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Found 4 papers, 0 papers with code
Weakly Supervised Learners for Correction of AI Errors with Provable Performance Guarantees
A key technical focus of the work is in providing performance guarantees for these new AI correctors through bounds on the probabilities of incorrect decisions.
Relative intrinsic dimensionality is intrinsic to learning
High dimensional data can have a surprising property: pairs of data points may be easily separated from each other, or even from arbitrary subsets, with high probability using just simple linear classifiers.
How adversarial attacks can disrupt seemingly stable accurate classifiers
We introduce a simple generic and generalisable framework for which key behaviours observed in practical systems arise with high probability -- notably the simultaneous susceptibility of the (otherwise accurate) model to easily constructed adversarial attacks, and robustness to random perturbations of the input data.
Towards a mathematical understanding of learning from few examples with nonlinear feature maps
We consider the problem of data classification where the training set consists of just a few data points.
