6 papers · 1 filter
Adaptive Sparse Möbius Transforms for Learning Polynomials
Yigit Efe Erginbas, Justin Singh Kang, Elizabeth Polito +1
We consider the problem of exactly learning an -sparse real-valued Boolean polynomial of degree of the form . This problem corresponds t…
An Odd Estimator for Shapley Values
Fabian Fumagalli, Landon Butler, Justin Singh Kang +2
The Shapley value is a ubiquitous framework for attribution in machine learning, encompassing feature importance, data valuation, and causal inference. However, its exact computati…
ProxySPEX: Inference-Efficient Interpretability via Sparse Feature Interactions in LLMs
Landon Butler, Abhineet Agarwal, Justin Singh Kang +3
Large Language Models (LLMs) have achieved remarkable performance by capturing complex interactions between input features. To identify these interactions, most existing approaches…
SPEX: Scaling Feature Interaction Explanations for LLMs
Justin Singh Kang, Landon Butler, Abhineet Agarwal +4
Large language models (LLMs) have revolutionized machine learning due to their ability to capture complex interactions between input features. Popular post-hoc explanation methods…
SHAP zero Explains Biological Sequence Models with Near-zero Marginal Cost for Future Queries
Darin Tsui, Aryan Musharaf, Yigit Efe Erginbas +2
The growing adoption of machine learning models for biological sequences has intensified the need for interpretable predictions, with Shapley values emerging as a theoretically gro…
Learning to Understand: Identifying Interactions via the Möbius Transform
Justin S. Kang, Yigit E. Erginbas, Landon Butler +2
One of the key challenges in machine learning is to find interpretable representations of learned functions. The Möbius transform is essential for this purpose, as its coefficients…