Saved in:
| Main Authors: | Shikuri, Yuta, Fujisawa, Hironori |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.04421 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Surrogate Graph Partitioning for Spatial Prediction
by: Shikuri, Yuta, et al.
Published: (2025)
by: Shikuri, Yuta, et al.
Published: (2025)
Robust estimation with Lasso when outputs are adversarially contaminated
by: Sasai, Takeyuki, et al.
Published: (2020)
by: Sasai, Takeyuki, et al.
Published: (2020)
Adaptive Lasso, Transfer Lasso, and Beyond: An Asymptotic Perspective
by: Takada, Masaaki, et al.
Published: (2023)
by: Takada, Masaaki, et al.
Published: (2023)
Outlier Robust and Sparse Estimation of Linear Regression Coefficients
by: Sasai, Takeyuki, et al.
Published: (2022)
by: Sasai, Takeyuki, et al.
Published: (2022)
Adversarial robust weighted Huber regression
by: Sasai, Takeyuki, et al.
Published: (2021)
by: Sasai, Takeyuki, et al.
Published: (2021)
Adversarial Robust Low Rank Matrix Estimation: Compressed Sensing and Matrix Completion
by: Sasai, Takeyuki, et al.
Published: (2020)
by: Sasai, Takeyuki, et al.
Published: (2020)
Decomposed Quadratization: Efficient QUBO Formulation for Learning Bayesian Network
by: Shikuri, Yuta
Published: (2020)
by: Shikuri, Yuta
Published: (2020)
Learning from Summarized Data: Gaussian Process Regression with Sample Quasi-Likelihood
by: Shikuri, Yuta
Published: (2024)
by: Shikuri, Yuta
Published: (2024)
Likelihood-Free Gaussian Process for Regression
by: Shikuri, Yuta
Published: (2020)
by: Shikuri, Yuta
Published: (2020)
$L_2$-Regularized Empirical Risk Minimization Guarantees Small Smooth Calibration Error
by: Fujisawa, Masahiro, et al.
Published: (2025)
by: Fujisawa, Masahiro, et al.
Published: (2025)
Information-theoretic Generalization Analysis for Expected Calibration Error
by: Futami, Futoshi, et al.
Published: (2024)
by: Futami, Futoshi, et al.
Published: (2024)
Interpretable Deep Regression Models with Interval-Censored Failure Time Data
by: Yuan, Changhui, et al.
Published: (2025)
by: Yuan, Changhui, et al.
Published: (2025)
Learning Robust Treatment Rules for Censored Data
by: Cui, Yifan, et al.
Published: (2024)
by: Cui, Yifan, et al.
Published: (2024)
Learning High-dimensional Gaussians from Censored Data
by: Bhattacharyya, Arnab, et al.
Published: (2025)
by: Bhattacharyya, Arnab, et al.
Published: (2025)
On Difference Between Two Types of $γ$-divergence for Regression
by: Kawashima, Takayuki, et al.
Published: (2018)
by: Kawashima, Takayuki, et al.
Published: (2018)
Proximal Survival Analysis to Handle Dependent Right Censoring
by: Ying, Andrew
Published: (2022)
by: Ying, Andrew
Published: (2022)
Learning Survival Distributions with the Asymmetric Laplace Distribution
by: Sheng, Deming, et al.
Published: (2025)
by: Sheng, Deming, et al.
Published: (2025)
Understanding Overparametrization in Survival Models through Interpolation
by: Liu, Yin, et al.
Published: (2025)
by: Liu, Yin, et al.
Published: (2025)
An Easily Tunable Approach to Robust and Sparse High-Dimensional Linear Regression
by: Sasai, Takeyuki, et al.
Published: (2025)
by: Sasai, Takeyuki, et al.
Published: (2025)
Adaptive deep learning for nonlinear time series models
by: Kurisu, Daisuke, et al.
Published: (2022)
by: Kurisu, Daisuke, et al.
Published: (2022)
Dimension-Free Convergence of Discrete Diffusion Models: Adjoint Equations Induce the Right Space
by: Kan, Kelvin, et al.
Published: (2026)
by: Kan, Kelvin, et al.
Published: (2026)
Accurate Evaluation of Quickest Changepoint Detectors via Non-parametric Survival Analysis
by: Miyagawa, Taiki, et al.
