| Detection of data drift and outliers affecting machine learning model performance over time S Ackerman, E Farchi, O Raz, M Zalmanovici, P Dube arXiv preprint arXiv:2012.09258, 2020 | 41 | 2020 |
| Automatically detecting data drift in machine learning classifiers S Ackerman, O Raz, M Zalmanovici, A Zlotnick arXiv preprint arXiv:2111.05672, 2021 | 28 | 2021 |
| FreaAI: Automated extraction of data slices to test machine learning models S Ackerman, O Raz, M Zalmanovici International Workshop on Engineering Dependable and Secure Machine Learning …, 2020 | 17 | 2020 |
| Machine learning model drift detection via weak data slices S Ackerman, P Dube, E Farchi, O Raz, M Zalmanovici 2021 IEEE/ACM Third International Workshop on Deep Learning for Testing and …, 2021 | 9 | 2021 |
| The effect of self-reported transitory income shocks on household spending S Ackerman, J Sabelhaus Finance and Economics Discussion Series 64, 2012 | 9 | 2012 |
| The effect of self-reported transitory income shocks on household spending J Sabelhaus, S Ackerman FEDS Working Paper, 2012 | 8 | 2012 |
| Measuring the measuring tools: An automatic evaluation of semantic metrics for text corpora G Kour, S Ackerman, O Raz, E Farchi, B Carmeli, A Anaby-Tavor arXiv preprint arXiv:2211.16259, 2022 | 6 | 2022 |
| Sequential drift detection in deep learning classifiers S Ackerman, P Dube, E Farchi arXiv preprint arXiv:2007.16109, 2020 | 5 | 2020 |
| Diminution of test templates in test suites SS Ackerman, R Gal, A Koyfman, A Ziv US Patent 11,023,366, 2021 | 4 | 2021 |
| Predicting Question-Answering Performance of Large Language Models through Semantic Consistency E Rabinovich, S Ackerman, O Raz, E Farchi, A Anaby-Tavor arXiv preprint arXiv:2311.01152, 2023 | 3 | 2023 |
| Density-based interpretable hypercube region partitioning for mixed numeric and categorical data S Ackerman, E Farchi, O Raz, M Zalmanovici, M Zohar arXiv preprint arXiv:2110.05430, 2021 | 3 | 2021 |
| Data Drift Monitoring for Log Anomaly Detection Pipelines D Wani, S Ackerman, E Farchi, X Liu, H Chang, S Lalithsena arXiv preprint arXiv:2310.14893, 2023 | 2 | 2023 |
| Detecting model drift using polynomial relations E Roffe, S Ackerman, O Raz, E Farchi arXiv preprint arXiv:2110.12506, 2021 | 2 | 2021 |
| Consistency of survey opinions and external data S Ackerman Computational Statistics 34 (4), 1489-1509, 2019 | 2 | 2019 |
| Theory and Practice of Quality Assurance for Machine Learning Systems An Experiment Driven Approach S Ackerman, G Barash, E Farchi, O Raz, O Shehory arXiv preprint arXiv:2201.00355, 2022 | 1 | 2022 |
| Classifier Data Quality: A Geometric Complexity Based Method for Automated Baseline And Insights Generation G Kour, M Zalmanovici, O Raz, S Ackerman, A Anaby-Tavor arXiv preprint arXiv:2112.11832, 2021 | 1 | 2021 |
| Using sequential drift detection to test the API economy S Ackerman, P Dube, E Farchi arXiv preprint arXiv:2111.05136, 2021 | 1 | 2021 |
| Red Zone, Blue Zone: Discovering Parking Ticket Trends in New York City SS ACKERMAN, RE MOUSTAFA | 1 | 2011 |
| Characterizing how'distributional'NLP corpora distance metrics are S Ackerman, G Kour, E Farchi arXiv preprint arXiv:2310.14829, 2023 | | 2023 |
| Generating data slice rules for data generation O Raz, G Kour, R Narayanam, SS Ackerman, M Zalmanovici US Patent App. 17/682,635, 2023 | | 2023 |
Eitan FarchiIBM Research Lab in HaifaVerified email at il.ibm.com
Eitan FarchiIBM Research Lab in HaifaVerified email at il.ibm.com
Parijat DubeIBMVerified email at us.ibm.com