{"id":17,"date":"2021-10-11T09:28:24","date_gmt":"2021-10-11T09:28:24","guid":{"rendered":"http:\/\/www.eng.biu.ac.il\/lindeno\/?page_id=17"},"modified":"2025-11-03T11:25:15","modified_gmt":"2025-11-03T11:25:15","slug":"publications","status":"publish","type":"page","link":"https:\/\/www.eng.biu.ac.il\/lindeno\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"\n<p style=\"font-size:30px\">Preprints<\/p>\n\n\n\n<p>\u200b* indicates equal contribution<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>B. Battash, A. Rozner, L. Wolf, <strong>O. Lindenbaum<\/strong>, \"<a href=\"https:\/\/scholar.google.co.il\/citations?view_op=view_citation&amp;hl=en&amp;user=jXxk6gcAAAAJ&amp;sortby=pubdate&amp;citation_for_view=jXxk6gcAAAAJ:2P1L_qKh6hAC\"><strong>Obtaining Favorable Layouts for Multiple Object Generation<\/strong><\/a>\"<\/li>\n\n\n\n<li>B. Yasinik, M. Salhov, O. Lindenbaum, A. Averbuch. <a href=\"http:\/\/Imbalanced Classification via Explicit Gradient Learning From Augmented Data\"><strong>\"Imbalanced Classification via Explicit Gradient Learning From Augmented Data\".<\/strong><\/a><\/li>\n\n\n\n<li>J. Gradshtein, M. Salhov, Y. Tulpan, <strong>O. Lindenbaum<\/strong>, A. Averbuch. <a href=\"https:\/\/arxiv.org\/abs\/2204.08683\"><strong>\"Imbalanced Classification via a Tabular Translation GAN\".<\/strong><\/a><\/li>\n<\/ul>\n\n\n\n<p style=\"font-size:30px\">Conference and Workshops<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>E. Naor, <strong>Ofir Lindenbaum<\/strong>, <a href=\"https:\/\/openreview.net\/pdf\/7db78659e883b2a5d4c00f1612b3c608384aef10.pdf\">Hybrid Autoencoders for Tabular Data: Leveraging Model-Based Augmentation in Low-Label Settings<\/a>. <em>Conference on Neural Information Processing Systems (NeurIPS) 2025.<\/em><\/li>\n\n\n\n<li>Y. Refael,&nbsp;G. Smorodinsky,&nbsp;T. Tirer,&nbsp;<strong>Ofir Lindenbaum<\/strong>, <a href=\"https:\/\/arxiv.org\/abs\/2505.24749\">SUMO: Subspace-Aware Moment-Orthogonalization for Accelerating Memory-Efficient LLM Training<\/a>. <em>Conference on Neural Information Processing Systems (NeurIPS) 2025.<\/em><\/li>\n\n\n\n<li>E. Peterfreund, <strong>O. Lindenbaum<\/strong>, Y. Kluger, B. Landa, \u201c <a href=\"https:\/\/openreview.net\/pdf\/83fd645f4157941b5b6f82780aaee84adc8a059e.pdf\">Partition First, Embed Later: Laplacian-Based Feature Partitioning for Refined Embedding and Visualization of High-Dimensional Data.<\/a>\u201d <em>International Conference on Machine Learning (ICML), 2025.<\/em> (oral, top 1% papers).<\/li>\n\n\n\n<li>Y. Refael, J. Svirsky, B. Shustin, W. Huliel, <strong>O. Lindenbaum<\/strong>, \"<strong><a href=\"https:\/\/arxiv.org\/abs\/2410.17881\" data-type=\"link\" data-id=\"https:\/\/arxiv.org\/abs\/2410.17881\">AdaRankGrad: Adaptive Gradient-Rank and Moments for Memory-Efficient LLMs Training and Fine-Tuning<\/a>.<\/strong>\" <em> International Conference on Learning Representations (ICLR) 2025<\/em>. <\/li>\n\n\n\n<li>R. Eisenberg, J. Svirsky, <strong>O. Lindenbaum<\/strong>, \"<a href=\"https:\/\/scholar.google.co.il\/citations?view_op=view_citation&amp;hl=en&amp;user=jXxk6gcAAAAJ&amp;sortby=pubdate&amp;citation_for_view=jXxk6gcAAAAJ:35N4QoGY0k4C\"><strong>Correlation-based Permutations for Multi-View Clustering<\/strong>.\"<\/a><em> International Conference on Learning Representations (ICLR) 2025<\/em>. Spotlight. <\/li>\n\n\n\n<li>A. Steinberg, R. Eisenberg, <strong>O. Lindenbaum<\/strong>, \"<a href=\"https:\/\/scholar.google.co.il\/citations?view_op=view_citation&amp;hl=iw&amp;user=jXxk6gcAAAAJ&amp;sortby=pubdate&amp;citation_for_view=jXxk6gcAAAAJ:dfsIfKJdRG4C\"><strong>Conditional Deep Canonical Time Warping.\" <\/strong><\/a>International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2025.\u200f<\/li>\n\n\n\n<li>A. Rozner, B. Battash, L. Wolf, <strong>O. Lindenbaum<\/strong>, \"<strong><a href=\"https:\/\/openreview.net\/pdf?id=O9RUANpPmb\">Knowledge Editing in Language Models via Adapted Direct Preference Optimization<\/a><\/strong>.\" <em>Conference on Empirical Methods in Natural Language Processing (EMNLP Findings) 2024.<\/em><\/li>\n\n\n\n<li>J. Svirsky, <strong>O. Lindenbaum<\/strong>, \"<a href=\"https:\/\/scholar.google.co.il\/citations?view_op=view_citation&amp;hl=en&amp;user=jXxk6gcAAAAJ&amp;sortby=pubdate&amp;citation_for_view=jXxk6gcAAAAJ:GnPB-g6toBAC\"><strong>Interpretable deep clustering<\/strong><\/a>.\" <em>International Conference on Machine Learning (ICML) 2024.<\/em><\/li>\n\n\n\n<li>R. Dyuthi Sristi, <strong>O. Lindenbaum<\/strong>, M. Lavzin, J. Schiller, G. Mishne, H. Benisty, \"<strong><a href=\"https:\/\/scholar.google.co.il\/citations?view_op=view_citation&amp;hl=en&amp;user=jXxk6gcAAAAJ&amp;sortby=pubdate&amp;citation_for_view=jXxk6gcAAAAJ:vV6vV6tmYwMC\">Contextual Feature Selection with Conditional Stochastic Gates<\/a><\/strong>.\" <em>International Conference on Machine Learning (ICML) 2024.<\/em><\/li>\n\n\n\n<li>A. Rozner*, B. Battash*,  H. Li,  L. Wolf, <strong>O. Lindenbaum<\/strong>, \"<a href=\"https:\/\/scholar.google.co.il\/citations?view_op=view_citation&amp;hl=en&amp;user=jXxk6gcAAAAJ&amp;sortby=pubdate&amp;citation_for_view=jXxk6gcAAAAJ:YFjsv_pBGBYC\"><strong>Anomaly Detection with Variance Stabilized Density Estimation<\/strong><\/a>\". <em>Conference on Uncertainty in Artificial Intelligence (UAI) 2024.<\/em><\/li>\n\n\n\n<li><strong>O. Lindenbaum*<\/strong>, Y. Aizenbud*, Y. Kluger, <strong>\"<span class=\"tadv-color\" style=\"color:#db572f\"><a href=\"https:\/\/arxiv.org\/abs\/2110.00494\"><\/a><\/span><\/strong><span class=\"tadv-color\" style=\"color:#db572f\"><a href=\"https:\/\/openreview.net\/forum?id=UkA5dZs5mP&amp;referrer=%5BAuthor%20Console%5D(%2Fgroup%3Fid%3Dauai.org%2FUAI%2F2024%2FConference%2FAuthors%23your-submissions)\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Transductive and Inductive Outlier Detection with Robust Autoencoders<\/strong><\/a>\".