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Found 6 papers, 2 papers with code
Epsilon*: Privacy Metric for Machine Learning Models
We introduce Epsilon*, a new privacy metric for measuring the privacy risk of a single model instance prior to, during, or after deployment of privacy mitigation strategies.
Unlocking Sales Growth: Account Prioritization Engine with Explainable AI
B2B sales requires effective prediction of customer growth, identification of upsell potential, and mitigation of churn risks.
Confounder Analysis in Measuring Representation in Product Funnels
This paper discusses an application of Shapley values in the causal inference field, specifically on how to select the top confounder variables for coarsened exact matching method in a scalable way.
Fast TreeSHAP: Accelerating SHAP Value Computation for Trees
We empirically find that Fast TreeSHAP v1 is 1. 5x faster than TreeSHAP while keeping the memory cost unchanged.
CrystalCandle: A User-Facing Model Explainer for Narrative Explanations
Predictive machine learning models often lack interpretability, resulting in low trust from model end users despite having high predictive performance.
Estimating Time-Varying Graphical Models
In this paper, we study time-varying graphical models based on data measured over a temporal grid.
