3 papers
cs.LG2023
Epsilon*: Privacy Metric for Machine Learning Models
Diana M. Negoescu, Humberto Gonzalez, Saad Eddin Al Orjany +9
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. The me…
cs.LG2022
Sales Channel Optimization via Simulations Based on Observational Data with Delayed Rewards: A Case Study at LinkedIn
Diana M. Negoescu, Pasha Khosravi, Shadow Zhao +3
Training models on data obtained from randomized experiments is ideal for making good decisions. However, randomized experiments are often time-consuming, expensive, risky, infeasi…
stat.ML2021
CrystalCandle: A User-Facing Model Explainer for Narrative Explanations
Jilei Yang, Diana Negoescu, Parvez Ahammad
Predictive machine learning models often lack interpretability, resulting in low trust from model end users despite having high predictive performance. While many model interpretat…