Blind separation of spatially-block-sparse sources from orthogonal mixtures. - Ofir Lindenbaum; Arie Yeredor; Ran Vitek; Moshe Mishali (MLSP, 2013)

Learning Coupled Embedding Using MultiView Diffusion Maps. - Ofir Lindenbaum; Arie Yeredor; Moshe Salhov (LVA/ICA, 2015)

MultiView Diffusion Maps. - Ofir Lindenbaum; Arie Yeredor; Moshe Salhov; Amir Averbuch (CoRR, 2015)

Musical key extraction using diffusion maps. - Ofir Lindenbaum; Arie Yeredor; Israel Cohen (Signal Process., 2015)

Blind Separation of Orthogonal Mixtures of Spatially-Sparse Sources with Unknown Sparsity Levels and with Temporal Blocks. - Ofir Lindenbaum; Arie Yeredor; Ran Vitek; Moshe Mishali (J. Signal Process. Syst., 2015)

Clustering Based on MultiView Diffusion Maps. - Ofir Lindenbaum; Arie Yeredor; Amir Averbuch (ICDM Workshops, 2016)

Multi-View Kernel Consensus For Data Analysis and Signal Processing. - Moshe Salhov; Ofir Lindenbaum; Avi Silberschatz; Yoel Shkolnisky; Amir Averbuch (CoRR, 2016)

Multi-View Kernels for Low-Dimensional Modeling of Seismic Events. - Ofir Lindenbaum; Yuri Bregman; Neta Rabin; Amir Averbuch (CoRR, 2017)

Kernel Scaling for Manifold Learning and Classification. - Ofir Lindenbaum; Moshe Salhov; Arie Yeredor; Amir Averbuch (CoRR, 2017)

Geometry Based Data Generation. - Ofir Lindenbaum; Jay S. Stanley III; Guy Wolf; Smita Krishnaswamy (NeurIPS, 2018)

Deep supervised feature selection using Stochastic Gates. - Yutaro Yamada; Ofir Lindenbaum; Sahand Negahban; Yuval Kluger (CoRR, 2018)

Multiview Kernels for Low-Dimensional Modeling of Seismic Events. - Ofir Lindenbaum; Yuri Bregman; Neta Rabin; Amir Averbuch (IEEE Trans. Geosci. Remote. Sens., 2018)

Diffusion Variational Autoencoders. - Henry Li; Ofir Lindenbaum; Xiuyuan Cheng; Alexander Cloninger (CoRR, 2019)

Variational Diffusion Autoencoders with Random Walk Sampling. - Henry Li; Ofir Lindenbaum; Xiuyuan Cheng; Alexander Cloninger (ECCV (23), 2020)

Feature Selection using Stochastic Gates. - Yutaro Yamada; Ofir Lindenbaum; Sahand Negahban; Yuval Kluger (ICML, 2020)

LOCA: LOcal Conformal Autoencoder for standardized data coordinates. - Erez Peterfreund; Ofir Lindenbaum; Felix Dietrich; Tom Bertalan; Matan Gavish; Ioannis G. Kevrekidis; Ronald R. Coifman (CoRR, 2020)

Let the Data Choose its Features: Differentiable Unsupervised Feature Selection. - Ofir Lindenbaum; Uri Shaham 001; Jonathan Svirsky; Erez Peterfreund; Yuval Kluger (CoRR, 2020)

Kernel-based parameter estimation of dynamical systems with unknown observation functions. - Ofir Lindenbaum; Amir Sagiv; Gal Mishne; Ronen Talmon (CoRR, 2020)

Deep Gated Canonical Correlation Analysis. - Ofir Lindenbaum; Moshe Salhov; Amir Averbuch; Yuval Kluger (CoRR, 2020)

Gaussian bandwidth selection for manifold learning and classification. - Ofir Lindenbaum; Moshe Salhov; Arie Yeredor; Amir Averbuch (Data Min. Knowl. Discov., 2020)

The Spectral Underpinning of word2vec. - Ariel Jaffe; Yuval Kluger; Ofir Lindenbaum; Jonathan Patsenker; Erez Peterfreund; Stefan Steinerberger (Frontiers Appl. Math. Stat., 2020)

Multi-view diffusion maps. - Ofir Lindenbaum; Arie Yeredor; Moshe Salhov; Amir Averbuch (Inf. Fusion, 2020)

Seismic Event Discrimination Using Deep CCA. - Ofir Lindenbaum; Neta Rabin; Yuri Bregman; Amir Averbuch (IEEE Geosci. Remote. Sens. Lett., 2020)

