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Found 5 papers, 0 papers with code
Non-Euclidean Differentially Private Stochastic Convex Optimization: Optimal Rates in Linear Time
For $2 < p \leq \infty$, we show that existing linear-time constructions for the Euclidean setup attain a nearly optimal excess risk in the low-dimensional regime.
Learning from Mixtures of Private and Public Populations
Inspired by the above example, we consider a model in which the population $\mathcal{D}$ is a mixture of two sub-populations: a private sub-population $\mathcal{D}_{\sf priv}$ of private and sensitive data, and a public sub-population $\mathcal{D}_{\sf pub}$ of data with no privacy concerns.
Privately Answering Classification Queries in the Agnostic PAC Model
We formally study this problem in the agnostic PAC model and derive a new upper bound on the private sample complexity.
Heavy-Tailed Analogues of the Covariance Matrix for ICA
Like the current state-of-the-art, the new algorithm is based on the centroid body (a first moment analogue of the covariance matrix).
Heavy-tailed Independent Component Analysis
Independent component analysis (ICA) is the problem of efficiently recovering a matrix $A \in \mathbb{R}^{n\times n}$ from i. i. d.
