arXiv Machine Learning
Oct 19
Nearly Linear Private Learning of Littlestone Classes
arXiv:2610. 23160v1 Announce Type: new Abstract: Differentially private learning aims to learn from data while protecting individual training examples.
Near-Optimal List Replicability from Littlestone Dimension
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Adversarially Robust PAC Learning with Optimal VC Rates
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Scale-Sensitive Shattering: Learnability and Evaluability at Optimal Scale
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arXiv Machine Learning
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On the Gradient Complexity of Private Optimization with Private Oracles
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Optimistic Rates for Multiclass PAC Learning
arXiv:2608. 10869v1 Announce Type: new Abstract: Worst-case multiclass bounds do not become smaller when the best classifier is already nearly correct: what is missing is an optimistic rate, a guarantee whose fluctuation scales with the oracle risk itself.
arXiv Machine Learning
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Quadratic Sample Complexity for Strictly Replicable Realizable Learning with Unknown Marginals
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arXiv Machine Learning
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On Randomized Algorithms in Online Strategic Classification
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arXiv Machine Learning
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An Agnostic Sample Compression Scheme for Squared Loss of Near-Linear Size in the Fat-Shattering Dimension
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