Preprints

​* indicates equal contribution

• B. Yasinik, M. Salhov, O. Lindenbaum, A. Averbuch. "Imbalanced Classification via Explicit Gradient Learning From Augmented Data".
• J. Gradshtein, M. Salhov, Y. Tulpan, O. Lindenbaum, A. Averbuch. "Imbalanced Classification via a Tabular Translation GAN".
• O. Lindenbaum*, Y. Aizenbud*, Y. Kluger, "Probabilistic Robust Autoencoders for Anomaly Detection".

Conference and Workshops

• B. Battash, L. Wolf, O. Lindenbaum, "Revisiting the Noise Model of Stochastic Gradient Descent". International Conference on Artificial Intelligence and Statistics (AISTATS) 2024.
• A. Rozner, B. Battash, O. Lindenbaum, L. Wolf, "Efficient Verification-Based Face Identification", The IEEE conference series on Automatic Face and Gesture Recognition 2024.
• B. Battash, L. Wolf, O. Lindenbaum, "Revisiting the Noise Model of SGD". NeurIPS 2023 Workshop Heavy Tails in Machine Learning 2023.
• J. Yang, O. Lindenbaum, Y. Kluger, A. Jaffe, "Multi-modal Differentiable Unsupervised Feature Selection," UAI 2023.
• T. Yamplosky, R. Talmon, O. Lindenbaum, "Domain and Modality Adaptation Using Multi-Kernel Matching," EUSIPCO 2023.
• J. Svirsky, O. Lindenbaum, "SG-VAD: Stochastic Gates Based Speech Activity Detection," ICASSP 2023.
• J. Yang*, O. Lindenbaum*, Y. Kluger, "Locally Sparse Neural Networks for Tabular Biomedical Data". ICML 2022.
• O. Lindenbaum, M. Salhov, A. Averbuch, Y. Kluger. "L0-Sparse Canonical Correlation Analysis". ICLR 2022. 
• O. Lindenbaum*, U. Shaham*, J. Svirsky, E. Peterfreund, Y. Kluger, "Differentiable Unsupervised Feature Selection based on a Gated Laplacian." NeurIPS 2021.
• H. Li*, O. Lindenbaum*, X. Cheng, A. Cloninger, "Variational Diffusion Autoencoders with Random Walk Sampling ", European Conference on Computer Vision (ECCV) 2020. 
• Y. Yamada*, O. Lindenbaum*, S. Negahban, Y. Kluger, "Feature selection using Stochastic Gates",  International Conference on Machine Learning (ICML) 2020.
•  O. Lindenbaum*,  Jay S. Stanley III*, Guy Wolf, Smita Krishnaswamy, "Geometry-Based Data Generation", Conference on Neural Information Processing Systems (NeurIPS) 2018. (spotlight ~4% acceptance rate) .
• O. Lindenbaum, N. Rabin, Y. Bregman, A. Averbuch, "Multi-channel fusion for seismic event detection and classification", IEEE International Conference on the Science of Electrical Engineering (ICSEE), 2016.
• O. Lindenbaum, A. Yeredor, A. Averbuch, "Clustering Based on MultiView Diffusion Maps", IEEE 16th International Conference on Data Mining Workshops (ICDMW), 2016. 
• O. Lindenbaum, A. Yeredor, A. Averbuch, "Bandwidth selection for kernel-based classification", IEEE International Conference on the Science of Electrical Engineering (ICSEE), 2016.
• A. Averbuch, M. Salhov, O. Lindenbaum, A. Silberschatz, Y. Shkolnisky, "Multi-view kernel-based data analysis‏", IEEE International Conference on the Science of Electrical Engineering (ICSEE), 2016.
• O. Lindenbaum, A. Yeredor, M. Salhov, "Learning coupled embedding using multiview diffusion maps", International Conference on Latent Variable Analysis and Signal Separation, 2015.
• O. Lindenbaum, A. Yeredor, R. Vitek, M. Mishali, "Blind separation of spatially-block-sparse sources from orthogonal mixtures", IEEE.  International Workshop on Machine Learning for Signal Processing (MLSP), 2013.
• O. Lindenbaum, S. Maskit, O. Kutiel, G. Nave. "Musical features extraction for audio-based search", IEEE 26th Convention of Electrical and Electronics Engineers in Israel (IEEEI), 2010.

