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regression

Papers

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Showing 9151–9175 of 9424 papers

TitleStatusHype
Efficient Multiscale Gaussian Process Regression using Hierarchical Clustering—0
Efficient Normalized Conformal Prediction and Uncertainty Quantification for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests—0
Efficient Online Hyperparameter Optimization for Kernel Ridge Regression with Applications to Traffic Time Series Prediction—0
Efficient Optimization Algorithms for Linear Adversarial Training—0
Efficient Optimization for Sparse Gaussian Process Regression—0
Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method—0
Efficient Path Algorithms for Clustered Lasso and OSCAR—0
Efficient Rate Optimal Regret for Adversarial Contextual MDPs Using Online Function Approximation—0
Efficient Regression in Metric Spaces via Approximate Lipschitz Extension—0
Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data—0
Efficient Regularized Piecewise-Linear Regression Trees—0
Efficient Regularized Regression for Variable Selection with L0 Penalty—0
Efficient sampling for Gaussian linear regression with arbitrary priors—0
Efficient Sampling for Gaussian Process Inference using Control Variables—0
Efficient semi-supervised inference for logistic regression under case-control studies—0
Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces—0
Efficient Sparse Least Absolute Deviation Regression with Differential Privacy—0
Efficient Spatio-Temporal Gaussian Regression via Kalman Filtering—0
Efficient Strongly Polynomial Algorithms for Quantile Regression—0
Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression with Limited Observation—0
Efficient Symmetric Norm Regression via Linear Sketching—0
Efficient Tensor Kernel methods for sparse regression—0
Efficient Two-Stage Gaussian Process Regression Via Automatic Kernel Search and Subsampling—0
EFMVFL: An Efficient and Flexible Multi-party Vertical Federated Learning without a Third Party—0
EiTAKA at SemEval-2018 Task 1: An Ensemble of N-Channels ConvNet and XGboost Regressors for Emotion Analysis of Tweets—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