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Learning with risks based on M-location
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DOI：10.1007/s10994-022-06217-5.png)
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Abstract
En 中文
In this work, we study a new class of risks defined in terms of the location and deviation of the loss distribution, generalizing far beyond classical mean-variance risk functions. The class is easily implemented as a wrapper around any smooth loss, it admits finite-sample stationarity guarantees for stochastic gradient methods, it is straightforward to interpret and adjust, with close links to M-estimators of the loss location, and has a salient effect on the test loss distribution, giving us control over symmetry and deviations that are not possible under naive ERM.
Keywords:
Risk-sensitive learning
Non-convex non-smooth risk minimization
Stochastic optimization