On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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48 results for “private linear regression”

Private Sketches for Linear Regressioncs.LG

Private sketches protect linear regression data privacy.

problem Protecting sensitive information in linear regression.

method Release private sketches of datasets, compute approximate solutions.

result Private sketches maintain good approximation guarantees to the original problem.

differential privacy linear regression sketching private computation approximate solutions

2025-11-10abs ↗pdf ↗

Private Regression via Data-Dependent Sufficient Statistic Perturbationcs.LG

Paper introduces data-dependent SSP for private linear and logistic regression.

problem Private linear and logistic regression with better performance.

method Data-dependent sufficient statistic perturbation (SSP) for linear and logistic regression.

result Data-dependent SSP outperforms state-of-the-art methods for linear and logistic regression.

differential privacy private regression sufficient statistics logistic regression synthetic data

2024-05-23abs ↗pdf ↗

Differentially Private Regression with Unbounded Covariatescs.CR

Efficiently private regression for unbounded data.

problem Privacy constraints in regression settings with unbounded covariates.

method Differential privacy techniques on mean and covariance estimation extended to sub-gaussian regime.

result Unbiased estimate of true regression vector learned up to a scaling factor.

differential privacy regression unbounded covariates binary regression linear regression

2022-02-19abs ↗pdf ↗

Differentially Private Distributed Bayesian Linear Regression with MCMCstat.ML

A novel Bayesian framework for private linear regression with MCMC.

problem Private linear regression in a distributed setting.

method Generative statistical model, MCMC algorithms, fast Bayesian estimation.

result The proposed methods provide well-rounded estimation and prediction.

differential privacy distributed learning Bayesian inference MCMC linear regression

2023-01-31abs ↗pdf ↗

Beyond Covariance Matrix: The Statistical Complexity of Private Linear Regressioncs.LG

New method for private linear regression under privacy constraints, achieving optimal rates.

problem Statistical complexity of private linear regression under unknown, ill-conditioned covariates.

method Information-Weighted Regression method

result Optimal convergence rates for both central and local privacy models.

private linear regression covariate distribution minimax rates L1 analogues regret bounds

2025-02-18abs ↗pdf ↗

Differentially Private Inference for Longitudinal Linear Regressionmath.ST

Develops privacy-preserving methods for longitudinal linear regression.

problem Protecting individual information in longitudinal data with privacy-preserving statistics.

method Proposes a user-level private regression estimator and a privatized covariance estimator for longitudinal linear regression under user-level differential privacy.

result Establishes theoretical guarantees for practical user-level differential privacy estimation and inference in longitudinal linear regression.

differential privacy longitudinal data linear regression user-level privacy covariance estimation

2026-01-15abs ↗pdf ↗

Differentially Private Bayesian Inference for Generalized Linear Modelscs.LG

New method for GLMs under DP provides private uncertainty quantification.

problem Private inference for GLMs with uncertainty quantification.

method Noise-aware DP Bayesian inference method for GLMs.

result Posterior uncertainty allows determination of statistically significant coefficients.

differential privacy Bayesian inference generalized linear models logistic regression Poisson regression

2020-11-01abs ↗pdf ↗

Differentially Private Bayesian Linear Regressioncs.LG

Linear regression is an important tool across many fields that work with sensitive human-sourced data. Significant prior work has focused on producing differentially private point estimates, which provide a privacy guarantee to individuals while still allowing modelers to draw insights from data by estimating regressio…

2019-10-29abs ↗pdf ↗

Enhancing Differentially Private Linear Regression via Public Second-Momentcs.LG

Enhances DP linear regression using public data moments.

problem Limited utility of traditional DP methods in linear regression.

method Transform private data using public second-moment matrix for a better OLSE.

result Improved accuracy and robustness of OLSE in DP linear regression.

differential privacy linear regression public data second-moment sufficient statistics

2025-08-25abs ↗pdf ↗

Differentially Private Linear Regression and Synthetic Data Generation with Statistical Guaranteescs.LG

Proposes a method for differentially private linear regression and synthetic data generation.

problem Lack of valid inference and synthetic data generation methods for small-scale datasets in privacy-aware settings.

method Gaussian differentially private linear regression with bias-corrected estimator and SDG procedure.

result Improves accuracy and provides valid confidence intervals for downstream tasks.

differential privacy linear regression synthetic data statistical guarantees small-scale datasets

