SPINE: an integrated tracking database and data mining approach for identifying feasible targets in high-throughput structural proteomics. - Paul Bertone; Yuval Kluger; Ning Lan; Deyou Zheng; Dinesh Christendat; Adelinda Yee; Aled M. Edwards; Cheryl H. Arrowsmith; Gaetano T. Montelione; Mark Gerstein (Nucleic Acids Res., 2001)

Association between pathways in regulatory networks. - Yuval Kluger; Harriet Kluger; David Tuck (EMBC, 2006)

Unraveling condition specific gene transcriptional regulatory networks in Saccharomyces cerevisiae. - Hyunsoo Kim; William Hu; Yuval Kluger (BMC Bioinform., 2006)

Characterizing disease states from topological properties of transcriptional regulatory networks. - David Tuck; Harriet Kluger; Yuval Kluger (BMC Bioinform., 2006)

Inter- and Intra-Combinatorial Regulation by Transcriptions Factors and MicroRNAs. - Yiming Zhou; John Ferguson; Joseph T. Chang; Yuval Kluger (BIOCOMP, 2007)

The student's dilemma: ranking and improving prediction at test time without access to training data - Fabio Parisi; Francesco Strino; Boaz Nadler; Yuval Kluger (CoRR, 2013)

An implementation of a randomized algorithm for principal component analysis. - Arthur Szlam; Yuval Kluger; Mark Tygert (CoRR, 2014)

Estimating the accuracies of multiple classifiers without labeled data. - Ariel Jaffe; Boaz Nadler; Yuval Kluger (AISTATS, 2015)

Unsupervised Ensemble Learning with Dependent Classifiers. - Ariel Jaffe; Ethan Fetaya; Boaz Nadler; Tingting Jiang; Yuval Kluger (AISTATS, 2016)

A Deep Learning Approach to Unsupervised Ensemble Learning. - Uri Shaham 001; Xiuyuan Cheng; Omer Dror; Ariel Jaffe; Boaz Nadler; Joseph T. Chang; Yuval Kluger (ICML, 2016)

Deep Survival: A Deep Cox Proportional Hazards Network. - Jared Katzman; Uri Shaham 001; Alexander Cloninger; Jonathan Bates; Tingting Jiang; Yuval Kluger (CoRR, 2016)

Gating mass cytometry data by deep learning. - Huamin Li; Uri Shaham 001; Kelly P. Stanton; Yi Yao; Ruth R. Montgomery; Yuval Kluger (Bioinform., 2017)

Removal of batch effects using distribution-matching residual networks. - Uri Shaham 001; Kelly P. Stanton; Jun Zhao; