| Detection of differentially abundant cell subpopulations in scRNA-seq data J Zhao, A Jaffe, H Li, O Lindenbaum, E Sefik, R Jackson, X Cheng, ... Proceedings of the National Academy of Sciences 118 (22), e2100293118, 2021 | 127 | 2021 |
| Estimating the Accuracies of Multiple Classifiers Without Labeled Data A Jaffe, B Nadler, Y Kluger Proceedings of the Eighteenth International Conference on Artificial …, 2015 | 75 | 2015 |
| Single-site localization via maximum discrimination multipath fingerprinting A Jaffe, M Wax IEEE Transactions on Signal Processing 62 (7), 1718-1728, 2014 | 64 | 2014 |
| Impact of healthcare worker shift scheduling on workforce preservation during the COVID-19 pandemic DM Kluger, Y Aizenbud, A Jaffe, F Parisi, L Aizenbud, E Minsky-Fenick, ... Infection Control & Hospital Epidemiology 41 (12), 1443-1445, 2020 | 60 | 2020 |
| Diabetes medications and risk of HCC MC Plaz Torres, A Jaffe, R Perry, E Marabotto, M Strazzabosco, ... Hepatology 76 (6), 1880-1897, 2022 | 58 | 2022 |
| Unsupervised ensemble learning with dependent classifiers A Jaffe, E Fetaya, B Nadler, T Jiang, Y Kluger Proceedings of the 19th International Conference on Artificial Intelligence …, 2016 | 56 | 2016 |
| A deep learning approach to unsupervised ensemble learning U Shaham, X Cheng, O Dror, A Jaffe, B Nadler, J Chang, Y Kluger International conference on machine learning, 30-39, 2016 | 48 | 2016 |
| Minimax-optimal semi-supervised regression on unknown manifolds A Moscovich, A Jaffe, N Boaz Artificial Intelligence and Statistics, 933-942, 2017 | 40 | 2017 |
| Newton correction methods for computing real eigenpairs of symmetric tensors A Jaffe, R Weiss, B Nadler SIAM Journal on Matrix Analysis and Applications 39 (3), 1071-1094, 2018 | 26 | 2018 |
| Randomized near-neighbor graphs, giant components and applications in data science A Jaffe, Y Kluger, GC Linderman, G Mishne, S Steinerberger Journal of applied probability 57 (2), 458-476, 2020 | 18 | 2020 |
| Super-resolution multi-reference alignment T Bendory, A Jaffe, W Leeb, N Sharon, A Singer Information and Inference: A Journal of the IMA 11 (2), 533-555, 2022 | 17 | 2022 |
| Learning binary latent variable models: A tensor eigenpair approach A Jaffe, R Weiss, B Nadler, S Carmi, Y Kluger International Conference on Machine Learning, 2196-2205, 2018 | 15 | 2018 |
| The discrete sign problem: uniqueness, recovery algorithms and phase retrieval applications B Leshem, O Raz, A Jaffe, B Nadler Applied and Computational Harmonic Analysis 45 (3), 463-485, 2018 | 12 | 2018 |
| The spectral underpinning of word2vec A Jaffe, Y Kluger, O Lindenbaum, J Patsenker, E Peterfreund, ... Frontiers in applied mathematics and statistics 6, 593406, 2020 | 11 | 2020 |
| Spectral neighbor joining for reconstruction of latent tree models A Jaffe, N Amsel, Y Aizenbud, B Nadler, JT Chang, Y Kluger SIAM journal on mathematics of data science 3 (1), 113-141, 2021 | 10 | 2021 |
| Method and system for multipath fingerprinting by maximum discrimination techniques M Wax, A Yaffe US Patent 9,143,176, 2015 | 6 | 2015 |
| Disc: Differential spectral clustering of features RD Sristi, G Mishne, A Jaffe Advances in Neural Information Processing Systems 35, 26269-26282, 2022 | 5 | 2022 |
| Spectral top-down recovery of latent tree models Y Aizenbud, A Jaffe, M Wang, A Hu, N Amsel, B Nadler, JT Chang, ... Information and Inference: A Journal of the IMA 12 (3), 2300-2350, 2023 | 3 | 2023 |
| Multi-modal differentiable unsupervised feature selection J Yang, O Lindenbaum, Y Kluger, A Jaffe Uncertainty in Artificial Intelligence, 2400-2410, 2023 | 3 | 2023 |
| Spectral Extraction of Unique Latent Variables S Yoffe, A Moscovich, A Jaffe arXiv preprint arXiv:2402.18741, 2024 | 1 | 2024 |
Yuval KlugerYale UniversityVerified email at yale.edu