<\/span> <em>Conference on Uncertainty in Artificial Intelligence (UAI) 2024.<\/em><\/li>\n\n\n\n<li>B. Battash, L. Wolf, <strong>O. Lindenbaum<\/strong>, \"<strong><a href=\"https:\/\/proceedings.mlr.press\/v238\/battash24a\/battash24a.pdf\" data-type=\"link\" data-id=\"https:\/\/proceedings.mlr.press\/v238\/battash24a\/battash24a.pdf\">Revisiting the Noise Model of Stochastic Gradient Descent<\/a>\"<\/strong>.<em> International Conference on Artificial Intelligence and Statistics (AISTATS) 2024.<\/em><\/li>\n\n\n\n<li>A. Rozner, B. Battash, <strong>O. Lindenbaum<\/strong>, L. Wolf, \"<a href=\"https:\/\/scholar.google.com\/citations?view_op=view_citation&amp;hl=en&amp;user=jXxk6gcAAAAJ&amp;sortby=pubdate&amp;citation_for_view=jXxk6gcAAAAJ:J_g5lzvAfSwC\"><strong>Efficient Verification-Based Face Identification<\/strong><\/a>\", <em>The IEEE&nbsp;conference&nbsp;series on Automatic&nbsp;Face&nbsp;and  Gesture&nbsp;Recognition&nbsp;2024.<\/em><\/li>\n\n\n\n<li>I. Cohen, S. Gannot, <strong>O. Lindenbaum<\/strong>, \"<a href=\"https:\/\/scholar.google.co.il\/citations?view_op=view_citation&amp;hl=en&amp;user=jXxk6gcAAAAJ&amp;sortby=pubdate&amp;citation_for_view=jXxk6gcAAAAJ:M3NEmzRMIkIC\"><strong>Unsupervised Acoustic Scene Mapping Based on Acoustic Features and Dimensionality Reduction<\/strong><\/a>.\" <strong> <\/strong><em>ICASSP 2024.<\/em><\/li>\n\n\n\n<li>B. Battash, L. Wolf, <strong>O. Lindenbaum<\/strong>, \"<strong><a href=\"https:\/\/openreview.net\/forum?id=01eUekTYvE\">Revisiting the Noise Model of SGD<\/a><\/strong>\".<em> NeurIPS 2023 Workshop Heavy Tails in Machine Learning 2023.<\/em><\/li>\n\n\n\n<li>J. Yang, <strong>O. Lindenbaum<\/strong>, Y. Kluger, A. Jaffe, \"<a href=\"https:\/\/scholar.google.co.il\/citations?view_op=view_citation&amp;hl=en&amp;user=jXxk6gcAAAAJ&amp;sortby=pubdate&amp;citation_for_view=jXxk6gcAAAAJ:hMod-77fHWUC\"><strong>Multi-modal Differentiable Unsupervised Feature Selection<\/strong><\/a>,\" <em>UAI 2023.<\/em><\/li>\n\n\n\n<li>T. Yamplosky, R. Talmon, <strong>O. Lindenbaum<\/strong>, \"<a href=\"https:\/\/ieeexplore.ieee.org\/stamp\/stamp.jsp?arnumber=10290039\"><strong>Domain and Modality Adaptation Using Multi-Kernel Matching,<\/strong><\/a>\" <em>EUSIPCO 2023.<\/em><\/li>\n\n\n\n<li>J. Svirsky, <strong>O. Lindenbaum<\/strong>, <a href=\"https:\/\/ieeexplore.ieee.org\/abstract\/document\/10096938\/?casa_token=wLEJDoHH0-0AAAAA:Bs9yFxgmiuvI0QpI1aQl4DoMT9q6qmo55rES4Q94BkvkTmGnKIfSSrp4HciYzGVe6xd_cffDO1bd\"> <\/a><strong><a href=\"https:\/\/ieeexplore.ieee.org\/abstract\/document\/10096938\/?casa_token=wLEJDoHH0-0AAAAA:Bs9yFxgmiuvI0QpI1aQl4DoMT9q6qmo55rES4Q94BkvkTmGnKIfSSrp4HciYzGVe6xd_cffDO1bd\">\"SG-VAD: Stochastic Gates Based Speech Activity Detection<\/a>,\" <\/strong><em>ICASSP 2023.<\/em><\/li>\n\n\n\n<li>J. Yang*<strong>, O. Lindenbaum*, <\/strong>Y. Kluger, \"<strong><a href=\"https:\/\/proceedings.mlr.press\/v162\/yang22i.html\">Locally Sparse Neural Networks for Tabular Biomedical Data<\/a>\"<\/strong>. <em>ICML 2022<\/em>.<\/li>\n\n\n\n<li><strong>O. Lindenbaum<\/strong>, M. Salhov, A. Averbuch, Y. Kluger. \"<a href=\"https:\/\/arxiv.org\/abs\/2010.05620\"><strong>L0-Sparse Canonical Correlation Analysis<\/strong><\/a>\". <em>ICLR 2022.<\/em>&nbsp;<\/li>\n\n\n\n<li><strong>O. Lindenbaum*<\/strong>, U. Shaham*, J. Svirsky, E. Peterfreund, Y. Kluger, \"<a href=\"https:\/\/arxiv.org\/pdf\/2007.04728.pdf\"><strong>Differentiable Unsupervised Feature Selection based on a Gated Laplacian<\/strong><\/a>.\" <em>NeurIPS 2021.<\/em><\/li>\n\n\n\n<li>H. Li*, <strong>O. Lindenbaum*<\/strong>, X. Cheng, A. Cloninger, <span style=\"color:#bc360a\" class=\"tadv-color\">\"<a href=\"https:\/\/arxiv.org\/abs\/1905.12724\"><strong>Variational Diffusion Autoencoders with Random Walk Sampling \"<\/strong><\/a><\/span>, <em>European Conference on Computer Vision (ECCV) 2020.&nbsp;<\/em><\/li>\n\n\n\n<li>Y. Yamada*, <strong>O. Lindenbaum*<\/strong>, S. Negahban, Y. Kluger, \"<a href=\"https:\/\/arxiv.org\/abs\/1810.04247\"><strong>Feature selection using Stochastic Gates\"<\/strong>,<\/a>&nbsp; <em>International Conference on Machine Learning (ICML) 2020.<\/em><\/li>\n\n\n\n<li>&nbsp;<strong>O. Lindenbaum<\/strong>*,&nbsp; Jay S. Stanley III*, Guy Wolf, Smita Krishnaswamy, \"<a href=\"https:\/\/arxiv.org\/abs\/1802.04927\"><span class=\"tadv-color\" style=\"color:#bc360a\"><strong>Geometry-Based Data Generation<\/strong><\/span><\/a><strong><span class=\"tadv-color\" style=\"color:#bc360a\">\",<\/span> <\/strong><em>Conference on Neural Information Processing Systems (NeurIPS) 2018. (spotlight ~4% acceptance rate) .<\/em><\/li>\n\n\n\n<li><strong>O. Lindenbaum<\/strong>, N. Rabin, Y. Bregman, A. Averbuch, <span class=\"tadv-color\" style=\"color:#bc360a\">\"<strong>Multi-channel fusion for seismic event detection and classification<\/strong>\"<\/span>, <em>IEEE International Conference on the Science of Electrical Engineering (ICSEE), 2016.<\/em><\/li>\n\n\n\n<li><strong>O. Lindenbaum<\/strong>, A. Yeredor, A. Averbuch, \"<a href=\"https:\/\/d59ca680-d3ab-4e2c-9142-7cfc721a9b19.filesusr.com\/ugd\/4da382_b137a27fd2c04c01b21d3bd0d39db871.pdf\"><strong>Clustering Based on MultiView Diffusion Maps<\/strong><\/a><strong>\"<\/strong>, <em>IEEE 16th International Conference on Data Mining Workshops (ICDMW), 2016.&nbsp;<\/em><\/li>\n\n\n\n<li><strong>O. Lindenbaum<\/strong>, A. Yeredor, A. Averbuch, <span style=\"color:#bc360a\" class=\"tadv-color\">\"<strong>Bandwidth selection for kernel-based classification\"<\/strong><\/span><strong>, <\/strong><em>IEEE International Conference on the Science of Electrical Engineering (ICSEE), 2016.