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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.
Distribution-Free Inference for the Regression Function of Binary Classification
One of the key objects of binary classification is the regression function, i. e., the conditional expectation of the class labels given the inputs.
Robust Independence Tests with Finite Sample Guarantees for Synchronous Stochastic Linear Systems
The paper introduces robust independence tests with non-asymptotically guaranteed significance levels for stochastic linear time-invariant systems, assuming that the observed outputs are synchronous, which means that the systems are driven by jointly i. i. d.
Recursive Estimation of Conditional Kernel Mean Embeddings
In this paper we present a new recursive algorithm to estimate the conditional kernel mean map in a Hilbert space valued $L_2$ space, that is in a Bochner space.
Exact Distribution-Free Hypothesis Tests for the Regression Function of Binary Classification via Conditional Kernel Mean Embeddings
In this paper we suggest two statistical hypothesis tests for the regression function of binary classification based on conditional kernel mean embeddings.
Semi-Parametric Uncertainty Bounds for Binary Classification
The paper studies binary classification and aims at estimating the underlying regression function which is the conditional expectation of the class labels given the inputs.
