Data Deletion for Linear Regression with Noisy SGD
Fuente:
arXiv
Saved in:
| Main Authors: | Xia, Zhangjie, Wang, Chi-Hua, Cheng, Guang |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Online Forgetting Process for Linear Regression Models
by: Li, Yuantong, et al.
Published: (2020)
by: Li, Yuantong, et al.
Published: (2020)
Advancing Retail Data Science: Comprehensive Evaluation of Synthetic Data
by: Xia, Yu, et al.
Published: (2024)
by: Xia, Yu, et al.
Published: (2024)
Scaling Laws of SignSGD in Linear Regression: When Does It Outperform SGD?
by: Kim, Jihwan, et al.
Published: (2026)
by: Kim, Jihwan, et al.
Published: (2026)
Privacy Auditing Synthetic Data Release through Local Likelihood Attacks
by: Ward, Joshua, et al.
Published: (2025)
by: Ward, Joshua, et al.
Published: (2025)
Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting
by: Ward, Joshua, et al.
Published: (2026)
by: Ward, Joshua, et al.
Published: (2026)
Data Plagiarism Index: Characterizing the Privacy Risk of Data-Copying in Tabular Generative Models
by: Ward, Joshua, et al.
Published: (2024)
by: Ward, Joshua, et al.
Published: (2024)
From One-Pass SGD to Data Reuse: Mini-Batch Scaling Laws in Sketched Linear Regression
by: Chen, Ziyan, et al.
Published: (2026)
by: Chen, Ziyan, et al.
Published: (2026)
A Simplified Analysis of SGD for Linear Regression with Weight Averaging
by: Meterez, Alexandru, et al.
Published: (2025)
by: Meterez, Alexandru, et al.
Published: (2025)
Improve Fidelity and Utility of Synthetic Credit Card Transaction Time Series from Data-centric Perspective
by: Hsieh, Din-Yin, et al.
Published: (2024)
by: Hsieh, Din-Yin, et al.
Published: (2024)
BadGD: A unified data-centric framework to identify gradient descent vulnerabilities
by: Wang, Chi-Hua, et al.
Published: (2024)
by: Wang, Chi-Hua, et al.
Published: (2024)
Statistical Inference for Linear Functionals of Online SGD in High-dimensional Linear Regression
by: Agrawalla, Bhavya, et al.
Published: (2023)
by: Agrawalla, Bhavya, et al.
Published: (2023)
Understanding SGD with Exponential Moving Average: A Case Study in Linear Regression
by: Li, Xuheng, et al.
Published: (2025)
by: Li, Xuheng, et al.
Published: (2025)
Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training
by: Sander, Tom, et al.
Published: (2024)
by: Sander, Tom, et al.
Published: (2024)
Towards High Supervised Learning Utility Training Data Generation: Data Pruning and Column Reordering
by: Kwok, Tung Sum Thomas, et al.
Published: (2025)
by: Kwok, Tung Sum Thomas, et al.
Published: (2025)
Synth-MIA: A Testbed for Auditing Privacy Leakage in Tabular Data Synthesis
by: Ward, Joshua, et al.
Published: (2025)
by: Ward, Joshua, et al.
Published: (2025)
RQP-SGD: Differential Private Machine Learning through Noisy SGD and Randomized Quantization
by: Feng, Ce, et al.
Published: (2024)
by: Feng, Ce, et al.
Published: (2024)
When Tables Leak: Attacking String Memorization in LLM-Based Tabular Data Generation
by: Ward, Joshua, et al.
Published: (2025)
by: Ward, Joshua, et al.
Published: (2025)
Discriminative Estimation of Total Variation Distance: A Fidelity Auditor for Generative Data
by: Tao, Lan, et al.
Published: (2024)
by: Tao, Lan, et al.
Published: (2024)
Convergent Privacy Loss of Noisy-SGD without Convexity and Smoothness
by: Chien, Eli, et al.
Published: (2024)
by: Chien, Eli, et al.
Published: (2024)
Noise is All You Need: Private Second-Order Convergence of Noisy SGD
by: Avdiukhin, Dmitrii, et al.
Published: (2024)
by: Avdiukhin, Dmitrii, et al.
Published: (2024)
Decentralized Sparse Linear Regression via Gradient-Tracking: Linear Convergence and Statistical Guarantees
by: Maros, Marie, et al.
Published: (2022)
by: Maros, Marie, et al.
