RieszBoost: Gradient Boosting for Riesz Regression
Fuente:
arXiv
Saved in:
| Main Authors: | Lee, Kaitlyn J., Schuler, Alejandro |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Riesz Regression As Direct Density Ratio Estimation
by: Kato, Masahiro
Published: (2025)
by: Kato, Masahiro
Published: (2025)
Statistical Inference for Gradient Boosting Regression
by: Fang, Haimo, et al.
Published: (2025)
by: Fang, Haimo, et al.
Published: (2025)
Nearest Neighbor Matching as Least Squares Density Ratio Estimation and Riesz Regression
by: Kato, Masahiro
Published: (2025)
by: Kato, Masahiro
Published: (2025)
Stagewise Boosting Distributional Regression
by: Wetscher, Mattias, et al.
Published: (2024)
by: Wetscher, Mattias, et al.
Published: (2024)
genriesz: A Python Package for Automatic Debiased Machine Learning with Generalized Riesz Regression
by: Kato, Masahiro
Published: (2026)
by: Kato, Masahiro
Published: (2026)
Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning
by: Kato, Masahiro
Published: (2026)
by: Kato, Masahiro
Published: (2026)
A Unified Theory for Causal Inference: Direct Debiased Machine Learning via Bregman-Riesz Regression
by: Kato, Masahiro
Published: (2025)
by: Kato, Masahiro
Published: (2025)
Gradient Boosting for Spatial Panel Models with Random and Fixed Effects
by: Balzer, Michael, et al.
Published: (2026)
by: Balzer, Michael, et al.
Published: (2026)
Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning
by: Matsubara, Takuo
Published: (2024)
by: Matsubara, Takuo
Published: (2024)
ScoreMatchingRiesz: Score Matching for Debiased Machine Learning and Policy Path Estimation
by: Kato, Masahiro
Published: (2025)
by: Kato, Masahiro
Published: (2025)
Boosted Conformal Prediction Intervals
by: Xie, Ran, et al.
Published: (2024)
by: Xie, Ran, et al.
Published: (2024)
Improving reproducibility by controlling random seed stability in machine learning based estimation via bagging
by: Williams, Nicholas, et al.
Published: (2026)
by: Williams, Nicholas, et al.
Published: (2026)
Fréchet Geodesic Boosting
by: Zhou, Yidong, et al.
Published: (2025)
by: Zhou, Yidong, et al.
Published: (2025)
A Unified Framework for Debiased Machine Learning: Riesz Representer Fitting under Bregman Divergence
by: Kato, Masahiro
Published: (2026)
by: Kato, Masahiro
Published: (2026)
Gaussian Process Boosting
by: Sigrist, Fabio
Published: (2020)
by: Sigrist, Fabio
Published: (2020)
Gradient Boosted Mixed Models: Flexible Joint Estimation of Mean and Variance Components for Clustered Data
by: Prevett, Mitchell L., et al.
Published: (2025)
by: Prevett, Mitchell L., et al.
Published: (2025)
Counterfactual Survival Q-learning via Buckley-James Boosting, with Applications to ACTG 175 and CALGB 8923
by: Lee, Jeongjin, et al.
Published: (2025)
by: Lee, Jeongjin, et al.
Published: (2025)
Newfluence: Boosting Model interpretability and Understanding in High Dimensions
by: Zou, Haolin, et al.
Published: (2025)
by: Zou, Haolin, et al.
Published: (2025)
Boosting with copula-based components
by: Brant, Simon Boge, et al.
Published: (2022)
by: Brant, Simon Boge, et al.
Published: (2022)
Diffusion Boosted Trees
by: Han, Xizewen, et al.
Published: (2024)
by: Han, Xizewen, et al.
Published: (2024)
Bridging Binarization: Causal Inference with Dichotomized Continuous Exposures
by: Lee, Kaitlyn J., et al.
Published: (2024)
by: Lee, Kaitlyn J., et al.
