Optimal differentially private kernel learning with random projection
Fuente:
arXiv
Saved in:
| Main Authors: | Lee, Bonwoo, Park, Cheolwoo, Ahn, Jeongyoun |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| 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)
by: Kim, Minwoo, 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)
by: Park, Soojin, et al.
Published: (2025)
Nonparametric extensions of randomized response for private confidence sets
by: Waudby-Smith, Ian, et al.
Published: (2022)
by: Waudby-Smith, Ian, et al.
Published: (2022)
Kriging via variably scaled kernels
by: Audone, Gianluca, et al.
Published: (2026)
by: Audone, Gianluca, et al.
Published: (2026)
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)
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)
by: Park, Sihyung, et al.
Published: (2025)
Differentially private projection-depth-based medians
by: Ramsay, Kelly, et al.
Published: (2023)
by: Ramsay, Kelly, et al.
Published: (2023)
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)
Resampling methods for private statistical inference
by: Chadha, Karan, et al.
Published: (2024)
by: Chadha, Karan, et al.
Published: (2024)
Logistic-beta processes for dependent random probabilities with beta marginals
by: Lee, Changwoo J., et al.
Published: (2024)
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)
by: Song, Hoseung, et al.
Published: (2021)
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)
Random-projection ensemble dimension reduction
by: Zhou, Wenxing, et al.
Published: (2024)
by: Zhou, Wenxing, 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)
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)
Federated fairness-aware classification under differential privacy
by: Xue, Gengyu, et al.
Published: (2026)
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)
by: Park, Soojin, et al.
Published: (2025)
A spectral method for multi-view subspace learning using the product of projections
by: Sergazinov, Renat, et al.
Published: (2024)
by: Sergazinov, Renat, et al.
Published: (2024)
A projected nonlinear state-space model for forecasting time series signals
by: Donner, Christian, et al.
Published: (2023)
by: Donner, Christian, et al.
Published: (2023)
Knowledge Distillation of Uncertainty using Deep Latent Factor Model
by: Park, Sehyun, et al.
Published: (2025)
by: Park, Sehyun, et al.
Published: (2025)
Nearly Optimal Subdata Selection
by: Yang, Min, et al.
Published: (2026)
by: Yang, Min, et al.
Published: (2026)
Fast kernel methods: Sobolev, physics-informed, and additive models
by: Doumèche, Nathan, et al.
Published: (2025)
by: Doumèche, Nathan, et al.
Published: (2025)
Projected random forests and conformal prediction of circular data
by: F., Paulo C. Marques, et al.
Published: (2024)
by: F., Paulo C. Marques, et al.
Published: (2024)
Penalized Empirical Likelihood for Doubly Robust Causal Inference under Contamination in High Dimensions
by: Lee, Byeonghee, et al.
Published: (2025)
by: Lee, Byeonghee, et al.
Published: (2025)
Entropic Riemannian Neural Optimal Transport
by: Micheli, Alessandro, et al.
Published: (2026)
by: Micheli, Alessandro, et al.
Published: (2026)
Inference with non-differentiable surrogate loss in a general high-dimensional classification framework
by: Liang, Muxuan, et al.
Published: (2024)
by: Liang, Muxuan, et al.
Published: (2024)
Transfer Q-learning
by: Chen, Elynn, et al.
Published: (2022)
by: Chen, Elynn, et al.
Published: (2022)
Distribution learning via neural differential equations: minimal energy regularization and approximation theory
by: Marzouk, Youssef, et al.
Published: (2025)
by: Marzouk, Youssef, et al.
Published: (2025)
Data-Driven Sequential Sampling for Tail Risk Mitigation
by: Ahn, Dohyun, et al.
Published: (2025)
by: Ahn, Dohyun, et al.
Published: (2025)
Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts
by: van der Laan, Lars, et al.
Published: (2024)
by: van der Laan, Lars, et al.
Published: (2024)
Optimal Model Selection for Conformalized Robust Optimization
by: Bao, Yajie, et al.
Published: (2025)
by: Bao, Yajie, et al.
Published: (2025)
Sensitivity analysis using the Metamodel of Optimal Prognosis
by: Most, Thomas, et al.
Published: (2024)
by: Most, Thomas, et al.
Published: (2024)
A Unified View of Optimal Kernel Hypothesis Testing
by: Schrab, Antonin
Published: (2025)
by: Schrab, Antonin
Published: (2025)
Near-Optimal Approximations for Bayesian Inference in Function Space
by: Wild, Veit, et al.
Published: (2025)
by: Wild, Veit, et al.
Published: (2025)
Fusion of classical and quantum kernels enables accurate and robust two-sample tests
by: Terada, Yu, et al.
Published: (2025)
by: Terada, Yu, et al.
Published: (2025)
Nearly Optimal Best Arm Identification for Semiparametric Bandits
by: Kim, Seok-Jin
Published: (2026)
by: Kim, Seok-Jin
Published: (2026)
Aggregation Models with Optimal Weights for Distributed Gaussian Processes
by: Chen, Haoyuan, et al.
Published: (2024)
by: Chen, Haoyuan, et al.
Published: (2024)
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data
by: Risser, Mark D., et al.
Published: (2024)
by: Risser, Mark D., et al.
Published: (2024)
Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data
by: Karlsson, Rickard, et al.
Published: (2025)
by: Karlsson, Rickard, et al.
Published: (2025)
Causal-learn: Causal Discovery in Python
by: Zheng, Yujia, et al.
Published: (2023)
by: Zheng, Yujia, et al.
Published: (2023)
Similar Items
-
Variable selection and basis learning for ordinal classification
by: Kim, Minwoo, 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) -
Nonparametric extensions of randomized response for private confidence sets
by: Waudby-Smith, Ian, et al.
Published: (2022) -
Kriging via variably scaled kernels
by: Audone, Gianluca, et al.
Published: (2026) -
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)