Copertina

Highly Adaptive Principal Component Regression

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Wang, Mingxun, Schuler, Alejandro, van der Laan, Mark, Meixide, Carlos García
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Highly Adaptive Empirical Risk Minimization with Principal Components
di: Meixide, Carlos García, et al.
Pubblicazione: (2026)

Highly Adaptive Ridge
di: Schuler, Alejandro, et al.
Pubblicazione: (2024)

Self-Adaptive Quantum Kernel Principal Components Analysis for Compact Readout of Chemiresistive Sensor Arrays
di: Wang, Zeheng, et al.
Pubblicazione: (2024)

Adaptive-TMLE for the Average Treatment Effect based on Randomized Controlled Trial Augmented with Real-World Data
di: van der Laan, Mark, et al.
Pubblicazione: (2024)

Adaptive debiased machine learning using data-driven model selection techniques
di: van der Laan, Lars, et al.
Pubblicazione: (2023)

Adaptive Principal Component Regression with Applications to Panel Data
di: Agarwal, Anish, et al.
Pubblicazione: (2023)

Calibrated Principal Component Regression
di: Wu, Yixuan Florence, et al.
Pubblicazione: (2025)

Targeted Deep Architectures: A TMLE-Based Framework for Robust Causal Inference in Neural Networks
di: Li, Yi, et al.
Pubblicazione: (2025)

Causal inference via implied interventions
di: Meixide, Carlos García, et al.
Pubblicazione: (2025)

Generative Principal Component Regression via Variational Inference
di: Talbot, Austin, et al.
Pubblicazione: (2024)

Inverse Reinforcement Learning with Just Classification and a Few Regressions
di: van der Laan, Lars, et al.
Pubblicazione: (2025)

RieszBoost: Gradient Boosting for Riesz Regression
di: Lee, Kaitlyn J., et al.
Pubblicazione: (2025)

Calibeating Prediction-Powered Inference
di: van der Laan, Lars, et al.
Pubblicazione: (2026)

A Researcher's Guide to Empirical Risk Minimization
di: van der Laan, Lars
Pubblicazione: (2026)

Identifying Neural Signatures from fMRI using Hybrid Principal Components Regression
di: Rieck, Jared, et al.
Pubblicazione: (2025)

Stationary Reweighting Yields Local Convergence of Soft Fitted Q-Iteration
di: van der Laan, Lars, et al.
Pubblicazione: (2025)

Fitted $Q$ Evaluation Without Bellman Completeness via Stationary Weighting
di: van der Laan, Lars, et al.
Pubblicazione: (2025)

Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction
di: van der Laan, Lars, et al.
Pubblicazione: (2025)

Bellman Calibration for $V$-Learning in Offline Reinforcement Learning
di: van der Laan, Lars, et al.
Pubblicazione: (2025)

Neural interval-censored survival regression with feature selection
di: Meixide, Carlos García, et al.
Pubblicazione: (2022)

Self-Calibrating Conformal Prediction
di: van der Laan, Lars, et al.
Pubblicazione: (2024)

Self‐Adaptive Quantum Kernel Principal Component Analysis for Compact Readout of Chemiresistive Sensor Arrays
di: Zeheng Wang, et al.
Pubblicazione: (2025)

Machine learning to optimize precision in the analysis of randomized trials: A journey in pre-specified, yet data-adaptive learning
di: Balzer, Laura B., et al.
Pubblicazione: (2025)

Heavy-Tailed Principal Component Analysis
di: Sayde, Mario, et al.
Pubblicazione: (2026)

Hybrid Meta-learners for Estimating Heterogeneous Treatment Effects
di: Liang, Zhongyuan, et al.
Pubblicazione: (2025)

Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts
di: van der Laan, Lars, et al.
Pubblicazione: (2024)

Quasicyclic Principal Component Analysis
di: Rumsey, Susanna E., et al.
Pubblicazione: (2025)

Robust Principal Component Completion
di: Wang, Yinjian, et al.
Pubblicazione: (2026)

Efficient Inference for Inverse Reinforcement Learning and Dynamic Discrete Choice Models
di: van der Laan, Lars, et al.
Pubblicazione: (2025)

Knowledge Transfer across Multiple Principal Component Analysis Studies
di: Li, Zeyu, et al.
Pubblicazione: (2024)

Statistical learning for constrained functional parameters in infinite-dimensional models
di: Nabi, Razieh, et al.
Pubblicazione: (2024)

Riemannian Principal Component Analysis
di: Rodríguez, Oldemar
Pubblicazione: (2025)

Spectrum-Aware Debiasing: A Modern Inference Framework with Applications to Principal Components Regression
di: Li, Yufan, et al.
Pubblicazione: (2023)

Principal Component Analysis in Space Forms
di: Tabaghi, Puoya, et al.
Pubblicazione: (2023)

Score-Preserving Targeted Maximum Likelihood Estimation
di: Pimentel, Noel, et al.
Pubblicazione: (2025)

Principal Components for Neural Network Initialization
di: Phan, Nhan, et al.
Pubblicazione: (2025)

An Interpretable and Stable Framework for Sparse Principal Component Analysis
di: Hu, Ying, et al.
Pubblicazione: (2026)

TL-PCA: Transfer Learning of Principal Component Analysis
di: Hendy, Sharon, et al.
Pubblicazione: (2024)

Nonparametric Instrumental Variable Inference with Many Weak Instruments
di: van der Laan, Lars, et al.
Pubblicazione: (2025)

Doubly robust inference via calibration
di: van der Laan, Lars, et al.
Pubblicazione: (2024)