Published: (2024)
Machine Learning Assisted Adjustment Boosts Efficiency of Exact Inference in Randomized Controlled Trials
by: Yu, Han, et al.
Published: (2024)
by: Yu, Han, et al.
Published: (2024)
Boosting the Power of Kernel Two-Sample Tests
by: Chatterjee, Anirban, et al.
Published: (2023)
by: Chatterjee, Anirban, et al.
Published: (2023)
Doubly-Regressing Approach for Subgroup Fairness
by: Kim, Kunwoong, et al.
Published: (2025)
by: Kim, Kunwoong, et al.
Published: (2025)
A Riesz Representer Perspective on Targeted Learning
by: Balkus, Salvador V., et al.
Published: (2026)
by: Balkus, Salvador V., et al.
Published: (2026)
Integrated Subset Selection and Bandwidth Estimation Algorithm for Geographically Weighted Regression
by: Lee, Hyunwoo, et al.
Published: (2025)
by: Lee, Hyunwoo, et al.
Published: (2025)
Fast Robust Kernel Regression through Sign Gradient Descent with Early Stopping
by: Allerbo, Oskar
Published: (2023)
by: Allerbo, Oskar
Published: (2023)
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)
by: Lee, Hwiyoung, et al.
Published: (2024)
Optimal Multitask Linear Regression and Contextual Bandits under Sparse Heterogeneity
by: Huang, Xinmeng, et al.
Published: (2023)
by: Huang, Xinmeng, et al.
Published: (2023)
LASSO Inference for High Dimensional Predictive Regressions
by: Gao, Zhan, et al.
Published: (2024)
by: Gao, Zhan, et al.
Published: (2024)
Semiparametric Counterfactual Regression
by: Kim, Kwangho
Published: (2025)
by: Kim, Kwangho
Published: (2025)
Calibrated Multivariate Regression with Localized PIT Mappings
by: Kock, Lucas, et al.
Published: (2024)
by: Kock, Lucas, et al.
Published: (2024)
Deep Fréchet Regression
by: Iao, Su I, et al.
Published: (2024)
by: Iao, Su I, et al.
Published: (2024)
Generative Regression with IQ-BART
by: O'Hagan, Sean, et al.
Published: (2025)
by: O'Hagan, Sean, et al.
Published: (2025)
Robust Tensor-on-Tensor Regression
by: Hirari, Mehdi, et al.
Published: (2026)
by: Hirari, Mehdi, et al.
Published: (2026)
Bayesian Additive Distribution Regression
by: Linero, Antonio R., et al.
Published: (2026)
by: Linero, Antonio R., et al.
Published: (2026)
CP Degeneracy in Tensor Regression
by: Zhou, Ya, et al.
Published: (2020)
by: Zhou, Ya, et al.
Published: (2020)
Vector Quantile Regression on Manifolds
by: Pegoraro, Marco, et al.
Published: (2023)
by: Pegoraro, Marco, et al.
Published: (2023)
Direct Bayesian Additive Regression Trees for Conditional Average Treatment Effects in Regression Discontinuity Designs
by: Kondo, Daisuke, et al.
Published: (2026)
by: Kondo, Daisuke, et al.
Published: (2026)
CREDO: Epistemic-Aware Conformalized Credal Envelopes for Regression
by: Cabezas, Luben M. C., et al.
Published: (2026)
by: Cabezas, Luben M. C., et al.
Published: (2026)
Similar Items
-
Riesz Regression As Direct Density Ratio Estimation
by: Kato, Masahiro
Published: (2025) -
Statistical Inference for Gradient Boosting Regression
by: Fang, Haimo, et al.
Published: (2025) -
Nearest Neighbor Matching as Least Squares Density Ratio Estimation and Riesz Regression
by: Kato, Masahiro
Published: (2025) -
Stagewise Boosting Distributional Regression
by: Wetscher, Mattias, et al.
Published: (2024) -
genriesz: A Python Package for Automatic Debiased Machine Learning with Generalized Riesz Regression
by: Kato, Masahiro
Published: (2026)