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regression

Papers

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Showing 3851–3900 of 9424 papers

TitleStatusHype
EnsembleIV: Creating Instrumental Variables from Ensemble Learners for Robust Statistical Inference—0
BUT QUESST 2014 System Description—0
Ensemble Forecasting of Monthly Electricity Demand using Pattern Similarity-based Methods—0
Symbolic Regression on Sparse and Noisy Data with Gaussian Processes—0
GPTreeO: An R package for continual regression with dividing local Gaussian processes—0
GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification—0
Building a Legal Dialogue System: Development Process, Challenges and Opportunities—0
GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network—0
Enhancing VMAF through New Feature Integration and Model Combination—0
Buhscitu at SemEval-2020 Task 7: Assessing Humour in Edited News Headlines Using Hand-Crafted Features and Online Knowledge Bases—0
Gradient-Based Empirical Risk Minimization using Local Polynomial Regression—0
An implicit function learning approach for parametric modal regression—0
Gradient-based Optimization for Regression in the Functional Tensor-Train Format—0
A Consistent and Scalable Algorithm for Best Subset Selection in Single Index Models—0
Gradient-based Regularization Parameter Selection for Problems with Non-smooth Penalty Functions—0
Gradient Boosted Binary Histogram Ensemble for Large-scale Regression—0
Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis—0
Gradient boosting machine with partially randomized decision trees—0
Gradient Boosting Mapping for Dimensionality Reduction and Feature Extraction—0
Enhancing Supply Chain Resilience: A Machine Learning Approach for Predicting Product Availability Dates Under Disruption—0
Brute-Force Facial Landmark Analysis With A 140,000-Way Classifier—0
Enhancing Social Media Post Popularity Prediction with Visual Content—0
Enhancing Robustness of Gradient-Boosted Decision Trees through One-Hot Encoding and Regularization—0
Gradient Descent Converges Linearly for Logistic Regression on Separable Data—0
Ridge Regression with Over-Parametrized Two-Layer Networks Converge to Ridgelet Spectrum—0
Gradient Descent Finds Over-Parameterized Neural Networks with Sharp Generalization for Nonparametric Regression—0
Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization—0
Gradient Kernel Regression—0
Gradient Sparsification for Communication-Efficient Distributed Optimization—0
Gradient tree boosting with random output projections for multi-label classification and multi-output regression—0
Gradient Weights help Nonparametric Regressors—0
GradTail: Learning Long-Tailed Data Using Gradient-based Sample Weighting—0
Graph-based regularization for regression problems with alignment and highly-correlated designs—0
Broad Learning System Based on Maximum Correntropy Criterion—0
Enhancing Polynomial Chaos Expansion Based Surrogate Modeling using a Novel Probabilistic Transfer Learning Strategy—0
An Homotopy Algorithm for the Lasso with Online Observations—0
Adversarial Bi-Regressor Network for Domain Adaptive Regression—0
Enhancing Model Interpretability and Accuracy for Disease Progression Prediction via Phenotype-Based Patient Similarity Learning—0
Enhancing Microgrid Performance Prediction with Attention-based Deep Learning Models—0
Grapheme-to-Phoneme Conversion for Thai using Neural Regression Models—0
Bridging factor and sparse models—0
Practical Contextual Bandits with Feedback Graphs—0
Enhancing LambdaMART Using Oblivious Trees—0
Brewing Discontent: How U.S. Reciprocal Tariffs on Coffee Could Echo the Boston Tea Party—0
Angular upsampling in diffusion MRI using contextual HemiHex sub-sampling in q-space—0
GraphMDN: Leveraging graph structure and deep learning to solve inverse problems—0
Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution—0
Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regression—0
Bregman Distance to L1 Regularized Logistic Regression—0
HawkEye: Advancing Robust Regression with Bounded, Smooth, and Insensitive Loss Function—0
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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