28 citations · 65 across the 10 of their papers we have counts for
10 papers
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…
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…
Greykite: Deploying Flexible Forecasting at Scale at LinkedIn
Reza Hosseini, Albert Chen, Kaixu Yang +5
Forecasts help businesses allocate resources and achieve objectives. At LinkedIn, product owners use forecasts to set business targets, track outlook, and monitor health. Engineers…
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…
A Members First Approach to Enabling LinkedIn's Labor Market Insights at Scale
Ryan Rogers, Adrian Rivera Cardoso, Koray Mancuhan +5
We describe the privatization method used in reporting labor market insights from LinkedIn's Economic Graph, including the differentially private algorithms used to protect member'…
LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at Scale
Ryan Rogers, Subbu Subramaniam, Sean Peng +5
We present a privacy system that leverages differential privacy to protect LinkedIn members' data while also providing audience engagement insights to enable marketing analytics re…
MCA-based Rule Mining Enables Interpretable Inference in Clinical Psychiatry
Qingzhu Gao, Humberto Gonzalez, Parvez Ahammad
Development of interpretable machine learning models for clinical healthcare applications has the potential of changing the way we understand, treat, and ultimately cure, diseases…
Perceived Performance of Webpages In the Wild: Insights from Large-scale Crowdsourcing of Above-the-Fold QoE
Qingzhu Gao, Prasenjit Dey, Parvez Ahammad
Clearly, no one likes webpages with poor quality of experience (QoE). Being perceived as slow or fast is a key element in the overall perceived QoE of web applications. While exten…
SoK: Applying Machine Learning in Security - A Survey
Heju Jiang, Jasvir Nagra, Parvez Ahammad
The idea of applying machine learning(ML) to solve problems in security domains is almost 3 decades old. As information and communications grow more ubiquitous and more data become…
A recommender system for efficient discovery of new anomalies in large-scale access logs
Heju Jiang, Scott Algatt, Parvez Ahammad
We present a novel, non-standard recommender system for large-scale security policy management(SPM). Our system Helios discovers and recommends unknown and unseen anomalies in larg…