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non-convex optimization

topic1 events
papersSEP 10 04:00 UTC

Settling: Equilibrium Inference for Non-Convex Validity Sets

A new arXiv paper addresses learning systems that must output a single prediction even when the set of acceptable outputs is disconnected or non-convex. The authors show that with squared loss, the Bayes-optimal conditional mean can fall outside the valid output region when the underlying distribution is ambiguous. They propose an equilibrium-based 'settling' procedure that returns a point estimate guaranteed to lie within the validity set.

research/machine-learning/inference/theory