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Found 5 papers, 3 papers with code
SPEX: Scaling Feature Interaction Explanations for LLMs
We propose Spectral Explainer (SPEX), a model-agnostic interaction attribution algorithm that efficiently scales to large input lengths ($\approx 1000)$.
SHAP zero Explains Genomic Models with Near-zero Marginal Cost for Future Queried Sequences
With the rapid growth of large-scale machine learning models in genomics, Shapley values have emerged as a popular method for model explanations due to their theoretical guarantees.
Efficiently Computing Sparse Fourier Transforms of $q$-ary Functions
Fourier transformations of pseudo-Boolean functions are popular tools for analyzing functions of binary sequences.
Interactive Learning with Pricing for Optimal and Stable Allocations in Markets
Large-scale online recommendation systems must facilitate the allocation of a limited number of items among competing users while learning their preferences from user feedback.
Interactive Recommendations for Optimal Allocations in Markets with Constraints
Recommendation systems when employed in markets play a dual role: they assist users in selecting their most desired items from a large pool and they help in allocating a limited number of items to the users who desire them the most.