2025-10-19abs ↗pdf ↗

Near Optimal Private and Robust Linear Regressioncs.LG

Improved linear regression with privacy and robustness guarantees.

problem Private and robust linear regression with adversarial corruption.

method Differentially private stochastic gradient descent with full-batch gradient descent and adaptive clipping.

result Near optimal sample complexity for both private and robust linear regression.

differential privacy robust regression linear regression stochastic gradient descent adaptive clipping

2023-01-30abs ↗pdf ↗

On the Optimal Regret of Locally Private Linear Contextual Banditstat.ML

This paper achieves optimal regret bounds for locally private linear contextual bandit.

problem Designing locally private linear contextual bandit algorithms with optimal regret bounds.

method New algorithmic and analytical ideas, including mean absolute deviation analysis and layered principal component regression.

result Achieves an

ilde O(\sqrt{T})

regret upper bound for locally private linear contextual bandit.

locally private linear contextual bandit regret bounds mean absolute deviation principal component regression

2024-04-15abs ↗pdf ↗

Testing Credibility of Public and Private Surveys through the Lens of Regressioncs.LG

The paper tests the credibility of public and private surveys using linear regression and differential privacy.

problem Ensuring the validity of data analysis results from sample surveys using linear regression.

method Designing an algorithm to test the credibility of surveys and extending it to handle LDP.

result The algorithm achieves optimal estimation error bound for

\ell_1

linear regression and reduces sample complexity.

credibility testing linear regression differential privacy sample surveys data analysis

2024-10-07abs ↗pdf ↗

Better Locally Private Sparse Estimation Given Multiple Samples Per Userstat.ML

Improved locally private sparse estimation with multiple samples per user.

problem Challenges in high-dimensional locally private sparse estimation.

method Proposes a framework for user-level locally private sparse linear regression with multiple samples per user.

result Eliminates the dependency of dimensionality on error bounds, achieving tighter error bounds.

Locally private estimation Sparse estimation Multiple samples User-level Differential privacy

2024-08-08abs ↗pdf ↗

How Private is Your Attention? Bridging Privacy with In-Context Learningstat.ML

Paper explores privacy in machine learning models using ICL.

problem Privacy in in-context learning models.

method Differentially private pretraining algorithm for linear attention heads.

result Characterizes privacy-accuracy trade-off for ICL in linear regression.

privacy in-context learning transformer models differential privacy linear regression

2025-04-22abs ↗pdf ↗

Insufficient Statistics Perturbation: Stable Estimators for Private Least Squarescs.LG

Efficiently estimates private least squares with linear error growth.

problem Private estimation of ordinary least squares with bounded residuals and leverage.

method Scaled noise added to a stable nonprivate estimator of the regression vector.

result Near-optimal accuracy guarantee with linear error growth in dimension.

differential privacy least squares private estimation statistical leverage empirical regression vector

2024-04-23abs ↗pdf ↗

A Knowledge Transfer Framework for Differentially Private Sparse Learningstat.ML

We study the problem of estimating high dimensional models with underlying sparse structures while preserving the privacy of each training example. We develop a differentially private high-dimensional sparse learning framework using the idea of knowledge transfer. More specifically, we propose to distill the knowledge …

2019-09-13abs ↗pdf ↗

Parametric Bootstrap for Differentially Private Confidence Intervalscs.LG

Develops a simple method for creating private confidence intervals.

problem Creating private confidence intervals for parametric estimation.

method Parametric bootstrap approach to construct confidence intervals.

result The parametric bootstrap provides consistent and effective confidence intervals.

differential privacy confidence intervals parametric bootstrap private estimation linear regression

2020-06-14abs ↗pdf ↗

Differentially Private Geodesic Regressionstat.ML

Differentially private geodesic regression for non-Euclidean data.

problem Protecting sensitive data on non-linear spaces like manifolds.

method K-Norm Gradient (KNG) mechanism for Riemannian manifolds.

result Theoretical bounds for sensitivity of geodesic regression parameters.

differential privacy geodesic regression Riemannian manifolds sensitivity analysis Fréchet mean

2025-04-15abs ↗pdf ↗

Nearly Optimal Differentially Private ReLU Regressioncs.LG

Paper optimizes private ReLU regression with relaxed assumptions.

problem Private ReLU regression with relaxed assumptions.

method One-pass mini-batch Generalized Linear Model Perceptron algorithm (DP-MBGLMtron).

result Achieves optimal utility bound up to logarithmic factors.

