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regression

Papers

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Showing 301–325 of 9424 papers

TitleStatusHype
Explaining the Success of Nearest Neighbor Methods in Prediction—0
Optimizing Pre-Training Data Mixtures with Mixtures of Data Expert Models—0
Comparative Analysis of Black Hole Mass Estimation in Type-2 AGNs: Classical vs. Quantum Machine Learning and Deep Learning Approaches—0
Generalization Certificates for Adversarially Robust Bayesian Linear Regression—0
biastest: Testing parameter equality across different models in Stata—0
Modelling the term-structure of default risk under IFRS 9 within a multistate regression framework—0
Regression in EO: Are VLMs Up to the Challenge?—0
CARE: Confidence-Aware Regression Estimation of building density fine-tuning EO Foundation Models—0
OGBoost: A Python Package for Ordinal Gradient Boosting—0
A Study on Monthly Marine Heatwave Forecasts in New Zealand: An Investigation of Imbalanced Regression Loss Functions with Neural Network Models—0
Likelihood-Ratio Regularized Quantile Regression: Adapting Conformal Prediction to High-Dimensional Covariate Shifts—0
The Relationship Between Head Injury and Alzheimer's Disease: A Causal Analysis with Bayesian NetworksCode0
Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression—0
Task Shift: From Classification to Regression in Overparameterized Linear ModelsCode0
Asymptotic Optimism of Random-Design Linear and Kernel Regression Models—0
A Pathwise Coordinate Descent Algorithm for LASSO Penalized Quantile Regression—0
Bi-invariant Geodesic Regression with Data from the Osteoarthritis Initiative—0
Transfer Learning of CATE with Kernel Ridge RegressionCode0
On the kernel learning problem—0
PAR-AdvGAN: Improving Adversarial Attack Capability with Progressive Auto-Regression AdvGAN—0
Generalized Factor Neural Network Model for High-dimensional Regression—0
Using Artificial Intelligence to Improve Classroom Learning Experience—0
Towards Self-Supervised Covariance Estimation in Deep Heteroscedastic Regression—0
Self-Normalized Inference in (Quantile, Expected Shortfall) Regressions for Time Series—0
Variational empirical Bayes variable selection in high-dimensional logistic regression—0
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All datasetsCalifornia Housing PricesConcrete Compressive StrengthMedical Cost Personal DatasetSynthetic: y = x * sin xADORECar_Price_Prediction

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Neural NetworkR2 Score0.79—Unverified
2Baseline RegressionR2 Score0.58—Unverified
3Mimic / SurrogateR2 Score0.57—Unverified
#ModelMetricClaimedVerifiedStatus
1Neural NetworkR2 Score0.86—Unverified
2Baseline RegressionR2 Score0.59—Unverified
3Mimic / SurrogateR2 Score0.58—Unverified
#ModelMetricClaimedVerifiedStatus
1Neural NetworkR2 Score0.87—Unverified
2Baseline RegressionR2 Score0.78—Unverified
3Mimic / SurrogateR2 Score0.78—Unverified
#ModelMetricClaimedVerifiedStatus
1Neural NetworkR2 Score0.98—Unverified
2Baseline RegressionR2 Score-0.01—Unverified
3Mimic / SurrogateR2 Score-0.01—Unverified
#ModelMetricClaimedVerifiedStatus
1XGBoost-MACCSmicro-averaged RMSE0.93—Unverified
2RF-ToxPrintchemical macro-average RMSE0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1Linear and Decision Tree RegressionR Squared86.84—Unverified