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stat.ML2020
Minimum discrepancy principle strategy for choosing in -NN regression
Yaroslav Averyanov, Alain Celisse
We present a novel data-driven strategy to choose the hyperparameter in the -NN regression estimator without using any hold-out data. We treat the problem of choosing the hy…
stat.ML2020
Early stopping and polynomial smoothing in regression with reproducing kernels
Yaroslav Averyanov, Alain Celisse
In this paper, we study the problem of early stopping for iterative learning algorithms in a reproducing kernel Hilbert space (RKHS) in the nonparametric regression framework. In p…
stat.ML2016★ 12 cited
Stability revisited: new generalisation bounds for the Leave-one-Out
Alain Celisse, Benjamin Guedj
The present paper provides a new generic strategy leading to non-asymptotic theoretical guarantees on the Leave-one-Out procedure applied to a broad class of learning algorithms. T…