Abstract:Recently Shah et al., 2020 pointed out the pitfalls of the simplicity bias-the tendency of gradientbased algorithms to learn simple models-which include the model's high sensitivity to small input perturbations, as well as sub-optimal margins. In particular, while Stochastic Gradient Descent yields max-margin boundary on linear models, such guarantee does not extend to nonlinear models. To mitigate the simplicity bias, we consider uncertainty-driven perturbations (UDP) of the training data points, obtained ite… Show more
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