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ASK, a kernel-based steering method that learns from elicited pairwise judgments without accessing teacher embeddings or collecting new human similarity annotations, is introduced, a kernel-based steering method that learns from elicited pairwise judgments without accessing teacher embeddings or collecting new human si...
The concavity of the matrix-based conditional entropy functional is proved, which makes the resulting entropy-constrained projection a convex optimization problem, and a scalable mirror-descent algorithm for its implementation is developed.
These results suggest that in diversity-aware multi-armed bandits, e.g., for generative model selection, exploration can arise intrinsically from the objective's geometry, particularly for widely used metrics such as FID and Vendi where tight confidence bounds are difficult to construct.
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