Differential Privacy ReLU Regression Private Learning Generalized Linear Model Mini-Batch Algorithm

2025-03-08abs ↗pdf ↗

Differentially Private

\ell_1

-norm Linear Regression with Heavy-tailed Datacs.LG

Study differentially private linear regression with heavy-tailed data.

problem Differentially private

\ell_1

-norm linear regression with heavy-tailed data.

method Exponential mechanism for

\ell_2

-norm bounded second moment; relaxation to

\ell_2

-norm bounded

θ

-th moment; coordinate-wise bounded moments.

result Achieved upper bounds for privacy-preserving linear regression under various moment conditions.

Differential Privacy Linear Regression Heavy-tailed Data Exponential Mechanism Moment Conditions

2022-01-10abs ↗pdf ↗

Differentially Private Simple Linear Regressioncs.LG

Differential privacy for simple linear regression protects small datasets from individual data leaks.

problem Protecting sensitive personal information in small datasets from individual data leaks.

method Differential privacy algorithms for simple linear regression tailored for small datasets (tens to hundreds of datapoints).

result Robust estimators like Theil-Sen perform well on small datasets, but standard algorithms improve as dataset size increases.

differential privacy simple linear regression small datasets small-area analysis economic research

2020-07-10abs ↗pdf ↗

Distributionally-Robust Machine Learning Using Locally Differentially-Private Datacs.LG

New regularizer for machine learning using private data.

problem Machine learning with private data.

method Distributionally-robust optimization with locally-differentially-private datasets.

result New regularizer for training linear regression models.

machine learning privacy differential privacy distributionally-robust optimization regularization

2020-06-24abs ↗pdf ↗

DPpack: An R Package for Differentially Private Statistical Analysis and Machine Learningstat.ML

DPpack offers R tools for private data analysis and machine learning.

problem Ensuring privacy in statistical analysis and machine learning.

method Differential privacy mechanisms (Laplace, Gaussian, exponential).

result User-friendly implementation of privacy-preserving models.

differential privacy R package statistical analysis machine learning privacy-preserving

2023-09-19abs ↗pdf ↗

Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterationscs.LG

Improved Frank-Wolfe algorithm speeds up training of differentially private LASSO models.

problem Training differentially private LASSO models on sparse data.

method Adapted Frank-Wolfe algorithm for sparse inputs, reducing runtime.

result Training time reduced from

\mathcal{O}(TDS + TNs)

\mathcal{O}(N S + T \sqrt{D} \log{D} + TS^2)

Frank-Wolfe algorithm differential privacy LASSO logistic regression sparse data

2023-10-30abs ↗pdf ↗

Private Isotonic Regressioncs.LG

Paper develops DP algorithms for isotonic regression over posets.

problem Differential privacy in isotonic regression over partially ordered sets.

method Developed pure-DP and near-matching lower bound algorithms for isotonic regression.

result Achieved near-matching bounds for isotonic regression with and without poset structure.

Differential Privacy Isotonic Regression Partially Ordered Sets Lower Bounds Approximate-DP

2022-10-27abs ↗pdf ↗

(Nearly) Optimal Private Linear Regression via Adaptive Clippingcs.LG

New method improves privacy in linear regression with optimal error bounds.

problem Differentially private linear regression with suboptimal error bounds.

method One-pass mini-batch stochastic gradient descent (DP-AMBSSGD) with adaptive clipping.

result Nearly optimal error bounds in terms of key parameters like dimensionality, number of points, and noise standard deviation.

differential privacy linear regression stochastic gradient descent adaptive clipping sub-Gaussian distribution

2022-07-11abs ↗pdf ↗

On the Equivalence between Online and Private Learnability beyond Binary Classificationstat.ML

Study extends learnability equivalence to multi-class and regression, overcoming binary classification limits.

problem Equivalence of online and private learnability in multi-class and regression settings.

method Introduced a novel Littlestone dimension variant and threshold functions for multi-class classification.

result Online learnability implies private learnability in multi-class classification but not in regression.

online learnability private learnability multi-class classification regression Littlestone dimension

2020-06-02abs ↗pdf ↗

Private Learning with Public Featurescs.LG

New private learning algorithms improve utility in tasks with public features.

problem Private learning with public features in recommendation and ad prediction.

method Developed algorithms that protect only certain sufficient statistics, improving utility for linear regression and private recommendation benchmarks.

result Achieved state-of-the-art performance on private recommendation benchmarks.

private learning public features recommendation ad prediction sufficient statistics

2023-10-24abs ↗pdf ↗

Near-Optimal Private Linear Regression via Iterative Hessian Mixingcs.LG

Improved privacy-preserving linear regression via iterative Hessian mixing.

problem Differentially private linear regression with improved accuracy and efficiency.

method Iterative Hessian Mixing (IHM) for differentially private ordinary least squares (DP-OLS).

result IHM provides better utility guarantees and outperforms AdaSSP in empirical evaluations.

