Introduces hybrid noise and novel coupling analysis to achieve the first convergent hidden-state DP bound for zeroth-order optimization.
A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
abstract
In this note, we derive concentration inequalities for random vectors with subGaussian norm (a generalization of both subGaussian random vectors and norm bounded random vectors), which are tight up to logarithmic factors.
years
2025 3verdicts
UNVERDICTED 3representative citing papers
Decentralized SGD achieves high-probability convergence with order-optimal rates and linear speedup in the number of users under standard smoothness and convexity conditions on the cost function.
GL-LowPopArt is a Catoni-style two-stage estimator for generalized low-rank trace regression that attains state-of-the-art bounds and nearly instance-wise minimax optimality up to the Hessian condition number.
citing papers explorer
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Privacy Amplification in Differentially Private Zeroth-Order Optimization with Hidden States
Introduces hybrid noise and novel coupling analysis to achieve the first convergent hidden-state DP bound for zeroth-order optimization.
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High-probability Convergence Guarantees of Decentralized SGD
Decentralized SGD achieves high-probability convergence with order-optimal rates and linear speedup in the number of users under standard smoothness and convexity conditions on the cost function.
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GL-LowPopArt: A Nearly Instance-Wise Minimax-Optimal Estimator for Generalized Low-Rank Trace Regression
GL-LowPopArt is a Catoni-style two-stage estimator for generalized low-rank trace regression that attains state-of-the-art bounds and nearly instance-wise minimax optimality up to the Hessian condition number.