Cover Image

Error Reduction from Stacked Regressions

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Xin, Klusowski, Jason M., Tan, Yan Shuo
Format:	Preprint
Published:	2023
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Stochastic Gradient Descent for Nonparametric Additive Regression
by: Chen, Xin, et al.
Published: (2024)

Revisiting Randomization in Greedy Model Search
by: Chen, Xin, et al.
Published: (2025)

Statistical-Computational Trade-offs for Recursive Adaptive Partitioning Estimators
by: Tan, Yan Shuo, et al.
Published: (2024)

Classification Imbalance as Transfer Learning
by: Xia, Eric, et al.
Published: (2026)

Sharp Convergence Rates for Matching Pursuit
by: Klusowski, Jason M., et al.
Published: (2023)

Coupled Training with Privileged Information and Unlabeled Data
by: Shi, Jiahao, et al.
Published: (2026)

Robust Transfer Learning with Unreliable Source Data
by: Fan, Jianqing, et al.
Published: (2023)

Decoding Game: On Minimax Optimality of Heuristic Text Generation Strategies
by: Chen, Sijin, et al.
Published: (2024)

Sharp Trade-Offs in High-Dimensional Inference via 2-Level SLOPE
by: Bu, Zhiqi, et al.
Published: (2025)

On the Pointwise Behavior of Recursive Partitioning and Its Implications for Heterogeneous Causal Effect Estimation
by: Cattaneo, Matias D., et al.
Published: (2022)

BFTS: Thompson Sampling with Bayesian Additive Regression Trees
by: Deng, Ruizhe, et al.
Published: (2026)

On the Implicit Bias of Adam
by: Cattaneo, Matias D., et al.
Published: (2023)

The Honest Truth About Causal Trees: Accuracy Limits for Heterogeneous Treatment Effect Estimation
by: Cattaneo, Matias D., et al.
Published: (2025)

Unified Transfer Learning Models in High-Dimensional Linear Regression
by: Liu, Shuo Shuo
Published: (2023)

Challenges in Variable Importance Ranking Under Correlation
by: Liang, Annie, et al.
Published: (2024)

Dimension Reduction for Symbolic Regression
by: Kahlmeyer, Paul, et al.
Published: (2025)

Generalized Resubstitution for Regression Error Estimation
by: Marcondes, Diego, et al.
Published: (2024)

Inference with Mondrian Random Forests
by: Cattaneo, Matias D., et al.
Published: (2023)

On the Computational Efficiency of Bayesian Additive Regression Trees: An Asymptotic Analysis
by: Tan, Yan Shuo, et al.
Published: (2024)

A Systematic Bias of Machine Learning Regression Models and Its Correction: an Application to Imaging-based Brain Age Prediction
by: Lee, Hwiyoung, et al.
Published: (2024)

Conformal Prediction Sets for Deep Generative Models via Reduction to Conformal Regression
by: Shahrokhi, Hooman, et al.
Published: (2025)

How does Labeling Error Impact Contrastive Learning? A Perspective from Data Dimensionality Reduction
by: Chen, Jun, et al.
Published: (2025)

A Meta-learning based Stacked Regression Approach for Customer Lifetime Value Prediction
by: Gadgil, Karan, et al.
Published: (2023)

Error Distribution Smoothing:Advancing Low-Dimensional Imbalanced Regression
by: Chen, Donghe, et al.
Published: (2025)

Tensor Hypercontraction Error Correction Using Regression
by: Satyarth, Ishna, et al.
Published: (2026)

The Verification Tax: Fundamental Limits of AI Auditing in the Rare-Error Regime
by: Wang, Jason Z
Published: (2026)

Detecting Errors in a Numerical Response via any Regression Model
by: Zhou, Hang, et al.
Published: (2023)

On Uniform Error Bounds for Kernel Regression under Non-Gaussian Noise
by: Teutsch, Johannes, et al.
Published: (2026)

One-Layer Transformer Provably Learns One-Nearest Neighbor In Context
by: Li, Zihao, et al.
Published: (2024)

Beyond the Norms: Detecting Prediction Errors in Regression Models
by: Altieri, Andres, et al.
Published: (2024)

On the Interpolation Error of Nonlinear Attention versus Linear Regression
by: Liao, Zhenyu, et al.
Published: (2025)

Expectation Error Bounds for Transfer Learning in Linear Regression and Linear Neural Networks
by: Liu, Meitong, et al.
Published: (2026)

Quantitative Error Feedback for Quantization Noise Reduction of Filtering over Graphs
by: Zheng, Xue Xian, et al.
Published: (2025)

Learning with Subset Stacking
by: Birbil, Ş. İlker, et al.
Published: (2021)

Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning
by: Sun, Chenglu, et al.
Published: (2025)

Inference for Large Scale Regression Models with Dependent Errors
by: Voirol, Lionel, et al.
Published: (2024)

TabClustPFN: A Prior-Fitted Network for Tabular Data Clustering
by: Zhao, Tianqi, et al.
Published: (2026)

Attention Based Machine Learning Methods for Data Reduction with Guaranteed Error Bounds
by: Li, Xiao, et al.
Published: (2024)

Learning Multi-Index Models with Hyper-Kernel Ridge Regression
by: Huang, Shuo, et al.
Published: (2025)

Robust Variable Selection for High-dimensional Regression with Missing Data and Measurement Errors
by: Zhang, Zhenhao, et al.
Published: (2024)