Published: (2026)
by: Miyagawa, Taiki, et al.
Published: (2026)
Pseudo-Observations and Super Learner for the Estimation of the Restricted Mean Survival Time
by: Cwiling, Ariane, et al.
Published: (2024)
by: Cwiling, Ariane, et al.
Published: (2024)
Targeted Data Fusion for Region-Specific Survival Effects in the AMP HIV Prevention Trials
by: Liu, Yi, et al.
Published: (2025)
by: Liu, Yi, et al.
Published: (2025)
Valid Inference for Machine Learning Model Parameters
by: Dey, Neil, et al.
Published: (2023)
by: Dey, Neil, et al.
Published: (2023)
Provable Sample-Efficient Transfer Learning Conditional Diffusion Models via Representation Learning
by: Cheng, Ziheng, et al.
Published: (2025)
by: Cheng, Ziheng, et al.
Published: (2025)
Higher-Order Causal Structure Learning with Additive Models
by: Enouen, James, et al.
Published: (2025)
by: Enouen, James, et al.
Published: (2025)
Revisiting Optimism and Model Complexity in the Wake of Overparameterized Machine Learning
by: Patil, Pratik, et al.
Published: (2024)
by: Patil, Pratik, et al.
Published: (2024)
Reassessing How to Compare and Improve the Calibration of Machine Learning Models
by: Chidambaram, Muthu, et al.
Published: (2024)
by: Chidambaram, Muthu, et al.
Published: (2024)
When Does Model Collapse Occur in Structured Interactive Learning?
by: Wu, Yuchen, et al.
Published: (2026)
by: Wu, Yuchen, et al.
Published: (2026)
Transfer Learning on Edge Connecting Probability Estimation under Graphon Model
by: Wang, Yuyao, et al.
Published: (2025)
by: Wang, Yuyao, et al.
Published: (2025)
Efficient Inference for Inverse Reinforcement Learning and Dynamic Discrete Choice Models
by: van der Laan, Lars, et al.
Published: (2025)
by: van der Laan, Lars, et al.
Published: (2025)
Interactive Learning of Single-Index Models via Stochastic Gradient Descent
by: Rajaraman, Nived, et al.
Published: (2026)
by: Rajaraman, Nived, et al.
Published: (2026)
Information-Geometric Decomposition of Generalization Error in Unsupervised Learning
by: Kim, Gilhan
Published: (2026)
by: Kim, Gilhan
Published: (2026)
A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models
by: Suh, Namjoon, et al.
Published: (2024)
by: Suh, Namjoon, et al.
Published: (2024)
Statistical-Computational Trade-offs in Learning Multi-Index Models via Harmonic Analysis
by: Latourelle-Vigeant, Hugo, et al.
Published: (2026)
by: Latourelle-Vigeant, Hugo, et al.
Published: (2026)
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model
by: Shi, Laixi, et al.
Published: (2023)
by: Shi, Laixi, et al.
Published: (2023)
Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training
by: Wang, Kevin, et al.
Published: (2026)
by: Wang, Kevin, et al.
Published: (2026)
The Morgan-Pitman Test of Equality of Variances and its Application to Machine Learning Model Evaluation and Selection
by: Arratia, Argimiro, et al.
Published: (2025)
by: Arratia, Argimiro, et al.
Published: (2025)
Diffusion Models Are Statistically Optimal for Learning Low-Dimensional Multi-Modal Distributions
by: Wu, Jingda, et al.
Published: (2026)
by: Wu, Jingda, et al.
Published: (2026)
Similar Items
-
Surrogate Graph Partitioning for Spatial Prediction
by: Shikuri, Yuta, et al.
Published: (2025) -
Robust estimation with Lasso when outputs are adversarially contaminated
by: Sasai, Takeyuki, et al.
Published: (2020) -
Adaptive Lasso, Transfer Lasso, and Beyond: An Asymptotic Perspective
by: Takada, Masaaki, et al.
Published: (2023) -
Outlier Robust and Sparse Estimation of Linear Regression Coefficients
by: Sasai, Takeyuki, et al.
Published: (2022) -
Adversarial robust weighted Huber regression
by: Sasai, Takeyuki, et al.
Published: (2021)