Consistency of survey opinions and external data. - Samuel Ackerman (Comput. Stat., 2019)

Sequential Drift Detection in Deep Learning Classifiers. - Samuel Ackerman; Parijat Dube; Eitan Farchi (CoRR, 2020)

Detection of data drift and outliers affecting machine learning model performance over time. - Samuel Ackerman; Eitan Farchi; Orna Raz; Marcel Zalmanovici; Parijat Dube (CoRR, 2020)

Machine Learning Model Drift Detection Via Weak Data Slices. - Samuel Ackerman; Parijat Dube; Eitan Farchi; Orna Raz; Marcel Zalmanovici (DeepTest@ICSE, 2021)

FreaAI: Automated extraction of data slices to test machine learning models. - Samuel Ackerman; Orna Raz; Marcel Zalmanovici (CoRR, 2021)

Towards API Testing Across Cloud and Edge. - Samuel Ackerman; Sanjib Choudhury; Nirmit Desai; Eitan Farchi; Dan Gisolfi; Andrew Hicks; Saritha Route; Diptikalyan Saha (CoRR, 2021)

Density-based interpretable hypercube region partitioning for mixed numeric and categorical data. - Samuel Ackerman; Eitan Farchi; Orna Raz; Marcel Zalmanovici; Maya Zohar (CoRR, 2021)

Detecting model drift using polynomial relations. - Eliran Roffe; Samuel Ackerman; Orna Raz; Eitan Farchi (CoRR, 2021)

Using sequential drift detection to test the API economy. - Samuel Ackerman; Parijat Dube; Eitan Farchi (CoRR, 2021)

Automatically detecting data drift in machine learning classifiers. - Samuel Ackerman; Orna Raz; Marcel Zalmanovici; Aviad Zlotnick (CoRR, 2021)

Classifier Data Quality: A Geometric Complexity Based Method for Automated Baseline And Insights Generation. - George Kour; Marcel Zalmanovici; Orna Raz; Samuel Ackerman; Ateret Anaby-Tavor (CoRR, 2021)

Experiment Based Crafting and Analyzing of Machine Learning Solutions. - Samuel Ackerman; Eitan Farchi; Orna Raz; Onn Shehory (CoRR, 2022)

High-quality Conversational Systems. - Samuel Ackerman; Ateret Anaby-Tavor; Eitan Farchi; Esther Goldbraich; George Kour; Ella Rabinovich; Orna Raz; Saritha Route; Marcel Zalmanovici; Naama Zwerdling (CoRR, 2022)

Measuring the Measuring Tools: An Automatic Evaluation of Semantic Metrics for Text Corpora. - George Kour; Samuel Ackerman; Orna Raz; Eitan Farchi; Boaz Carmeli; Ateret Anaby-Tavor (CoRR, 2022)

Reliable and Interpretable Drift Detection in Streams of Short Texts. - Ella Rabinovich; Matan Vetzler; Samuel Ackerman; Ateret Anaby-Tavor (ACL (industry), 2023)

Automatic Generation of Attention Rules For Containment of Machine Learning Model Errors. - Samuel Ackerman; Axel Bendavid; Eitan Farchi; Orna Raz (CoRR, 2023)

Characterizing how 'distributional' NLP corpora distance metrics are. - Samuel Ackerman; George Kour; Eitan Farchi (CoRR, 2023)

Data Drift Monitoring for Log Anomaly Detection Pipelines. - Dipak Wani; Samuel Ackerman; Eitan Farchi; Xiaotong Liu; Hau-wen Chang; Sarasi Lalithsena (CoRR, 2023)

Predicting Question-Answering Performance of Large Language Models through Semantic Consistency. - Ella Rabinovich; Samuel Ackerman; Orna Raz; Eitan Farchi; Ateret Anaby-Tavor (CoRR, 2023)

Deploying automated ticket router across the enterprise. - Samuel Ackerman; Lincoln Alexander; Margaret Bennett; Donglin Chen; Eitan Farchi; Autumn Houseknecht; Padmanabhan Santhanam (AI Mag., 2023)

A Novel Metric for Measuring the Robustness of Large Language Models in Non-adversarial Scenarios. - Samuel Ackerman; Ella Rabinovich; Eitan Farchi; Ateret Anaby-Tavor (EMNLP (Findings), 2024)

Using Combinatorial Optimization to Design a High quality LLM Solution. - Samuel Ackerman; Eitan Farchi; Rami Katan; Orna Raz (CoRR, 2024)

Statistical multi-metric evaluation and visualization of LLM system predictive performance. - Samuel Ackerman; Eitan Farchi; Orna Raz; Assaf Toledo (CoRR, 2025)