Balancing Utility and Privacy: Dynamically Private SGD with Random Projection | Paper Citation Record

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This paper cites Deep learning with differential privacy.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Deep learning with differential privacy

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Database-friendly random projections

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection cpsgd: Communication-efficient and differentially-private distributed sgd

Reference 3

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Private empirical risk minimization: Efficient algorithms and tight error bounds

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This paper cites A critical review on the use (and misuse) of differential privacy in machine learning.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection A critical review on the use (and misuse) of differential privacy in machine learning

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This paper cites The johnson-lindenstrauss transform itself preserves differential privacy.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection The johnson-lindenstrauss transform itself preserves differential privacy

Reference 6

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Optimization methods for large-scale machine learning

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This paper cites Automatic clipping: Differentially private deep learning made easier and stronger.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Automatic clipping: Differentially private deep learning made easier and stronger

Reference 8

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Model compression

Reference 9

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Ethical machine learning in healthcare

Reference 10

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This paper cites Understanding gradient clipping in private sgd: A geometric perspective.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Understanding gradient clipping in private sgd: A geometric perspective

Reference 11

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Robust quantization: One model to rule them all

Reference 12

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection A comprehensive survey on model compression and acceleration

Reference 13

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This paper cites Beyond uniform lipschitz condition in differentially private optimization.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Beyond uniform lipschitz condition in differentially private optimization

Reference 14

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection A concentration theorem for projections

Reference 15

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Dynamic Differential-Privacy Preserving SGD

Reference 16

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Differential privacy

Reference 17

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This paper cites Differential privacy: A survey of results.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Differential privacy: A survey of results

Reference 18

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection RQP-SGD: Differential Private Machine Learning through Noisy SGD and Randomized Quantization

Reference 19

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This paper cites Semi-supervised learning using deep generative models and auxiliary tasks.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Semi-supervised learning using deep generative models and auxiliary tasks

Reference 20

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This paper cites Differentially Private Next-Token Prediction of Large Language Models.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Differentially Private Next-Token Prediction of Large Language Models

Reference 21

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This paper cites Handbook of Convergence Theorems for (Stochastic) Gradient Methods.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Handbook of Convergence Theorems for (Stochastic) Gradient Methods

Reference 22

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This paper cites Stochastic first-and zeroth-order methods for nonconvex stochastic programming.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Stochastic first-and zeroth-order methods for nonconvex stochastic programming

Reference 23

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This paper cites Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis

Reference 24

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This paper cites Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance

Reference 25

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This paper cites Differential Privacy and Machine Learning: a Survey and Review.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Differential Privacy and Machine Learning: a Survey and Review

Reference 26

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This paper cites Extensions of lipschitz mappings into a hilbert space.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Extensions of lipschitz mappings into a hilbert space

Reference 27

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This paper cites Sgd with low-dimensional gradients with applications to private and distributed learning.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Sgd with low-dimensional gradients with applications to private and distributed learning

Reference 28

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This paper cites u chemann, Maria Bannert, Daryna Dementieva, Frank Fischer, Urs Gasser, Georg Groh, Stephan G \.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection u chemann, Maria Bannert, Daryna Dementieva, Frank Fischer, Urs Gasser, Georg Groh, Stephan G \

Reference 29

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This paper cites Revisiting gradient clipping: Stochastic bias and tight convergence guarantees.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Revisiting gradient clipping: Stochastic bias and tight convergence guarantees

Reference 30

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This paper cites Gradient descent with linearly correlated noise: Theory and applications to differential privacy.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Gradient descent with linearly correlated noise: Theory and applications to differential privacy

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This paper cites Optimality of the johnson-lindenstrauss lemma.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Optimality of the johnson-lindenstrauss lemma

Reference 32

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Convergence and privacy of decentralized nonconvex optimization with gradient clipping and communication compression

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Differentially Private Language Models for Secure Data Sharing

Reference 34

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Efficient deep learning: A survey on making deep learning models smaller, faster, and better

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Differentially private model compression

Reference 36

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection A survey of regularization strategies for deep models

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Random Projection and Its Applications

Reference 38

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Explicit regularization in overparametrized models via noise injection

Reference 39

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection How to dp-fy ml: A practical guide to machine learning with differential privacy

Reference 40

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Enabling fast differentially private sgd via just-in-time compilation and vectorization

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Large language models in medicine

Reference 42

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This paper cites On Differentially Private Subspace Estimation in a Distribution-Free Setting.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection On Differentially Private Subspace Estimation in a Distribution-Free Setting

Reference 43

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Resnets ensemble via the feynman-kac formalism to improve natural and robust accuracies

Reference 44

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Private model compression via knowledge distillation

Reference 45

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Protect privacy from gradient leakage attack in federated learning

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Differentially private sgd with non-smooth losses

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This paper cites A theory to instruct differentially-private learning via clipping bias reduction.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection A theory to instruct differentially-private learning via clipping bias reduction

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Robust regression and lasso

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This paper cites Opacus: User-Friendly Differential Privacy Library in PyTorch.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Opacus: User-Friendly Differential Privacy Library in PyTorch

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Differentially Private Fine-tuning of Language Models

Reference 51

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This paper cites Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection iDLG: Improved Deep Leakage from Gradients

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Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification

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This paper cites write newline.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection write newline

Reference 55

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