AIHT is an online sparse quantile regression method with adaptive thresholding that achieves logarithmic regret under restricted curvature and gradient-leakage conditions.
Journal of the Royal Statistical Society: Series B , volume =
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PhysEDA folds separable Manhattan-distance exponential decay into linear attention and potential-based rewards, cutting complexity to linear while improving zero-shot transfer and sparse-reward performance on decoupling-cap placement, macro placement, and IR-drop prediction.
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Adaptive Iterative Hard Thresholding for Online High-dimensional Quantile Regression
AIHT is an online sparse quantile regression method with adaptive thresholding that achieves logarithmic regret under restricted curvature and gradient-leakage conditions.
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PhysEDA: Physics-Aware Learning Framework for Efficient EDA With Manhattan Distance Decay
PhysEDA folds separable Manhattan-distance exponential decay into linear attention and potential-based rewards, cutting complexity to linear while improving zero-shot transfer and sparse-reward performance on decoupling-cap placement, macro placement, and IR-drop prediction.