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.

169,051 papers · 148 categories

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4 results for “DP-means”

Generalized Dirichlet-process-means for

f

-separable distortion measurescs.LG

Extends DP-means to handle outliers in $f$ -separable distortion measures.

problem Vulnerability to outliers and large maximum distortion in clusters.

method Extends DP-means to

f

-separable distortion measures and proposes a robust learning algorithm.

result Demonstrates improved performance in handling outliers and reducing maximum distortion.

DP-means outliers robustness f-separable distortion clustering

2019-01-31abs ↗pdf ↗

Private Mean Estimation with Person-Level Differential Privacycs.DS

Private mean estimation with multiple samples requires a certain number of people to maintain privacy.

problem Private mean estimation with person-level differential privacy for multiple samples.

method The approach involves estimating the mean up to a distance α in ℓ_2-norm under ε-differential privacy, using algorithms based on the clip-and-noise framework and new analyses.

result The necessary and sufficient number of people to estimate the mean up to distance α in ℓ_2-norm is given by a specific formula.

differential privacy mean estimation person-level multiple samples private statistics

2024-05-30abs ↗pdf ↗

Optimal Differentially Private Model Training with Public Datacs.LG

Optimal DP model training with public data improves privacy and accuracy.

problem Ensuring privacy while training models with public data.

method Proves optimal error rates for DP model training with public data, develops novel algorithms.

result Optimal error rates can be achieved by using public data or optimal DP algorithms.

differential privacy model training public data mean estimation empirical risk minimization

2023-06-26abs ↗pdf ↗

Public-data Assisted Private Stochastic Optimization: Power and Limitationscs.LG

Study public-data assisted private stochastic optimization with labeled or unlabeled public data.

problem Limits and capability of public-data assisted differentially private (PA-DP) algorithms in stochastic convex optimization.

method Lower bounds for PA-DP mean estimation and novel methods for leveraging public data in private supervised learning.

result Achieved dimension independent rate for GLM with unlabeled public data, showing optimality.

differential privacy stochastic convex optimization public data private stochastic optimization supervised learning

2024-03-06abs ↗pdf ↗