Differential Privacy Linear Regression Iterative Hessian Sketch Ordinary Least Squares Sketching Approaches

2026-01-12abs ↗pdf ↗

Differentially Private Sliced Inverse Regression: Minimax Optimality and Algorithmstat.ML

The paper proposes differentially private sliced inverse regression algorithms for high-dimensional data.

problem Privacy concerns in high-dimensional data analysis.

method Differentially private sliced inverse regression algorithms designed for privacy preservation.

result Achieves minimax lower bounds up to logarithmic factors.

differential privacy sliced inverse regression dimension reduction minimax optimality privacy-preserving algorithms

2024-01-16abs ↗pdf ↗

Majority Vote for Distributed Differentially Private Sign Selectioncs.CR

Proposes a privacy-preserving sign selection method for distributed systems.

problem Sign selection in distributed differentially private settings.

method Iterative peeling of stability function combined with exponential mechanism.

result Recovery of support and signs with optimal signal-to-noise ratio.

privacy-preserving differential privacy sign selection distributed systems iterative peeling

2022-09-08abs ↗pdf ↗

Differentially Private (Gradient) Expectation Maximization Algorithm with Statistical Guaranteescs.LG

New DP EM algorithm with statistical guarantees for mixture models.

problem Preserving privacy in EM algorithms for mixture models.

method Proposed a DP EM algorithm with statistical guarantees.

result Near optimal estimation error for GMM in DP model.

Differentially Private Expectation Maximization Mixture Models Statistical Guarantees Privacy Preserving Algorithms

2020-10-22abs ↗pdf ↗

Financial Variables Effect on the U.S. Gross Private Domestic Investment (GPDI) 1959-2001q-fin.GN

I studied what role the US stock markets and money markets have possibly played in the Gross Private Domestic Investment (GPDI) of the United States from the year 1959 to the year 2001, Gross Private Domestic Investment refers to the total amount of investment spending by businesses and firms located within the borders…

2007-12-13abs ↗pdf ↗

Differentially Private Nonparametric Regression Under a Growth Conditioncs.LG

New algorithm learns regression models privately under growth condition.

problem Private learning of nonparametric regression models.

method Novel filtering procedure to output stable hypotheses for nonparametric function classes.

result Established first nonparametric private learnability guarantee for diverging fat shattering dimensions.

differential privacy nonparametric regression fat shattering dimension private learning stable hypotheses

2021-11-24abs ↗pdf ↗

A Framework for Private Matrix Analysiscs.LG

Efficient private matrix analysis algorithms for recent variants.

problem Private analysis of recent matrix updates.

method Identifying sufficient conditions on positive semidefinite matrices.

result First efficient differentially private algorithms for various matrix analysis tasks.

differential privacy matrix analysis principal component analysis sliding window model sparse principal component analysis

2020-09-06abs ↗pdf ↗

Private Incremental Regressioncs.DS

Data is continuously generated by modern data sources, and a recent challenge in machine learning has been to develop techniques that perform well in an incremental (streaming) setting. In this paper, we investigate the problem of private machine learning, where as common in practice, the data is not given at once, but…

2017-01-04abs ↗pdf ↗

CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learningcs.LG

How to train a machine learning model while keeping the data private and secure? We present CodedPrivateML, a fast and scalable approach to this critical problem. CodedPrivateML keeps both the data and the model information-theoretically private, while allowing efficient parallelization of training across distributed w…

2019-02-02abs ↗pdf ↗

High-Dimensional Differentially Private Quantile Regression: Distributed Estimation and Statistical Inferencestat.ML

Paper proposes differentially private quantile regression for high-dimensional data.

problem Privacy concerns in big data with heterogeneous sensitive personal information.

method Newton-type transformation for reformulating quantile regression into an OLS problem; iterative updates for estimation; debiased estimator for inference; communication-efficient bootstrap.

