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.
Modified AUC improves CNN training by considering model confidence.
Paper estimates optimal ROC curve arc length and AUC, improving classification performance.
Improves ROC/AUC for multi-class classification.
Receiver operating characteristic (ROC) analysis is widely used for evaluating diagnostic systems. Recent studies have shown that estimating an area under ROC curve (AUC) with standard cross-validation methods suffers from a large bias. The leave-pair-out (LPO) cross-validation has been shown to correct this bias. Howe…
New optimization method improves AUC for binary classification and changepoint detection.
DCC separates marginal estimation from dependence modeling for improved classification accuracy.
Paper proposes a new algorithm to minimize AUC disparities in machine learning models.
The area under the ROC curve (AUC) is a widely used performance measure in machine learning. Increasingly, however, in several applications, ranging from ranking to biometric screening to medicine, performance is measured not in terms of the full area under the ROC curve, but in terms of the \emph{partial} area under t…
Deep ROC analysis improves model selection and interpretation in medical and AI applications.
ABROCA assesses algorithmic bias, revealing skewed distributions that inflate results.
A scalable ROC-SVM variant reduces training time for imbalanced binary classification.
New algorithm optimizes AUC in binary classification and changepoint detection.
Rapid overlay of chemical structures (ROCS) is a standard tool for the calculation of 3D shape and chemical ("color") similarity. ROCS uses unweighted sums to combine many aspects of similarity, yielding parameter-free models for virtual screening. In this report, we decompose the ROCS color force field into "color com…
Area under ROC (AUC) is an important metric for binary classification and bipartite ranking problems. However, it is difficult to directly optimizing AUC as a learning objective, so most existing algorithms are based on optimizing a surrogate loss to AUC. One significant drawback of these surrogate losses is that they …
Throughout science and technology, receiver operating characteristic (ROC) curves and associated area under the curve (AUC) measures constitute powerful tools for assessing the predictive abilities of features, markers and tests in binary classification problems. Despite its immense popularity, ROC analysis has been su…
Optimizes partial AUC across various FPRs for machine learning models.
This report examines the Pinned AUC metric introduced and highlights some of its limitations. Pinned AUC provides a threshold-agnostic measure of unintended bias in a classification model, inspired by the ROC-AUC metric. However, as we highlight in this report, there are ways that the metric can obscure different kinds…
Deep neural networks predict earthquake locations with high accuracy.
AUC used as a measure of clustering quality in unsupervised learning.
Study finds AUC is most consistent across different prevalence in binary classification.
Adequate evaluation of an information retrieval system to estimate future performance is a crucial task. Area under the ROC curve (AUC) is widely used to evaluate the generalization of a retrieval system. However, the objective function optimized in many retrieval systems is the error rate and not the AUC value. This p…
In many situations, we need to build and deploy separate models in related environments with different data qualities. For example, an environment with strong observation equipments (e.g., intensive care units) often provides high-quality multi-modal data, which are acquired from multiple sensory devices and have rich-…
We consider the problem of quantifying the quality of a model selection problem for a graphical model. We discuss this by formulating the problem as a detection problem. Model selection problems usually minimize a distance between the original distribution and the model distribution. For the special case of Gaussian di…
New models improve classification model performance, especially robust to small training sets.
The paper breaks down AUC into cluster-level components for better model diagnostics.
This paper surveys AUC maximization for big data and AI.
Improved FDAM algorithms for heterogeneous data with constant communication complexity.
AUC (Area under the ROC curve) is an important performance measure for applications where the data is highly imbalanced. Learning to maximize AUC performance is thus an important research problem. Using a max-margin based surrogate loss function, AUC optimization problem can be approximated as a pairwise rankSVM learni…
AUC (area under ROC curve) is an important evaluation criterion, which has been popularly used in many learning tasks such as class-imbalance learning, cost-sensitive learning, learning to rank, etc. Many learning approaches try to optimize AUC, while owing to the non-convexity and discontinuousness of AUC, almost all …
Study reveals significant performance flips in GLOD using repurposed graph classification datasets.
Proposes a method to evaluate classifiers with missing labels using multiple imputation.
This work optimizes model performance while ensuring fairness through AUC constraints.
Stochastic Gradient Descent has been widely studied with classification accuracy as a performance measure. However, these stochastic algorithms cannot be directly used when non-decomposable pairwise performance measures are used such as Area under the ROC curve (AUC) which is a common performance metric when the classe…
In this paper we consider the problem of maximizing the Area under the ROC curve (AUC) which is a widely used performance metric in imbalanced classification and anomaly detection. Due to the pairwise nonlinearity of the objective function, classical SGD algorithms do not apply to the task of AUC maximization. We propo…
TrialGraph uses graph machine learning to improve clinical trial design and predict side effects.
Proposes Isometric Graph Neural Networks to preserve graph distances.
The paper analyzes recalibration methods for binary classifiers under distribution shift.
New algorithm optimizes AUC for sparse high-dimensional data in online learning.
Neural network models improve ROC curve evaluation of biomarkers, focusing on age's role in physical activity-mortality association.
The paper tackles fairness in scoring functions for binary classification.
Deep Convolutional Neural Networks (DCNN) has shown excellent performance in a variety of machine learning tasks. This manuscript presents Deep Convolutional Neural Fields (DeepCNF), a combination of DCNN with Conditional Random Field (CRF), for sequence labeling with highly imbalanced label distribution. The widely-us…
AUC-spec optimizes graph-based SSL for complex label distributions.
Deep learning improves forensic matching of casings.
In many applications, monitoring area under the ROC curve (AUC) in a sliding window over a data stream is a natural way of detecting changes in the system. The drawback is that computing AUC in a sliding window is expensive, especially if the window size is large and the data flow is significant. In this paper we propo…
We propose a supervised anomaly detection method for data with inexact anomaly labels, where each label, which is assigned to a set of instances, indicates that at least one instance in the set is anomalous. Although many anomaly detection methods have been proposed, they cannot handle inexact anomaly labels. To measur…
The paper proves concentration inequalities for two-sample rank processes and applies them to ranking performance criteria.
Paper tackles multi-block min-max optimization with applications in deep AUC maximization.
In this work, we ask two questions: 1. Can we predict the type of community interested in a news article using only features from the article content? and 2. How well do these models generalize over time? To answer these questions, we compute well-studied content-based features on over 60K news articles from 4 communit…