Differentiable Unsupervised Feature Selection based on a Gated Laplacian. - Ofir Lindenbaum; Uri Shaham 001; Erez Peterfreund; Jonathan Svirsky; Nicolas Casey; Yuval Kluger (NeurIPS, 2021)

Locally Sparse Networks for Interpretable Predictions. - Junchen Yang; Ofir Lindenbaum; Yuval Kluger (CoRR, 2021)

Probabilistic Robust Autoencoders for Anomaly Detection. - Yariv Aizenbud; Ofir Lindenbaum; Yuval Kluger (CoRR, 2021)

Support Recovery with Stochastic Gates: Theory and Application for Linear Models. - Soham Jana; Henry Li; Yutaro Yamada; Ofir Lindenbaum (CoRR, 2021)

Randomly aggregated least squares for support recovery. - Ofir Lindenbaum; Stefan Steinerberger (Signal Process., 2021)

L0-Sparse Canonical Correlation Analysis. - Ofir Lindenbaum; Moshe Salhov; Amir Averbuch; Yuval Kluger (ICLR, 2022)

Locally Sparse Neural Networks for Tabular Biomedical Data. - Junchen Yang; Ofir Lindenbaum; Yuval Kluger (ICML, 2022)

Imbalanced Classification via a Tabular Translation GAN. - Jonathan Gradstein; Moshe Salhov; Yoav Tulpan; Ofir Lindenbaum; Amir Averbuch (CoRR, 2022)

Deep unsupervised feature selection by discarding nuisance and correlated features. - Uri Shaham 001; Ofir Lindenbaum; Jonathan Svirsky; Yuval Kluger (Neural Networks, 2022)

Refined least squares for support recovery. - Ofir Lindenbaum; Stefan Steinerberger (Signal Process., 2022)

Domain and Modality Adaptation Using Multi-Kernel Matching. - Tamir Baruch Yampolsky; Ronen Talmon; Ofir Lindenbaum (EUSIPCO, 2023)

Neuronal Cell Type Classification Using Locally Sparse Networks. - Ofek Ophir; Orit Shefi; Ofir Lindenbaum (ICASSP Workshops, 2023)

SG-VAD: Stochastic Gates Based Speech Activity Detection. - Jonathan Svirsky; Ofir Lindenbaum (ICASSP, 2023)

Multi-modal differentiable unsupervised feature selection. - Junchen Yang; Ofir Lindenbaum; Yuval Kluger; Ariel Jaffe (UAI, 2023)

Revisiting the Noise Model of Stochastic Gradient Descent. - Barak Battash; Ofir Lindenbaum (CoRR, 2023)

Neuronal Cell Type Classification using Deep Learning. - Ofek Ophir; Orit Shefi; Ofir Lindenbaum (CoRR, 2023)

Interpretable Deep Clustering. - Jonathan Svirsky; Ofir Lindenbaum (CoRR, 2023)

TabADM: Unsupervised Tabular Anomaly Detection with Diffusion Models. - Guy Zamberg; Moshe Salhov; Ofir Lindenbaum; Amir Averbuch (CoRR, 2023)

Gappy local conformal auto-encoders for heterogeneous data fusion: in praise of rigidity. - Erez Peterfreund; Iryna Burak; Ofir Lindenbaum; Jim Gimlett; Felix Dietrich; Ronald R. Coifman; Ioannis G. Kevrekidis (CoRR, 2023)

Contextual Feature Selection with Conditional Stochastic Gates. - Ram Dyuthi Sristi; Ofir Lindenbaum; Maria Lavzin; Jackie Schiller; Gal Mishne; Hadas Benisty (CoRR, 2023)

Support recovery with Projected Stochastic Gates: Theory and application for linear models. - Soham Jana; Henry Li; Yutaro Yamada; Ofir Lindenbaum (Signal Process., 2023)

Revisiting the Noise Model of Stochastic Gradient Descent. - Barak Battash; Lior Wolf; Ofir Lindenbaum (AISTATS, 2024)

Knowledge Editing in Language Models via Adapted Direct Preference Optimization. - Amit Rozner; Barak Battash; Lior Wolf; Ofir Lindenbaum (EMNLP (Findings), 2024)

Efficient Verification-Based Face Identification. - Amit Rozner; Barak Battash; Ofir Lindenbaum; Lior Wolf (FG, 2024)

