Using a distributionally robust tail-loss objective in meta-learning improves both worst-case and average RMSE of an in-context transformer system identifier on synthetic and benchmark dynamics.
Optimization of conditional value- at-risk,
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Distributionally robust minimization in meta-learning for system identification
Using a distributionally robust tail-loss objective in meta-learning improves both worst-case and average RMSE of an in-context transformer system identifier on synthetic and benchmark dynamics.