1 citations · 2 across the 10 of their papers we have counts for
8 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…
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…
Online Assortment and Price Optimization Under Contextual Choice Models
Yigit Efe Erginbas, Thomas A. Courtade, Kannan Ramchandran
We consider an assortment selection and pricing problem in which a seller has different items available for sale. In each round, the seller observes a -dimensional contextua…
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…
Interactive Learning with Pricing for Optimal and Stable Allocations in Markets
Yigit Efe Erginbas, Soham Phade, Kannan Ramchandran
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. As…
Interactive Recommendations for Optimal Allocations in Markets with Constraints
Yigit Efe Erginbas, Soham Phade, Kannan Ramchandran
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 nu…