear scaling of storage capacities for linear and MLP associative memories, introduced a synthetic task, and analyzed gradient flow.
result Shallow transformers can achieve near optimal storage capacity for factual recall tasks using associative memories.
Recalls intrinsically harmonic forms and open problems.
problem Open problems related to intrinsically harmonic forms.
method Recalling definitions and known results.
result Clarification of intrinsically harmonic forms and open questions.
Paper tackles imbalanced binary classification by optimizing precision and recall directly.
problem Imbalanced binary classification where standard accuracy is misleading.
method Exact constrained reformulations for precision and recall optimization.
result ERO framework outperforms state-of-the-art methods on multiple datasets.
LLMs can memorize economic data and recall exact values before their training cutoff.
problem Evaluating the trustworthiness of LLMs' economic forecasts during their training period.
method Demonstrated through counterfactual forecasting and analysis of LLMs' recall ability.
result LLMs have memorized economic and financial data, leading to recall-level accuracy before their knowledge cutoff.
RAGuard improves safety in LLMs for offshore wind maintenance.
problem Conventional LLMs fail with specialised or unexpected scenarios in offshore wind maintenance.
method Integrates safety-critical documents alongside technical manuals in RAG framework.
result RAGuard increases safety recall from almost 0% to over 50% while maintaining technical recall above 60%.
MassMutual uses neural network embeddings from financial news to predict downgrade risk.
problem Predicting downgrade risk in financial institutions using alternative data sources.
method Proposes a predictive downgrade model using neural network embeddings of financial news.
result Improves performance of benchmark model by more than 5 percent in terms of AUC and recall rate.
A new algorithm LONR learns without terminal states or perfect recall.
problem Learning in settings without terminal states or perfect recall.
method Local No-Regret Learning (LONR) using Q-learning-like updates.
result LONR achieves last iterate convergence in challenging settings.
Framework for precise recall control in spatial conflation tasks.
problem Precise recall control in large-scale spatial conflation tasks to avoid downstream analytics failures and excessive manual review.
method End-to-end framework using equigrid bounding-box filter, CSR representation, neural ranker, and inverse-variance weighted ensemble of threshold estimators.
result Achieves exact recall with sub-percent variance over tens of millions of geometry pairs, runs on a single TPU v3 core.
A new method for generating replay samples on the fly, optimizing for not forgetting.
problem Addressing the issue of forgetting in neural networks.
method Generates auxiliary samples on the fly using the model's implicit memory, specialized to each real training batch.
result Optimizing for not forgetting leads to more efficient and scalable generation of specialized samples.
Proposes a new tree-based algorithm for class-imbalanced data.
problem Classification of imbalanced data.
method Precision-Recall Curve (PRC) classification tree algorithm.
result The PRC classification tree outperforms classic algorithms for class-imbalanced data.
Optimizes trading strategy for cointegrated assets with bounded risk.
problem Maximizing profit from cointegrated assets with risk constraints.
method Formulates as convex optimization problem, then generalizes to bounded risk.
result Optimal strategy remains efficiently solvable even with bounded risk.
ClauseLens uses reinforcement learning to price reinsurance treaties transparently and auditably.
problem Opaque and difficult-to-audit reinsurance treaty pricing practices.
method ClauseLens models treaty pricing as a Risk-Aware Constrained Markov Decision Process (RA-CMDP), incorporating legal clauses and generating interpretable explanations.
result ClauseLens reduces solvency violations and improves tail-risk performance, achieving 88.2% accuracy in clause-grounded explanations.
AI-driven framework improves enterprise financial audits and risk identification.
problem Manual auditing is inefficient and limited by data complexity and evolving fraud tactics.
method Machine learning algorithms (SVM, RF, KNN) applied to a dataset of audit project counts, violations, and fraud instances.
result Random Forest achieves best performance with F1-score of 0.9012, identifying fraud and compliance anomalies.
Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlationcs.LG
The paper critiques and expands on common evaluation metrics in machine learning.
problem The common evaluation metrics like Precision, Recall, F-Measure, and Rand Accuracy are biased and misleading.
method The paper introduces new measures like Informedness, Markedness, and Correlation to better reflect the quality of predictions.
result A system that performs worse in terms of Informedness can appear better using common measures like Precision and Recall.
Predict real-time crash risks during hurricane evacuations using connected vehicle data.
problem Mitigate crash risks during hurricane evacuations by predicting high-risk locations.
method Used connected vehicle data to predict crash risks in real-time, considering weather and traffic features.
result Gaussian Process Boosting and Extreme Gradient Boosting models performed best, with recall of 0.91.
Paper introduces a new evaluation framework for generative models using Rényi divergences.
problem Lack of tools to diagnose and assess generative models' performance.
method Develops a general evaluation framework using Rényi divergences to measure precision and recall.
result Extends existing techniques to continuous and discrete models with efficient algorithms.
New findings show tool-augmented models can recall unlimited facts, outperforming purely memorized models.
problem Limitations of purely memorized models in recalling large amounts of factual information.
method Demonstrated the benefits of in-tool learning (external retrieval) over in-weight learning (memorization) for factual recall.
result Proved that tool-use enables unbounded factual recall via a simple and efficient circuit construction.
Online TERM improves robustness and fairness in streaming data.
problem Streaming data's lack of worst-case fairness and robustness in ERM.
method Proposes an online TERM formulation to balance average-case accuracy with worst-case fairness and robustness.
result Negative tilting effectively suppresses outlier influence, positive tilting improves recall with minimal precision loss.
The optimal ranking score between precision and recall is rarely F1 and can be found using specific methods.
problem Finding a meaningful and optimal compromise between precision and recall scores.
method Established a shortest path between precision- and recall-induced rankings, framed the problem as an optimization problem, and provided theoretical tools to find the optimal β.
result F1 and its skew-insensitive version are not optimal tradeoffs between precision and recall scores.
Recalls and refines the concept of algebraically rectifiable curves.
problem Classical notion of algebraically rectifiable plane curves.
method Provides new criteria, relates to quadratic differentials, and generalizes to higher order differentials.
result Generalization and new criteria for algebraic rectifiability.