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View article: Stability is Stable: Connections between Replicability, Privacy, and Adaptive Generalization
Stability is Stable: Connections between Replicability, Privacy, and Adaptive Generalization Open
The notion of replicable algorithms was introduced in Impagliazzo et al. [STOC '22] to describe randomized algorithms that are stable under the resampling of their inputs. More precisely, a replicable algorithm gives the same output with h…
View article: Reproducibility in learning
Reproducibility in learning Open
We introduce the notion of a reproducible algorithm in the context of learning. A reproducible learning algorithm is resilient to variations in its samples — with high probability, it returns the exact same output when run on two samples f…
View article: Reproducibility in Learning
Reproducibility in Learning Open
We introduce the notion of a reproducible algorithm in the context of learning. A reproducible learning algorithm is resilient to variations in its samples -- with high probability, it returns the exact same output when run on two samples …
View article: Boosting in the Presence of Massart Noise
Boosting in the Presence of Massart Noise Open
We study the problem of boosting the accuracy of a weak learner in the (distribution-independent) PAC model with Massart noise. In the Massart noise model, the label of each example $x$ is independently misclassified with probability $η(x)…