BOHM extracts multi-resolution attribution trees from existing routing weights in hierarchical AI systems, providing zero-cost explanations that correlate with SHAP when routing is near-optimal.
A decision-theoretic generalization of on-line learning and an application to boosting.Journal of Computer and System Sciences, 55(1):119–139, 1997
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
CONDITIONAL 2representative citing papers
A randomized (1+ε)-approximation algorithm for ordered-norm load balancing uses O((n+d)(ε^{-2} + log log d) log(n+d)) linear-oracle calls via follow-the-regularized-leader prices and martingale progress analysis.
citing papers explorer
-
BOHM: Zero-Cost Hierarchical Attribution for Compound AI Systems
BOHM extracts multi-resolution attribution trees from existing routing weights in hierarchical AI systems, providing zero-cost explanations that correlate with SHAP when routing is near-optimal.
-
An Efficient Algorithm for Minimizing Ordered Norms in Fractional Load Balancing
A randomized (1+ε)-approximation algorithm for ordered-norm load balancing uses O((n+d)(ε^{-2} + log log d) log(n+d)) linear-oracle calls via follow-the-regularized-leader prices and martingale progress analysis.