SOTAVerified

Binary Classification

Papers

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Showing 1276–1300 of 2574 papers

TitleStatusHype
Online Passive-Aggressive Total-Error-Rate Minimization—0
On Lipschitz Continuity and Smoothness of Loss Functions in Learning to Rank—0
On Merging Feature Engineering and Deep Learning for Diagnosis, Risk-Prediction and Age Estimation Based on the 12-Lead ECG—0
On Regularized Sparse Logistic Regression—0
On Reject and Refine Options in Multicategory Classification—0
On statistical learning via the lens of compression—0
On support vector machines under a multiple-cost scenario—0
On the Bayes-optimality of F-measure maximizers—0
On the Calibration of Nested Dichotomies for Large Multiclass Tasks—0
On the complexity of PAC learning in Hilbert spaces—0
On the Consistency of Ordinal Regression Methods—0
On the Development of Binary Classification Algorithm Based on Principles of Geometry and Statistical Inference—0
On the Direction of Discrimination: An Information-Theoretic Analysis of Disparate Impact in Machine Learning—0
On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias—0
On the Efficiency of Subclass Knowledge Distillation in Classification Tasks—0
On the equivalence between Kolmogorov-Smirnov and ROC curve metrics for binary classification—0
On the Equivalence between Online and Private Learnability beyond Binary Classification—0
On the existence of consistent adversarial attacks in high-dimensional linear classification—0
On the existence of solutions to adversarial training in multiclass classification—0
On the Generalization Ability of Unsupervised Pretraining—0
On the Generalization of the C-Bound to Structured Output Ensemble Methods—0
On the Geometry of Receiver Operating Characteristic and Precision-Recall Curves—0
On the Geometry of Regularization in Adversarial Training: High-Dimensional Asymptotics and Generalization Bounds—0
On the Hardness of Robustness Transfer: A Perspective from Rademacher Complexity over Symmetric Difference Hypothesis Space—0
On the Implicit Bias of Linear Equivariant Steerable Networks—0
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All datasetsfakekickstarterGlyphnet DatasetTII-SSRC-23

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Trompt + OpenAI embeddingAUROC0.98—Unverified
2LightGBM + OpenAI embeddingAUROC0.97—Unverified
3FTTransformer + RoBERTa fintuneAUROC0.96—Unverified
4LightGBM + RoBERTa embeddingAUROC0.95—Unverified
5FTTransformer + RoBERTa embeddingAUROC0.94—Unverified
6ResNet + RoBERTa embeddingAUROC0.93—Unverified
7ResNet + OpenAI embeddingAUROC0.92—Unverified
8FTTransformer + OpenAI embeddingAUROC0.91—Unverified
#ModelMetricClaimedVerifiedStatus
1Trompt + OpenAI embeddingAUROC0.81—Unverified
2Multimodal-Net All-TextAUROC0.8—Unverified
3ResNet + RoBERTa finetuneAUROC0.79—Unverified
4LightGBM + RoBERTa embeddingAUROC0.77—Unverified
#ModelMetricClaimedVerifiedStatus
1Attention based CNNF1 score0.93—Unverified
#ModelMetricClaimedVerifiedStatus
1XGBoostF1-Score98.79—Unverified