Efficient learning with robust gradient descent
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Minimizing the empirical risk is a popular training strategy, but for learning tasks where the data may be noisy or heavy-tailed, one may require many observations in order to generalize well. To achieve better performance under less stringent requirements, we introduce a procedure which constructs a robust approximation of the risk gradient for use in an iterative learning routine. We provide high-probability bounds on the excess risk of this algorithm, by showing that it does not deviate far from the ideal gradient-based update. Empirical tests show that in diverse settings, the proposed procedure can learn more efficiently, using less resources (iterations and observations) while generalizing better.
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