Samuel Ackerman

According to our database, Samuel Ackerman authored at least 20 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Deploying automated ticket router across the enterprise.

Samuel Ackerman, Lincoln Alexander, Margaret Bennett, Donglin Chen, Eitan Farchi, Autumn Houseknecht, Padmanabhan Santhanam

AI Mag., March, 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

Data Drift Monitoring for Log Anomaly Detection Pipelines.

Dipak Wani, Samuel Ackerman, Eitan Farchi, Xiaotong Liu, Hau-wen Chang, Sarasi Lalithsena

CoRR, 2023

Characterizing how 'distributional' NLP corpora distance metrics are.

Samuel Ackerman, George Kour, Eitan Farchi

CoRR, 2023

Automatic Generation of Attention Rules For Containment of Machine Learning Model Errors.

Samuel Ackerman, Axel Bendavid, Eitan Farchi, Orna Raz

CoRR, 2023

Reliable and Interpretable Drift Detection in Streams of Short Texts.

Ella Rabinovich, Matan Vetzler, Samuel Ackerman, Ateret Anaby-Tavor

Proceedings of the The 61st Annual Meeting of the Association for Computational Linguistics: Industry Track, 2023

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

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

Experiment Based Crafting and Analyzing of Machine Learning Solutions.

Samuel Ackerman, Eitan Farchi, Orna Raz, Onn Shehory

CoRR, 2022

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

Automatically detecting data drift in machine learning classifiers.

Samuel Ackerman, Orna Raz, Marcel Zalmanovici, Aviad Zlotnick

CoRR, 2021

Using sequential drift detection to test the API economy.

Samuel Ackerman, Parijat Dube, Eitan Farchi

CoRR, 2021

Detecting model drift using polynomial relations.

Eliran Roffe, Samuel Ackerman, Orna Raz, Eitan Farchi

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

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

FreaAI: Automated extraction of data slices to test machine learning models.

Samuel Ackerman, Orna Raz, Marcel Zalmanovici

CoRR, 2021

Machine Learning Model Drift Detection Via Weak Data Slices.

Samuel Ackerman, Parijat Dube, Eitan Farchi, Orna Raz, Marcel Zalmanovici

Proceedings of the 3rd IEEE/ACM International Workshop on Deep Learning for Testing and Testing for Deep Learning, 2021

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

Sequential Drift Detection in Deep Learning Classifiers.

Samuel Ackerman, Parijat Dube, Eitan Farchi

CoRR, 2020

2019

Consistency of survey opinions and external data.

Samuel Ackerman

Comput. Stat., 2019