<\/em><\/li>\n\n\n\n<li>A. Averbuch, M. Salhov, <strong>O. Lindenbaum<\/strong>, A. Silberschatz, Y. Shkolnisky, <span style=\"color:#bc360a\" class=\"tadv-color\">\"<strong>Multi-view kernel-based data analysis\u200f\"<\/strong><\/span><strong>,<\/strong> <em>IEEE International Conference on the Science of Electrical Engineering (ICSEE), 2016.<\/em><\/li>\n\n\n\n<li><strong>O. Lindenbaum<\/strong>, A. Yeredor, M. Salhov, \"<a href=\"https:\/\/d59ca680-d3ab-4e2c-9142-7cfc721a9b19.filesusr.com\/ugd\/4da382_476b0a99bfee4478b6ffe01cdcfb0f0a.pdf\"><strong>Learning coupled embedding using multiview diffusion maps\",<\/strong><\/a> <em>International Conference on Latent Variable Analysis and Signal Separation, 2015.<\/em><\/li>\n\n\n\n<li>O. Lindenbaum, A. Yeredor, R. Vitek, M. Mishali, \"<a href=\"https:\/\/d59ca680-d3ab-4e2c-9142-7cfc721a9b19.filesusr.com\/ugd\/4da382_8652704b938349fb9429977d7bcc3255.pdf\"><strong>Blind separation of spatially-block-sparse sources from orthogonal mixtures\",<\/strong><\/a> <em>IEEE.&nbsp; International Workshop on Machine Learning for Signal Processing (MLSP), 2013.<\/em><\/li>\n\n\n\n<li>O. Lindenbaum, S. Maskit, O. Kutiel, G. Nave. \"<a href=\"https:\/\/d59ca680-d3ab-4e2c-9142-7cfc721a9b19.filesusr.com\/ugd\/4da382_f5cd876033064a4ab9f5674ecfd2b80e.pdf\"><strong>Musical features extraction for audio-based search\",<\/strong><\/a> <em>IEEE 26th Convention of Electrical and Electronics Engineers in Israel (IEEEI), 2010.<\/em><\/li>\n<\/ul>\n\n\n\n<p style=\"font-size:30px\">Journal Papers<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>K. Rahimi, T. Tirer, <strong>O. Lindenbaum,<\/strong> \"<a href=\"https:\/\/scholar.google.co.il\/citations?view_op=view_citation&amp;hl=iw&amp;user=jXxk6gcAAAAJ&amp;sortby=pubdate&amp;citation_for_view=jXxk6gcAAAAJ:HoB7MX3m0LUC\">Multiple Descents in Unsupervised Learning: The Role of Noise, Domain Shift and Anomalies.\"<\/a> <em>Transactions on Machine Learning Research 2025.<\/em><\/li>\n\n\n\n<li>T. Konstantinovsky, A. Peres, R. Eisenberg, P. Polak, <strong>O. Lindenbaum<\/strong>, Gur Yaari, \u201c<a href=\"https:\/\/academic.oup.com\/nar\/article-abstract\/53\/13\/gkaf651\/8198040\">Enhancing sequence alignment of adaptive immune receptors through multi-task deep learning<\/a>.\u201d Nucleic Acid Research, 2025.<\/li>\n\n\n\n<li>I. Cohen, S. Gannot, <strong>O. Lindenbaum<\/strong>, \u201c<a href=\"https:\/\/ieeexplore.ieee.org\/abstract\/document\/11080057\">Synthetic Aperture Local Conformal Autoencoder for Semi-Supervised Speaker\u2019s DOA Tracking<\/a>.\u201d IEEE Transactions on Audio, Speech and Language Processing, 2025.<\/li>\n\n\n\n<li>A. Yaakobi, O. Lindenbaum, U. Shaham,<strong> \"<a href=\"https:\/\/openreview.net\/forum?id=SNNdmfqWFu\">SpecRaGE: Robust and Generalizable Multi-view Spectral Representation Learning<\/a><\/strong>.