:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	An, Bingxue, Tang, Tiffany M.
Format:	Preprint
Published:	2025
Subjects:	Methodology Machine Learning
Online Access:	https://arxiv.org/abs/2512.15802
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Random-projection ensemble dimension reduction
by: Zhou, Wenxing, et al.
Published: (2024)

Imputation of missing values in multi-view data
by: van Loon, Wouter, et al.
Published: (2022)

Interpretable Network-assisted Random Forest+
by: Tang, Tiffany M., et al.
Published: (2025)

Expected information gain estimation via density approximations: Sample allocation and dimension reduction
by: Li, Fengyi, et al.
Published: (2024)

A spectral method for multi-view subspace learning using the product of projections
by: Sergazinov, Renat, et al.
Published: (2024)

On metric choice in dimension reduction for Fréchet regression
by: Soale, Abdul-Nasah, et al.
Published: (2024)

Inference on covariance structure in high-dimensional multi-view data
by: Mauri, Lorenzo, et al.
Published: (2025)

Statistical description and dimension reduction of continuous time categorical trajectories with multivariate functional principal components
by: Cardot, Hervé, et al.
Published: (2025)

Local MDI+: Local Feature Importances for Tree-Based Models
by: Liang, Zhongyuan, et al.
Published: (2025)

View selection in multi-view stacking: Choosing the meta-learner
by: van Loon, Wouter, et al.
Published: (2020)

Generative adversarial learning with optimal input dimension and its adaptive generator architecture
by: Tan, Zhiyao, et al.
Published: (2024)

Sequential design of multi-fidelity computer experiments: maximizing the rate of stepwise uncertainty reduction
by: Stroh, Rémi, et al.
Published: (2020)

Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design
by: Saves, Paul, et al.
Published: (2025)

Integrating Random Forests and Generalized Linear Models for Improved Accuracy and Interpretability
by: Agarwal, Abhineet, et al.
Published: (2023)

Vertical Consensus Inference for High-Dimensional Random Partition
by: Nguyen, Khai, et al.
Published: (2026)

Automatic debiasing of neural networks via moment-constrained learning
by: Hines, Christian L., et al.
Published: (2024)

Calibrated Multi-Level Quantile Forecasting
by: Ding, Tiffany, et al.
Published: (2025)

Causal inference through multi-stage learning and doubly robust deep neural networks
by: Zhang, Yuqian, et al.
Published: (2024)

Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of components
by: Carmona, Chris U., et al.
Published: (2020)

Multi-task learning via robust regularized clustering with non-convex group penalties
by: Okazaki, Akira, 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)

Survey on Algorithms for multi-index models
by: Bruna, Joan, 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)

Transfer Q-learning
by: Chen, Elynn, et al.
Published: (2022)

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

Misspecification-robust likelihood-free inference in high dimensions
by: Thomas, Owen, et al.
Published: (2020)

A spectral clustering-type algorithm for the consistent estimation of the Hurst distribution in moderately high dimensions
by: Abry, Patrice, et al.
Published: (2025)

A general framework for adaptive nonparametric dimensionality reduction
by: Di Noia, Antonio, et al.
Published: (2025)

Towards a pretrained deep learning estimator of the Linfoot informational correlation
by: Berg, Stéphanie M. van den, et al.
Published: (2025)

Variance reduction combining pre-experiment and in-experiment data
by: Lin, Zhexiao, et al.
Published: (2024)

Causal-learn: Causal Discovery in Python
by: Zheng, Yujia, et al.
Published: (2023)

Generalized Bayesian deep reinforcement learning
by: Roy, Shreya Sinha, et al.
Published: (2024)

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

Nonparametric estimation of time-varying network connections by multi-stage smoothing
by: Lee, Jeonghwan, et al.
Published: (2026)

Transfer learning under latent space model
by: Fang, Kuangnan, et al.
Published: (2025)

Semi-supervised learning for linear extremile regression
by: Jiang, Rong, et al.
Published: (2025)

Causal hybrid modeling with double machine learning
by: Cohrs, Kai-Hendrik, et al.
Published: (2024)

Accounting for multiplicity in machine learning benchmark performance
by: Møllersen, Kajsa, et al.
Published: (2023)

Scale-adaptive and robust intrinsic dimension estimation via optimal neighbourhood identification
by: Di Noia, Antonio, et al.
Published: (2024)

Quantile deep learning models for multi-step ahead time series prediction
by: Cheung, Jimmy, et al.
Published: (2024)