K-SVFair-FBF uses the new K-Shapley value to achieve meritocratic fairness with O(T^{3/4}) regret in budgeted combinatorial bandits under full-bandit feedback.
The many shapley values for model expla- nation
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
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UNVERDICTED 2representative citing papers
CECF is a new causal framework for edge classification that balances high-dimensional edge features against node influences via GNN embeddings and cross-attention to achieve better performance than standard methods.
citing papers explorer
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Meritocratic Fairness in Budgeted Combinatorial Multi-armed Bandits via Shapley Values
K-SVFair-FBF uses the new K-Shapley value to achieve meritocratic fairness with O(T^{3/4}) regret in budgeted combinatorial bandits under full-bandit feedback.
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Advancing Edge Classification through High-Dimensional Causal Modeling of Node-Edge Interplay
CECF is a new causal framework for edge classification that balances high-dimensional edge features against node influences via GNN embeddings and cross-attention to achieve better performance than standard methods.