A new optimistic online mirror descent variant uses a post-hoc penalty to allow learning rates up to Θ(T) while bounding cumulative penalty at O(log T), achieving near-optimal dynamic regret and faster adaptation on non-stationary data.
Proceedings of the 22Nd International Conference on Machine Learning , series =
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Weighted rules extend stable model semantics to support probabilistic reasoning, model ranking, and statistical inference in answer set programs.
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Weighted Rules under the Stable Model Semantics
Weighted rules extend stable model semantics to support probabilistic reasoning, model ranking, and statistical inference in answer set programs.