Unsupervised Acoustic Scene Mapping Based on Acoustic Features and Dimensionality Reduction. - Idan Cohen; Sharon Gannot; Ofir Lindenbaum (ICASSP, 2024)

Contextual Feature Selection with Conditional Stochastic Gates. - Ram Dyuthi Sristi; Ofir Lindenbaum; Shira Lifshitz; Maria Lavzin; Jackie Schiller; Gal Mishne; Hadas Benisty (ICML, 2024)

Interpretable Deep Clustering for Tabular Data. - Jonathan Svirsky; Ofir Lindenbaum (ICML, 2024)

Sparse Binarization for Fast Keyword Spotting. - Jonathan Svirsky; Uri Shaham 001; Ofir Lindenbaum (INTERSPEECH, 2024)

Transductive and Inductive Outlier Detection with Robust Autoencoders. - Ofir Lindenbaum; Yariv Aizenbud; Yuval Kluger (UAI, 2024)

Anomaly Detection with Variance Stabilized Density Estimation. - Amit Rozner; Barak Battash; Henry Li; Lior Wolf; Ofir Lindenbaum (UAI, 2024)

Self Supervised Correlation-based Permutations for Multi-View Clustering. - Ran Eisenberg; Jonathan Svirsky; Ofir Lindenbaum (CoRR, 2024)

Obtaining Favorable Layouts for Multiple Object Generation. - Barak Battash; Amit Rozner; Lior Wolf; Ofir Lindenbaum (CoRR, 2024)

SEL-CIE: Knowledge-Guided Self-Supervised Learning Framework for CIE-XYZ Reconstruction from Non-Linear sRGB Images. - Shir Barzel; Moshe Salhov; Ofir Lindenbaum; Amir Averbuch (CoRR, 2024)

Multiple Descents in Unsupervised Learning: The Role of Noise, Domain Shift and Anomalies. - Kobi Rahimi; Tom Tirer; Ofir Lindenbaum (CoRR, 2024)

TransformLLM: Adapting Large Language Models via LLM-Transformed Reading Comprehension Text. - Iftach Arbel; Yehonathan Refael; Ofir Lindenbaum (CoRR, 2024)

SpecRaGE: Robust and Generalizable Multi-view Spectral Representation Learning. - Amitai Yacobi; Ofir Lindenbaum; Uri Shaham 001 (CoRR, 2024)

FineGates: LLMs Finetuning with Compression using Stochastic Gates. - Jonathan Svirsky; Yehonathan Refael; Ofir Lindenbaum (CoRR, 2024)

Domain-Generalizable Multiple-Domain Clustering. - Amit Rozner; Barak Battash; Lior Wolf; Ofir Lindenbaum (Trans. Mach. Learn. Res., 2024)

Spectral Self-supervised Feature Selection. - Daniel Segal; Ofir Lindenbaum; Ariel Jaffe (Trans. Mach. Learn. Res., 2024)

Classifying Neuronal Cell Types Based on Shared Electrophysiological Information from Humans and Mice. - Ofek Ophir; Orit Shefi; Ofir Lindenbaum (Neuroinformatics, 2024)

Conditional Deep Canonical Time Warping. - Ran Eisenberg; Afek Steinberg; Ofir Lindenbaum (ICASSP, 2025)

COPER: Correlation-based Permutations for Multi-View Clustering. - Ran Eisenberg; Jonathan Svirsky; Ofir Lindenbaum (ICLR, 2025)

AdaRankGrad: Adaptive Gradient Rank and Moments for Memory-Efficient LLMs Training and Fine-Tuning. - Yehonathan Refael; Jonathan Svirsky; Boris Shustin; Wasim Huleihel; Ofir Lindenbaum (ICLR, 2025)

Generalizable and Robust Spectral Method for Multi-view Representation Learning. - Amitai Yacobi; Ofir Lindenbaum; Uri Shaham 001 (Trans. Mach. Learn. Res., 2025)

LORENZA: Enhancing Generalization in Low-Rank Gradient LLM Training via Efficient Zeroth-Order Adaptive SAM. - Yehonathan Refael; Iftach Arbel; Ofir Lindenbaum; Tom Tirer (CoRR, 2025)

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels. - Yuval Grinberg; Nimrod Harel; Jacob Goldberger; Ofir Lindenbaum (CoRR, 2025)

SUMO: Subspace-Aware Moment-Orthogonalization for Accelerating Memory-Efficient LLM Training. - Yehonathan Refael; Guy Smorodinsky; Tom Tirer; Ofir Lindenbaum (CoRR, 2025)