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On Rate-Optimal Partitioning Classification from Observable and from Privatised Data
Here, we study the problem under much milder assumptions: in addition to the standard Lipschitz and margin conditions, a novel characteristic of the absolutely continuous component is introduced, by which the exact convergence rate of the classification error probability is calculated, both for the binary and for the multi-label cases.
Lossless Transformations and Excess Risk Bounds in Statistical Inference
After characterizing lossless transformations, i. e., transformations for which the excess risk is zero for all loss functions, we construct a partitioning test statistic for the hypothesis that a given transformation is lossless and show that for i. i. d.
Repeated Observations for Classification
We study the problem nonparametric classification with repeated observations.
Strongly universally consistent nonparametric regression and classification with privatised data
In this paper we revisit the classical problem of nonparametric regression, but impose local differential privacy constraints.