\" <em>Transactions on Machine Learning Research 2025.<\/em><\/li>\n\n\n\n<li>D. Segal, <strong>O. Lindenbaum<\/strong>, A. Jaffe, \"<a href=\"https:\/\/arxiv.org\/abs\/2407.09061\"><strong>Spectral Self-supervised Feature Selection<\/strong><\/a>.\" <em>Transactions on Machine Learning Research 2024.<\/em><\/li>\n\n\n\n<li>A. Rozner, B. Battash, L. Wolf, <strong>O. Lindenbaum<\/strong>, \"<strong><a href=\"https:\/\/openreview.net\/pdf?id=O9RUANpPmb\">Domain-Generalizable Multiple-Domain Clustering<\/a><\/strong>.\" Transactions on Machine Learning Research 2024. <\/li>\n\n\n\n<li>S. Jana,&nbsp;H. Li,&nbsp;Y. Yamada,&nbsp;<strong>O. Lindenbaum<\/strong>, \"<a href=\"https:\/\/arxiv.org\/abs\/2110.15960\"><strong>Support Recovery with Stochastic Gates: Theory and Application for Linear Models\"<\/strong><\/a>. <em>Elsevier Signal Processing, 2023.<\/em><\/li>\n\n\n\n<li>S. Farhadian, <strong>O. Lindenbaum<\/strong>, 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. <strong><a href=\"https:\/\/insight.jci.org\/articles\/view\/160267\">\"HIV viral transcription and immune perturbations in the CNS of people with HIV despite ART\"<\/a>,<\/strong> JCI insight, 2022<\/li>\n\n\n\n<li>U. Shaham*,&nbsp;<strong>O. Lindenbaum<\/strong>*,&nbsp;J. Svirsky,&nbsp;Y. Kluger, \"<a href=\"https:\/\/arxiv.org\/abs\/2110.05306\"><strong>Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Features<\/strong><\/a>\". <em>Elsevier neural networks, 2022.<\/em><\/li>\n\n\n\n<li><strong>O. Lindenbaum<\/strong>, S. Steinerberger, <strong>\"<\/strong><a href=\"https:\/\/arxiv.org\/abs\/2103.10949\"><strong>Refined Least Squares for Support Recovery<\/strong><\/a><strong>\"<\/strong>. <em>Elsevier Journal of Signal Processing, 2022.<\/em><\/li>\n\n\n\n<li>L. Irshaid, J. Bleiberg, E Weinberger, J. Garritanod, R. M. Shallis, J. Patsenker, <strong>O. Lindenbaum<\/strong>, Y. Kluger, S. G. Katz, M. L. Xu, \"<a href=\"https:\/\/meridian.allenpress.com\/aplm\/article\/doi\/10.5858\/arpa.2020-0510-OA\/466162\/Histopathologic-and-Machine-Deep-Learning-Criteria\"><strong>Histopathologic and Machine Deep Learning Criteria to Predict Lymphoma Transformation in Bone Marrow Biopsies<\/strong><\/a>\", &nbsp; Archives of Pathology and Laboratory Medicine<em>, 2021.&nbsp;<\/em><\/li>\n\n\n\n<li>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.&nbsp;&nbsp;&nbsp;&nbsp;<\/li>\n\n\n\n<li><strong>O. Lindenbaum<\/strong>*, A. Sagiv*, G. Mishne, R. Talmon, \"<a href=\"https:\/\/arxiv.org\/abs\/2009.04142\"><strong>Kernel-based parameter estimation of dynamical systems with unknown observation functions\", <\/strong><\/a><strong>&nbsp;<\/strong><em>Chaos: An Interdisciplinary Journal of Nonlinear Science, 2021.<\/em>&nbsp;<\/li>\n\n\n\n<li><strong>O. Lindenbaum<\/strong>, S. Steinerberger, \"<a href=\"https:\/\/arxiv.org\/abs\/2003.07331\"><strong>Randomly Aggregated Least Squares for Support Recovery\",<\/strong><\/a><strong> <\/strong><em>Elsevier Journal of Signal Processing, 2020.