sHAIL
Saturated Hierarchical Atomic Increment Learning, a framework that traverses a nested hierarchy of hypothesis classes via a sequential staircase mechanism. The algorithm ascends to the next hypothesis class level when performance improvement saturates, balancing model expressiveness against empirical fit in a principled, stage-wise manner.
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sHAIL-Causal is a specialized version of the Saturated Hierarchical Atomic Incremental Learning (sHAIL) paradigm for the causal discovery setting
The basic mechanism of sHAIL is to climb step by step along a nested hierarchy of hypothesis classes H_0 ⊂ H_1 ⊂ ... ⊂ H_K, triggering a signal to advance to the next level when mastery becomes saturated
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sHAIL-Causal is a specialized version of the Saturated Hierarchical Atomic Incremental Learning (sHAIL) paradigm for the causal discovery setting
50%UnverifiedThe basic mechanism of sHAIL is to climb step by step along a nested hierarchy of hypothesis classes H_0 ⊂ H_1 ⊂ ... ⊂ H_K, triggering a signal to advance to the next level when mastery becomes saturated
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