A non-parametric rehearsal learning framework using conditional mean embeddings and a Probit surrogate for avoiding undesired outcomes, with consistency guarantees.
Since 0<Φ(·)<1, the product is bounded by any single factor: ∆(y) = lY k=1 Φ(ηhk(y))≤Φ(ηh j(y))≤Φ(−ηϵ)
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Non-Parametric Rehearsal Learning via Conditional Mean Embeddings
A non-parametric rehearsal learning framework using conditional mean embeddings and a Probit surrogate for avoiding undesired outcomes, with consistency guarantees.