<\/em> &nbsp; <a href=\"https:\/\/arxiv.org\/abs\/2003.07331\">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<\/a>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<\/li>\n\n\n\n<li>&nbsp;<strong>O. Lindenbaum<\/strong>,&nbsp; N. Nouri, Y. Kluger, S. H. Kleinstein, \"<a href=\"https:\/\/www.biorxiv.org\/content\/10.1101\/2020.03.30.017384v1\"><strong>Alignment free identification of clones in B cell receptor repertoires\",<\/strong><\/a><strong> <\/strong><em>Nucleic Acid Research (NAR),&nbsp; 2020.\u200b<\/em> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<\/li>\n\n\n\n<li>A. Jaffe, Y. Kluger, <strong>O. Lindenbaum<\/strong>, J. Patsenker, E.Peterfreund, S. Steinerberger, \"<a href=\"https:\/\/arxiv.org\/abs\/2002.12317\"><strong>The Spectral Underpinning of word2vec<\/strong><\/a><strong>\",&nbsp; <\/strong><em>Frontiers in Applied Mathematics and Statistics, 2020.<\/em><strong> <\/strong>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<\/li>\n\n\n\n<li>E. Peterfreund*, <strong>O. Lindenbaum*<\/strong>, F. Dietrich, T. Bertalan, M. Gavish, I. G. Kevrekidis, R. R. Coifman, \"<a href=\"https:\/\/www.pnas.org\/content\/early\/2020\/11\/20\/2014627117\"><strong>LOcal Conformal Autoencoder for standardized data coordinates\",<\/strong><\/a> <em>Proceedings of the national academy of sciences (PNAS), 2020.&nbsp;<\/em><\/li>\n\n\n\n<li><strong>O. Lindenbaum<\/strong>, M. Salhov, A. Yeredor, A. Averbuch, \"<a href=\"https:\/\/arxiv.org\/abs\/1707.01093\"><strong>Gaussian Bandwidth Selection for Manifold Learning and Classification\"<\/strong><\/a> , <em>Data Mining and Knowledge Discovery, 2020.<\/em><\/li>\n\n\n\n<li>Y. Bregman, <strong>O. Lindenbaum<\/strong>, N. Rabin, \"<a href=\"https:\/\/link.springer.com\/article\/10.1007\/s00024-020-02452-w\"><strong>Array-Based Earthquakes-Explosion Discrimination Using Diffusion Maps<\/strong><\/a><strong>\"<\/strong>. <em>Pure and Applied Geophysics, 2020.<\/em><\/li>\n\n\n\n<li><span style=\"margin: 0px;padding: 0px\"><strong>O. Lindenbaum<\/strong>, A. Yeredor, M. Salhov,&nbsp; A. Averbuch,<a href=\"https:\/\/arxiv.org\/abs\/1508.05550\" target=\"_blank\" rel=\"noopener\">&nbsp;<\/a><a href=\"https:\/\/arxiv.org\/abs\/1508.05550\" target=\"_blank\" rel=\"noopener\"><strong>\"Multiview diffusion maps\",<\/strong><\/a>&nbsp;<em>Information Fusion, 2020.<\/em><\/span><\/li>\n\n\n\n<li><strong>O. Lindenbaum<\/strong>, N. Rabin, Y. Bregman, A. Averbuch, <span style=\"color:#bc360a\" class=\"tadv-color\">\"<strong>Seismic Event Discrimination Using Deep CCA\"<\/strong><\/span><strong>,<\/strong> <em>IEEE Geoscience and Remote Sensing Letters, 2019<\/em><\/li>\n\n\n\n<li>M. Salhov, <strong>O. Lindenbaum<\/strong>, Y. Aizenbud, A. Silberschatz, Y. Shkolnisky, A. Averbuch, \"<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1063520318300952\"><strong>Multi-view kernel consensus for data analysis and signal processing<\/strong><\/a><strong>\",<\/strong> <em>Applied and Computational Harmonics Analysis (ACHA), 2019.