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Lower Bounds for Private Estimation of Gaussian Covariance Matrices under All Reasonable Parameter Regimes
We prove lower bounds on the number of samples needed to privately estimate the covariance matrix of a Gaussian distribution.
Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses
We extend the known regret bounds for classical OCO algorithms to the relative setting.
Online mirror descent and dual averaging: keeping pace in the dynamic case
Online mirror descent (OMD) and dual averaging (DA) -- two fundamental algorithms for online convex optimization -- are known to have very similar (and sometimes identical) performance guarantees when used with a fixed learning rate.
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