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This paper derives a closed-form upper bound for admissible learning-rate steps in belief-space dynamics using KL divergence and Bregman geometry, focusing on cross-entropy classification.
This paper presents a closed-form upper bound for admissible learning-rate steps in belief-space dynamics, providing a theoretical result for optimization in robotics or control.