<\/em><\/li>\n\n\n\n<li><strong>O. Lindenbaum<\/strong>, Y. Bregman, N. Rabin, A. Averbuch. \"<a href=\"https:\/\/d59ca680-d3ab-4e2c-9142-7cfc721a9b19.filesusr.com\/ugd\/4da382_f64beb2094e247b29d84f4dbe0bd9b93.pdf\"><strong>Multi-View Kernels for Low-Dimensional Modeling of Seismic Events<\/strong><\/a><strong>\".<\/strong> <em>IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2018.<\/em><\/li>\n\n\n\n<li>N. N. Rabin, Y. Bregman, <strong>O. Lindenbaum<\/strong>, Y. Ben-Horin, A. Averbuch\u200f, \"<a href=\"https:\/\/d59ca680-d3ab-4e2c-9142-7cfc721a9b19.filesusr.com\/ugd\/4da382_49ac1482953f41e4a0097246036e83dc.pdf\"><strong>Earthquake-explosion discrimination using diffusion maps<\/strong><\/a><strong>\",<\/strong> <em>Geophysical Journal International 207 (3), 1484-1492, \u200f. 2016<\/em><\/li>\n\n\n\n<li><strong>O. Lindenbaum<\/strong>, A. Yeredor, Y. Cohen, \"<a href=\"https:\/\/d59ca680-d3ab-4e2c-9142-7cfc721a9b19.filesusr.com\/ugd\/4da382_f599dd5e271342fc9159f86394b20325.pdf\"><strong>Musical key extraction using diffusion maps\"<\/strong><\/a>, <em>Signal Processing 117, 198-207, 2015.<\/em><\/li>\n\n\n\n<li>&nbsp;<strong>O. Lindenbaum<\/strong>, A. Yeredor, R. Vitek, M. Mishali, \"<a href=\"https:\/\/d59ca680-d3ab-4e2c-9142-7cfc721a9b19.filesusr.com\/ugd\/4da382_ca0d7a4a15924ea4832ecb72e7b71c31.pdf\"><strong>Blind Separation of Orthogonal Mixtures of Spatially-Sparse Sources with Unknown &nbsp; Sparsity Levels and with Temporal Blocks<\/strong><\/a>\". <em>Journal of Signal Processing Systems 79 (2), 167-178, 2015.<\/em><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Preprints \u200b* indicates equal contribution Conference and Workshops Journal Papers<\/p>\n","protected":false},"author":93,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-17","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.eng.biu.ac.il\/lindeno\/wp-json\/wp\/v2\/pages\/17"}],"collection":[{"href":"https:\/\/www.eng.biu.ac.il\/lindeno\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.eng.biu.ac.il\/lindeno\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.eng.biu.ac.il\/lindeno\/wp-json\/wp\/v2\/users\/93"}],"replies":[{"embeddable":true,"href":"https:\/\/www.eng.biu.ac.il\/lindeno\/wp-json\/wp\/v2\/comments?post=17"}],"version-history":[{"count":51,"href":"https:\/\/www.eng.biu.ac.il\/lindeno\/wp-json\/wp\/v2\/pages\/17\/revisions"}],"predecessor-version":[{"id":300,"href":"https:\/\/www.eng.biu.ac.il\/lindeno\/wp-json\/wp\/v2\/pages\/17\/revisions\/300"}],"wp:attachment":[{"href":"https:\/\/www.eng.biu.ac.il\/lindeno\/wp-json\/wp\/v2\/media?parent=17"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}