Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences

NeurIPS 2016 Hongseok NamkoongJohn C. Duchi

We develop efficient solution methods for a robust empirical risk minimization problem designed to give calibrated confidence intervals on performance and provide optimal tradeoffs between bias and variance. Our methods apply to distributionally robust optimization problems proposed by Ben-Tal et al., which put more weight on observations inducing high loss via a worst-case approach over a non-parametric uncertainty set on the underlying data distribution... (read more)

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