Statistical Linkage Learning enables a new mask construction algorithm for Partition Crossover that maintains effectiveness on noisy problems with hidden dependencies and matches noise-free performance when decomposition quality is high.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
LyMPuS is a parameterless on-the-fly surrogate enabling perfect monotonic comparisons and guaranteed linkage discovery in at most 2 log n steps for non-linear problems.
Composing a policy that maps 2D waypoints to joint actions with a frozen world model yields a lifted world model that achieves 3.8 times lower mean joint error than direct low-level search while being more compute-efficient and generalizing to unseen environments.
Two-step pessimistic Virtual Gap Analysis via linear programming ranks alternatives with cardinal and ordinal data to identify and remove the weakest option.
citing papers explorer
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Obtaining Partition Crossover masks using Statistical Linkage Learning for solving noised optimization problems with hidden variable dependency structure
Statistical Linkage Learning enables a new mask construction algorithm for Partition Crossover that maintains effectiveness on noisy problems with hidden dependencies and matches noise-free performance when decomposition quality is high.
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Limited Perfect Monotonical Surrogates constructed using low-cost recursive linkage discovery with guaranteed output
LyMPuS is a parameterless on-the-fly surrogate enabling perfect monotonic comparisons and guaranteed linkage discovery in at most 2 log n steps for non-linear problems.
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Lifting Embodied World Models for Planning and Control
Composing a policy that maps 2D waypoints to joint actions with a frozen world model yields a lifted world model that achieves 3.8 times lower mean joint error than direct low-level search while being more compute-efficient and generalizing to unseen environments.
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Linear Programming for Multi-Criteria Assessment with Cardinal and Ordinal Data: A Pessimistic Virtual Gap Analysis
Two-step pessimistic Virtual Gap Analysis via linear programming ranks alternatives with cardinal and ordinal data to identify and remove the weakest option.