| Feature selection using Stochastic Gates Y Yamada*, O Lindenbaum*, S Negahban, Y Kluger International Conference on Machine Learning, ICML 2020, 2020 | 140 | 2020 |
| 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 | 95 | 2021 |
| Multi-view diffusion maps O Lindenbaum, A Yeredor, M Salhov, A Averbuch Information Fusion 55, 127-149, 2020 | 78 | 2020 |
| Variational Diffusion Autoencoders with Random Walk Sampling AC H Li*, O Lindenbaum*, X Cheng European Conference on Computer Vision, ECCV, 2020, 2019 | 54* | 2019 |
| Differentiable Unsupervised Feature Selection based on a Gated Laplacian O Lindenbaum*, U Shaham*, J Svirsky, E Peterfreund, Y Kluger Thirty-Fifth Conference on Neural Information Processing Systems, 2021 | 39* | 2021 |
| Gaussian bandwidth selection for manifold learning and classification O Lindenbaum, M Salhov, A Yeredor, A Averbuch Data mining and knowledge discovery 34 (6), 1676-1712, 2020 | 33* | 2020 |
| Earthquake-explosion discrimination using diffusion maps N Rabin, Y Bregman, O Lindenbaum, Y Ben-Horin, A Averbuch Geophysical Journal International 207 (3), 1484-1492, 2016 | 33 | 2016 |
| Geometry-based data generation O Lindenbaum, JS Stanley III, G Wolf, S Krishnaswamy Neural Information Processing Systems, NeurIPS, 2018, 2018 | 29 | 2018 |
| Multiview kernels for low-dimensional modeling of seismic events O Lindenbaum, Y Bregman, N Rabin, A Averbuch IEEE Transactions on Geoscience and Remote Sensing 56 (6), 3300-3310, 2018 | 24 | 2018 |
| Locally Sparse Neural Networks for Tabular Biomedical Data J Yang*, O Lindenbaum*, Y Kluger International Conference on Machine Learning, 25123-25153, 2022 | 22 | 2022 |
| LOCA: LOcal Conformal Autoencoder for standardized data coordinates E Peterfreund*, O Lindenbaum*, F Dietrich, T Bertalan, M Gavish, ... Proceedings of National Academy of Sciences (PNAS), 2020, 2020 | 22* | 2020 |
| Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Features U Shaham*, O Lindenbaum*, J Svirsky, Y Kluger Neural Networks, 2022, 2022 | 21 | 2022 |
| Multi-channel fusion for seismic event detection and classification O Lindenbaum, N Rabin, Y Bregman, A Averbuch 2016 IEEE International Conference on the Science of Electrical Engineering …, 2016 | 20 | 2016 |
| HIV viral transcription and immune perturbations in the CNS of people with HIV despite ART SF Farhadian, O Lindenbaum, J Zhao, MJ Corley, Y Im, H Walsh, ... JCI insight 7 (13), 2022 | 19 | 2022 |
| Alignment free identification of clones in B cell receptor repertoires O Lindenbaum, N Nouri, Y Kluger, S Kleinstein Nucleic Acid Research (NAR), 2020, 2020 | 19 | 2020 |
| Multi-view kernel consensus for data analysis M Salhov, O Lindenbaum, Y Aizenbud, A Silberschatz, Y Shkolnisky, ... Applied and Computational Harmonic Analysis 49 (1), 208-228, 2020 | 18* | 2020 |
| Array based earthquakes-explosion discrimination using diffusion maps Y Bregman, O Lindenbaum, N Rabin Pure and Applied Geophysics 178, 2403-2418, 2021 | 16 | 2021 |
| Histopathologic and machine deep learning criteria to predict lymphoma transformation in bone marrow biopsies L Irshaid, J Bleiberg, E Weinberger, J Garritano, RM Shallis, J Patsenker, ... Archives of Pathology & Laboratory Medicine 146 (2), 182-193, 2022 | 15 | 2022 |
| L0-Sparse Canonical Correlation Analysis O Lindenbaum, M Salhov, A Averbuch, Y Kluger 2022, International Conference on Learning Representations, 2022 | 15* | 2022 |
| Learning coupled embedding using multiview diffusion maps O Lindenbaum, A Yeredor, M Salhov Latent Variable Analysis and Signal Separation: 12th International …, 2015 | 14 | 2015 |
Yuval KlugerYale UniversityVerified email at yale.edu