result Near-optimal statistical accuracy and formal privacy guarantees achieved.

differential privacy quantile regression high-dimensional data distributed estimation statistical inference

2025-08-07abs ↗pdf ↗

Differentially Private Online Learningcs.LG

In this paper, we consider the problem of preserving privacy in the online learning setting. We study the problem in the online convex programming (OCP) framework---a popular online learning setting with several interesting theoretical and practical implications---while using differential privacy as the formal privacy …

2011-09-01abs ↗pdf ↗

Differentially Private Two-Stage Gradient Descent for Instrumental Variable Regressionstat.ML

New algorithm protects privacy in IVaR regression while maintaining accuracy.

problem Privacy leakage in classical IVaR methods.

method Noisy two-stage gradient descent with differential privacy guarantees.

result Achieves statistical efficiency and privacy in IVaR regression.

differential privacy instrumental variable regression gradient descent two-stage privacy guarantees

2025-09-26abs ↗pdf ↗

High-Dimensional Private Linear Regression with Optimal Ratesstat.ML

Optimal DP-GD achieves high-dimensional linear regression risk.

problem Differentially private linear regression in high-dimensional settings.

method One-pass DP gradient descent (DP-GD) with gradient clipping and decaying learning rate.

result Achieves optimal non-asymptotic risk of

O(γ+ γ^2 / ρ^2)

for well-conditioned data.

Differential Privacy Linear Regression Gradient Descent Privacy-Preserving Algorithms High-Dimensional Data

2025-05-22abs ↗pdf ↗

The Cost of Privacy: Optimal Rates of Convergence for Parameter Estimation with Differential Privacystat.ML

Privacy-preserving data analysis is a rising challenge in contemporary statistics, as the privacy guarantees of statistical methods are often achieved at the expense of accuracy. In this paper, we investigate the tradeoff between statistical accuracy and privacy in mean estimation and linear regression, under both the …

2019-02-12abs ↗pdf ↗

Differentially Private Domain Adaptation with Theoretical Guaranteescs.LG

Two private algorithms improve domain adaptation with privacy guarantees.

problem Improving predictions for a private target domain using public data.

method Two

(ε, δ)

-differentially private algorithms for supervised domain adaptation.

result Private algorithms maintain performance close to non-private versions.

differential privacy domain adaptation supervised learning private data public data

2023-06-15abs ↗pdf ↗

Secure Approximation Guarantee for Cryptographically Private Empirical Risk Minimizationstat.ML

Privacy concern has been increasingly important in many machine learning (ML) problems. We study empirical risk minimization (ERM) problems under secure multi-party computation (MPC) frameworks. Main technical tools for MPC have been developed based on cryptography. One of limitations in current cryptographically priva…

2016-02-15abs ↗pdf ↗

Differentially Private Federated Learning: Servers Trustworthiness, Estimation, and Statistical Inferencestat.ML

Paper explores differential privacy in high-dimensional federated learning, tackling server trustworthiness and estimation.

problem Maintaining privacy in distributed environments with high-dimensional data.

method Investigates scenarios with untrusted and trusted central servers, introduces novel federated estimation algorithms for linear regression models.

result Tight minimax rates depend on high-dimensionality even with sparsity assumptions, and novel algorithms handle slight variations among distributed models.

differential privacy federated learning high-dimensional estimation statistical inference server trustworthiness

2024-04-25abs ↗pdf ↗

Locally Private Online Quantile Regression: Estimation and Inferencestat.ML

Locally private online quantile regression method addresses privacy constraints.

problem Estimating and inferring quantile regression under local differential privacy constraints.

method Developed a finite-alphabet channel where users compute local contributions, apply randomized response, and send reports. A public decoder corrects distortion and reconstructs inputs for averaging.

result Established local privacy, decoder unbiasedness, consistency, asymptotic normality, and inference for scalar contrasts.

quantile regression local differential privacy online learning privacy-preserving stochastic quantization

2026-07-06abs ↗pdf ↗

From Robustness to Privacy and Backcs.LG

Transforms robust algorithms into private ones with optimal error rates.

problem Balancing privacy and robustness in machine learning.

method Black-box transformation method to convert robust algorithms to private ones with optimal error rates.

result Optimal private estimators for various tasks, including Gaussian and PCA.

differential privacy robustness machine learning algorithm transformation statistical inference

2023-02-03abs ↗pdf ↗