| General and specific utility measures for synthetic data J Snoke, GM Raab, B Nowok, C Dibben, A Slavkovic Journal of the Royal Statistical Society: Series A (Statistics in Society …, 2018 | 198 | 2018 |
| COVID-19 and the State of K-12 Schools: Results and Technical Documentation from the Fall 2020 American Educator Panels COVID-19 Surveys. Research Report. RR-A168-5. JH Kaufman, M Diliberti, GP Hunter, D Grant, LS Hamilton, HL Schwartz, ... RAND Corporation, 2020 | 143* | 2020 |
| Comparative study of differentially private synthetic data algorithms from the NIST PSCR differential privacy synthetic data challenge CMK Bowen, J Snoke Journal of Privacy and Confidentiality 11 (1), 2021 | 48 | 2021 |
| pMSE Mechanism: Differentially Private Synthetic Data with Maximal Distributional Similarity J Snoke, A Slavković International Conference on Privacy in Statistical Databases, 138-159, 2018 | 42 | 2018 |
| American Instructional Resources Surveys: 2021 Technical Documentation and Survey Results. Research Report. RR-A134-10. S Doan, MP Fernandez, D Grant, JH Kaufman, CM Setodji, J Snoke, ... RAND Corporation, 2021 | 32 | 2021 |
| COVID-19 and the State of K-12 Schools: Results and Technical Documentation from the Spring 2021 American Educator Panels COVID-19 Surveys JH Kaufman, MK Diliberti, GP Hunter, J Snoke, DM Grant, CM Setodji, ... RAND, 2021 | 20 | 2021 |
| A Feasibility Study of Differentially Private Summary Statistics and Regression Analyses with Evaluations on Administrative and Survey Data AF Barrientos, AR Williams, J Snoke, CMK Bowen Journal of the American Statistical Association 119 (545), 52-65, 2024 | 15* | 2024 |
| synthpop: Generating synthetic versions of sensitive microdata for statistical disclosure control B Nowok, GM Raab, J Snoke, C Dibben R package version, 1.3-0, 2016 | 13* | 2016 |
| How Statisticians Should Grapple with Privacy in a Changing Data Landscape J Snoke, CMK Bowen CHANCE 33 (4), 6-13, 2020 | 12 | 2020 |
| Providing accurate models across private partitioned data: Secure maximum likelihood estimation J Snoke, TR Brick, A Slavković, MD Hunter Annals of Applied Statistics 12 (2), 877-914, 2018 | 11 | 2018 |
| Using Neural Generative Models to Release Synthetic Twitter Corpora with Reduced Stylometric Identifiability of Users AG Ororbia II, F Linder, J Snoke arXiv preprint arXiv:1606.01151, 2017 | 9* | 2017 |
| Organizational Characteristics Associated with Risk of Sexual Assault and Sexual Harassment in the US Army M Matthews, AR Morral, TL Schell, M Cefalu, J Snoke, RJ Briggs | 7 | 2021 |
| American Instructional Resources Surveys S Doan, PAZ FERNANDEZ, D Grant, JH Kaufman, CM Setodji, J Snoke, ... | 6 | 2022 |
| Disclosing economists’ privacy perspectives: A survey of american economic association members on differential privacy and data fitness for use standards AR Williams, J Snoke, C Bowen, AF Barrientos National Bureau of Economic Research, 2023 | 5 | 2023 |
| Incompatibilities Between Current Practices in Statistical Data Analysis and Differential Privacy J Snoke, CMK Bowen, AR Williams, AF Barrientos arXiv preprint arXiv:2309.16703, 2023 | 3 | 2023 |
| Do No Harm Guide: Applying Equity Awareness In Data Privacy Methods C Bowen, J Snoke Urban Institute, 2023 | 3 | 2023 |
| US Air Force Enlisted Classification and Reclassification: Potential Improvements Using Machine Learning and Optimization Models S Robson, MC Lytell, M Walsh, KC Hall, KM Keller, V Kilambi, J Snoke, ... RAND, 2022 | 3* | 2022 |
| Leveraging Machine Learning to Improve Human Resource Management: Key Findings and Recommendations for Policymakers D Schulker, M Walsh, A Calkins, M Graham, CK Montemayor, AA Robbert, ... RAND, 2024 | 2 | 2024 |
| Advancing Equitable Decisionmaking for the Department of Defense Through Fairness in Machine Learning I Cabreros, J Snoke, OA Osoba, I Khan, MN Elliott | 2 | 2023 |
| Differential Privacy: What Is It? J Snoke, CMK Bowen AMSTAT news: the membership magazine of the American Statistical Association …, 2019 | 2 | 2019 |
Claire McKay BowenUrban InstituteVerified email at urban.org
