:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Liu, Haixia, Huang, Ruifan
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2601.22625
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Fairness via Independence: A (Conditional) Distance Covariance Framework
by: Huang, Ruifan, et al.
Published: (2024)

BlockRR: A Unified Framework of RR-type Algorithms for Label Differential Privacy
by: Liu, Haixia, et al.
Published: (2026)

Enhancing Learning with Label Differential Privacy by Vector Approximation
by: Zhao, Puning, et al.
Published: (2024)

Private Linear Regression with Differential Privacy and PAC Privacy
by: Yang, Hillary, et al.
Published: (2024)

The Exploration of Error Bounds in Classification with Noisy Labels
by: Liu, Haixia, et al.
Published: (2025)

Personalized Differential Privacy for Ridge Regression
by: Acharya, Krishna, et al.
Published: (2024)

On Theoretical Limits of Learning with Label Differential Privacy
by: Zhao, Puning, et al.
Published: (2025)

Binary structured physics-informed neural networks for solving equations with rapidly changing solutions
by: Liu, Yanzhi, et al.
Published: (2024)

Efficient Sparse Least Absolute Deviation Regression with Differential Privacy
by: Liu, Weidong, et al.
Published: (2024)

Differential Privacy Mechanisms in Neural Tangent Kernel Regression
by: Gu, Jiuxiang, et al.
Published: (2024)

DistPred: A Distribution-Free Probabilistic Inference Method for Regression and Forecasting
by: Liang, Daojun, et al.
Published: (2024)

Progressive Supervision via Label Decomposition: An Long-Term and Large-Scale Wireless Traffic Forecasting Method
by: Liang, Daojun, et al.
Published: (2025)

Privacy Leakage via Output Label Space and Differentially Private Continual Learning
by: Tobaben, Marlon, et al.
Published: (2024)

Horseshoe Prior Bayesian Quantile Regression
by: Kohns, David, et al.
Published: (2020)

Label Shift Estimation With Incremental Prior Update
by: Zhang, Yunrui, et al.
Published: (2026)

Multi-Head Encoding for Extreme Label Classification
by: Liang, Daojun, et al.
Published: (2024)

Can Class-Priors Help Single-Positive Multi-Label Learning?
by: Liu, Biao, et al.
Published: (2023)

Residue-based Label Protection Mechanisms in Vertical Logistic Regression
by: Tan, Juntao, et al.
Published: (2022)

Observational Auditing of Label Privacy
by: Kalemaj, Iden, et al.
Published: (2025)

Differentially Private Geodesic Regression
by: Kulkarni, Aditya, et al.
Published: (2025)

GRASP: Grouped Regression with Adaptive Shrinkage Priors
by: Tew, Shu Yu, et al.
Published: (2025)

Semi-Supervised Regression with Heteroscedastic Pseudo-Labels
by: Sun, Xueqing, et al.
Published: (2025)

The Exploration of Neural Collapse under Imbalanced Data
by: Liu, Haixia
Published: (2024)

Forward Learning with Differential Privacy
by: Feng, Mingqian, et al.
Published: (2025)

Learning with User-Level Local Differential Privacy
by: Zhao, Puning, et al.
Published: (2024)

Revisiting the Role of Label Smoothing in Enhanced Text Sentiment Classification
by: Gao, Yijie, et al.
Published: (2023)

Wasserstein Differential Privacy
by: Yang, Chengyi, et al.
Published: (2024)

Privacy-Preserving Conformal Prediction Under Local Differential Privacy
by: Penso, Coby, et al.
Published: (2025)

Regression for the Mean: Auto-Evaluation and Inference with Few Labels through Post-hoc Regression
by: Eyre, Benjamin, et al.
Published: (2024)

Privacy-aware Gaussian Process Regression
by: Tuo, Rui, et al.
Published: (2023)

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy
by: You, Zhichao, et al.
Published: (2025)

Parsing the Language of Expression: Enhancing Symbolic Regression with Domain-Aware Symbolic Priors
by: Huang, Sikai, et al.
Published: (2025)

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models
by: Li, Zeming, et al.
Published: (2025)

The Limits of Differential Privacy in Online Learning
by: Li, Bo, et al.
Published: (2024)

Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning
by: Chua, Lynn, et al.
Published: (2024)

Movable Antenna-Equipped UAV for Data Collection in Backscatter Sensor Networks: A Deep Reinforcement Learning-based Approach
by: Bai, Yu, et al.
Published: (2024)

On the Fairness of Privacy Protection: Measuring and Mitigating the Disparity of Group Privacy Risks for Differentially Private Machine Learning
by: Yang, Zhi, et al.
Published: (2025)

Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000
by: Huang, Xiaobo, et al.
Published: (2025)

Prior-Guided Symbolic Regression: Towards Scientific Consistency in Equation Discovery
by: Xiao, Jing, et al.
Published: (2026)

Efficient Conformal Prediction for Regression Models under Label Noise
by: Cohen, Yahav, et al.
Published: (2025)