Journal Papers

• A. Rozner, B. Battash, L. Wolf, O. Lindenbaum, "Domain-Generalizable Multiple-Domain Clustering." Transactions on Machine Learning Research 2024.
• S. Jana, H. Li, Y. Yamada, O. Lindenbaum, "Support Recovery with Stochastic Gates: Theory and Application for Linear Models". Elsevier Signal Processing, 2023.
• S. Farhadian, O. Lindenbaum, J. Zhao, M. Corly, Y. Im, H. Walsh, A. Vecchio, R. Milan, J. Chiarella, M. Chintanapol, R. Calvi, G. Wang, L. Ndhluvo, J. Yoon, D. Trotta, S. Ma, Y. Kluger, S. Spudich. "HIV viral transcription and immune perturbations in the CNS of people with HIV despite ART", JCI insight, 2022
• U. Shaham*, O. Lindenbaum*, J. Svirsky, Y. Kluger, "Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Features". Elsevier neural networks, 2022.
• O. Lindenbaum, S. Steinerberger, "Refined Least Squares for Support Recovery". Elsevier Journal of Signal Processing, 2022.
• L. Irshaid, J. Bleiberg, E Weinberger, J. Garritanod, R. M. Shallis, J. Patsenker, O. Lindenbaum, Y. Kluger, S. G. Katz, M. L. Xu, "Histopathologic and Machine Deep Learning Criteria to Predict Lymphoma Transformation in Bone Marrow Biopsies",   Archives of Pathology and Laboratory Medicine, 2021. 
• J. Zhao, A. Jaffe, H. Li, O. Lindenbaum, X. Cheng, R. Flavell, Y. Kluger, "Detecting regions of differential abundance between scRNA-seq datasets", Proceedings of the National Academy of Sciences (PNAS), 2021.    
• O. Lindenbaum*, A. Sagiv*, G. Mishne, R. Talmon, "Kernel-based parameter estimation of dynamical systems with unknown observation functions",  Chaos: An Interdisciplinary Journal of Nonlinear Science, 2021. 
• O. Lindenbaum, S. Steinerberger, "Randomly Aggregated Least Squares for Support Recovery", Elsevier Journal of Signal Processing, 2020.                                                                         
•  O. Lindenbaum,  N. Nouri, Y. Kluger, S. H. Kleinstein, "Alignment free identification of clones in B cell receptor repertoires", Nucleic Acid Research (NAR),  2020.​                            
• A. Jaffe, Y. Kluger, O. Lindenbaum, J. Patsenker, E.Peterfreund, S. Steinerberger, "The Spectral Underpinning of word2vec",  Frontiers in Applied Mathematics and Statistics, 2020.                         
• E. Peterfreund*, O. Lindenbaum*, F. Dietrich, T. Bertalan, M. Gavish, I. G. Kevrekidis, R. R. Coifman, "LOcal Conformal Autoencoder for standardized data coordinates", Proceedings of the national academy of sciences (PNAS), 2020. 
• O. Lindenbaum, M. Salhov, A. Yeredor, A. Averbuch, "Gaussian Bandwidth Selection for Manifold Learning and Classification" , Data Mining and Knowledge Discovery, 2020.
• Y. Bregman, O. Lindenbaum, N. Rabin, "Array-Based Earthquakes-Explosion Discrimination Using Diffusion Maps". Pure and Applied Geophysics, 2020.
• O. Lindenbaum, A. Yeredor, M Salhov,  A. Averbuch, Multiview diffusion maps", Information Fusion, 2020.
• O. Lindenbaum, N. Rabin, Y. Bregman, A. Averbuch, "Seismic Event Discrimination Using Deep CCA", IEEE Geoscience and Remote Sensing Letters, 2019
• M. Salhov, O. Lindenbaum, Y. Aizenbud, A. Silberschatz, Y. Shkolnisky, A. Averbuch, "Multi-view kernel consensus for data analysis and signal processing", Applied and Computational Harmonics Analysis (ACHA), 2019.
• O. Lindenbaum, Y. Bregman, N. Rabin, A. Averbuch. "Multi-View Kernels for Low-Dimensional Modeling of Seismic Events". IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2018.
• N. N. Rabin, Y. Bregman, O. Lindenbaum, Y. Ben-Horin, A. Averbuch‏, "Earthquake-explosion discrimination using diffusion maps", Geophysical Journal International 207 (3), 1484-1492, ‏. 2016
• O. Lindenbaum, A. Yeredor, Y. Cohen, "Musical key extraction using diffusion maps", Signal Processing 117, 198-207, 2015.
•  O. Lindenbaum, A. Yeredor, R. Vitek, M. Mishali, "Blind Separation of Orthogonal Mixtures of Spatially-Sparse Sources with Unknown   Sparsity Levels and with Temporal Blocks". Journal of Signal Processing Systems 79 (2), 167-178, 2015.