:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Lee, Bonwoo, Park, Cheolwoo, Ahn, Jeongyoun
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Methodology
Online Access:	https://arxiv.org/abs/2507.17544
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Variable selection and basis learning for ordinal classification
by: Kim, Minwoo, et al.
Published: (2022)

Nonparametric extensions of randomized response for private confidence sets
by: Waudby-Smith, Ian, et al.
Published: (2022)

Simulation-Based Sensitivity Analysis in Optimal Treatment Regimes and Causal Decomposition with Individualized Interventions
by: Park, Soojin, et al.
Published: (2025)

Differentially private projection-depth-based medians
by: Ramsay, Kelly, et al.
Published: (2023)

Kriging via variably scaled kernels
by: Audone, Gianluca, et al.
Published: (2026)

Resampling methods for private statistical inference
by: Chadha, Karan, 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)

Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death
by: Park, Sihyung, et al.
Published: (2025)

Improving reproducibility by controlling random seed stability in machine learning based estimation via bagging
by: Williams, Nicholas, et al.
Published: (2026)

Integrated Subset Selection and Bandwidth Estimation Algorithm for Geographically Weighted Regression
by: Lee, Hyunwoo, et al.
Published: (2025)

Logistic-beta processes for dependent random probabilities with beta marginals
by: Lee, Changwoo J., et al.
Published: (2024)

A fast and effective kernel two-sample test for large-scale data
by: Song, Hoseung, et al.
Published: (2021)

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

Optimal Multitask Linear Regression and Contextual Bandits under Sparse Heterogeneity
by: Huang, Xinmeng, et al.
Published: (2023)

Counterfactual Survival Q-learning via Buckley-James Boosting, with Applications to ACTG 175 and CALGB 8923
by: Lee, Jeongjin, et al.
Published: (2025)

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

Federated fairness-aware classification under differential privacy
by: Xue, Gengyu, et al.
Published: (2026)

Causal Decomposition Analysis with Synergistic Interventions: A Triply-Robust Machine Learning Approach to Addressing Multiple Dimensions of Social Disparities
by: Park, Soojin, et al.
Published: (2025)

Fast kernel methods: Sobolev, physics-informed, and additive models
by: Doumèche, Nathan, et al.
Published: (2025)

Data-Driven Sequential Sampling for Tail Risk Mitigation
by: Ahn, Dohyun, et al.
Published: (2025)

Distribution learning via neural differential equations: minimal energy regularization and approximation theory
by: Marzouk, Youssef, et al.
Published: (2025)

Knowledge Distillation of Uncertainty using Deep Latent Factor Model
by: Park, Sehyun, et al.
Published: (2025)

A projected nonlinear state-space model for forecasting time series signals
by: Donner, Christian, et al.
Published: (2023)

Penalized Empirical Likelihood for Doubly Robust Causal Inference under Contamination in High Dimensions
by: Lee, Byeonghee, et al.
Published: (2025)

Nearly Optimal Subdata Selection
by: Yang, Min, et al.
Published: (2026)

Fusion of classical and quantum kernels enables accurate and robust two-sample tests
by: Terada, Yu, et al.
Published: (2025)

Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data
by: Risser, Mark D., et al.
Published: (2024)

Projected random forests and conformal prediction of circular data
by: F., Paulo C. Marques, et al.
Published: (2024)

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

GoMS: Graph of Molecule Substructure Network for Molecule Property Prediction
by: Qu, Shuhui, et al.
Published: (2025)

Inference with non-differentiable surrogate loss in a general high-dimensional classification framework
by: Liang, Muxuan, et al.
Published: (2024)

Entropic Riemannian Neural Optimal Transport
by: Micheli, Alessandro, et al.
Published: (2026)

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

Optimal Model Selection for Conformalized Robust Optimization
by: Bao, Yajie, et al.
Published: (2025)

Sensitivity analysis using the Metamodel of Optimal Prognosis
by: Most, Thomas, et al.
Published: (2024)

Locally Optimal Private Sampling: Beyond the Global Minimax
by: Ghoukasian, Hrad, et al.
Published: (2025)

A joint optimization approach to identifying sparse dynamics using least squares kernel collocation
by: Hsu, Alexander W., et al.
Published: (2025)

A statistical approach to latent dynamic modeling with differential equations
by: Hackenberg, Maren, et al.
Published: (2023)

Simplifying debiased inference via automatic differentiation and probabilistic programming
by: Luedtke, Alex
Published: (2024)

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