Published: (2022)
Downstream Task-Oriented Generative Model Selections on Synthetic Data Training for Fraud Detection Models
by: Cheng, Yinan, et al.
Published: (2024)
by: Cheng, Yinan, et al.
Published: (2024)
Symbolic Regression on Sparse and Noisy Data with Gaussian Processes
by: Hsin, Junette, et al.
Published: (2023)
by: Hsin, Junette, et al.
Published: (2023)
GReaTER: Generate Realistic Tabular data after data Enhancement and Reduction
by: Kwok, Tung Sum Thomas, et al.
Published: (2025)
by: Kwok, Tung Sum Thomas, et al.
Published: (2025)
Exact Mean Square Linear Stability Analysis for SGD
by: Mulayoff, Rotem, et al.
Published: (2023)
by: Mulayoff, Rotem, et al.
Published: (2023)
RC-Mixup: A Data Augmentation Strategy against Noisy Data for Regression Tasks
by: Hwang, Seong-Hyeon, et al.
Published: (2024)
by: Hwang, Seong-Hyeon, et al.
Published: (2024)
Asymptotics of Linear Regression with Linearly Dependent Data
by: Moniri, Behrad, et al.
Published: (2024)
by: Moniri, Behrad, et al.
Published: (2024)
Localized Dynamics-Aware Domain Adaption for Off-Dynamics Offline Reinforcement Learning
by: Xia, Zhangjie, et al.
Published: (2026)
by: Xia, Zhangjie, et al.
Published: (2026)
Multiple Testing of Linear Forms for Noisy Matrix Completion
by: Ma, Wanteng, et al.
Published: (2023)
by: Ma, Wanteng, et al.
Published: (2023)
Heavy-Tailed Linear Bandits: Huber Regression with One-Pass Update
by: Wang, Jing, et al.
Published: (2025)
by: Wang, Jing, et al.
Published: (2025)
Ensembling Membership Inference Attacks Against Tabular Generative Models
by: Ward, Joshua, et al.
Published: (2025)
by: Ward, Joshua, et al.
Published: (2025)
Accelerating Single-Pass SGD for Generalized Linear Prediction
by: Chen, Qian, et al.
Published: (2026)
by: Chen, Qian, et al.
Published: (2026)
From Privacy to Generalization: Linear Max-Information Bounds for DP-SGD
by: Lampert, Christoph H., et al.
Published: (2026)
by: Lampert, Christoph H., et al.
Published: (2026)
On the Learning Dynamics of Two-layer Linear Networks with Label Noise SGD
by: Zhang, Tongcheng, et al.
Published: (2026)
by: Zhang, Tongcheng, et al.
Published: (2026)
DEREC-SIMPRO: unlock Language Model benefits to advance Synthesis in Data Clean Room
by: Kwok, Tung Sum Thomas, et al.
Published: (2024)
by: Kwok, Tung Sum Thomas, et al.
Published: (2024)
Delete My Account: Impact of Data Deletion on Machine Learning Classifiers
by: Dam, Tobias, et al.
Published: (2023)
by: Dam, Tobias, et al.
Published: (2023)
Active Learning for Graphs with Noisy Structures
by: Chi, Hongliang, et al.
Published: (2024)
by: Chi, Hongliang, et al.
Published: (2024)
From Continual Learning to SGD and Back: Better Rates for Continual Linear Models
by: Evron, Itay, et al.
Published: (2025)
by: Evron, Itay, et al.
Published: (2025)
Minibatch and Local SGD: Algorithmic Stability and Linear Speedup in Generalization
by: Lei, Yunwen, et al.
Published: (2023)
by: Lei, Yunwen, et al.
Published: (2023)
Estimating Generalization Performance Along the Trajectory of Proximal SGD in Robust Regression
by: Tan, Kai, et al.
Published: (2024)
by: Tan, Kai, et al.
Published: (2024)
Similar Items
-
Online Forgetting Process for Linear Regression Models
by: Li, Yuantong, et al.
Published: (2020) -
Advancing Retail Data Science: Comprehensive Evaluation of Synthetic Data
by: Xia, Yu, et al.
Published: (2024) -
Scaling Laws of SignSGD in Linear Regression: When Does It Outperform SGD?
by: Kim, Jihwan, et al.
Published: (2026) -
Privacy Auditing Synthetic Data Release through Local Likelihood Attacks
by: Ward, Joshua, et al.
Published: (2025) -
Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting
by: Ward, Joshua, et al.
Published: (2026)