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Regression

4964 directly classified papers

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Papers

Convergence Rates of a Class of Multivariate Density Estimation Methods Based on Adaptive Partitioning JMLR 2023
Regularized Joint Mixture Models JMLR 2023
On the Convergence of CART under Sufficient Impurity Decrease Condition NIPS 2023
Adaptive Principal Component Regression with Applications to Panel Data NIPS 2023
Smoothed Online Learning for Prediction in Piecewise Affine Systems NIPS 2023
On the Asymptotic Learning Curves of Kernel Ridge Regression under Power-law Decay NIPS 2023
OKRidge: Scalable Optimal k-Sparse Ridge Regression NIPS 2023
Better Private Linear Regression Through Better Private Feature Selection NIPS 2023
Rethinking Gauss-Newton for learning over-parameterized models NIPS 2023
Computing Approximate $\ell_p$ Sensitivities NIPS 2023
Feature Adaptation for Sparse Linear Regression NIPS 2023
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent NIPS 2023
Distribution-Free Model-Agnostic Regression Calibration via Nonparametric Methods NIPS 2023
Calibration by Distribution Matching: Trainable Kernel Calibration Metrics NIPS 2023
Robustifying Generalizable Implicit Shape Networks with a Tunable Non-Parametric Model NIPS 2023
Regularization properties of adversarially-trained linear regression NIPS 2023
Optimal Excess Risk Bounds for Empirical Risk Minimization on $p$-Norm Linear Regression NIPS 2023
(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability NIPS 2023
No-Regret Learning with Unbounded Losses: The Case of Logarithmic Pooling NIPS 2023
Learning the Efficient Frontier NIPS 2023
Near-optimal learning with average Hölder smoothness NIPS 2023
Label Robust and Differentially Private Linear Regression: Computational and Statistical Efficiency NIPS 2023
Anytime Model Selection in Linear Bandits NIPS 2023
Imbalanced Mixed Linear Regression NIPS 2023
Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression NIPS 2023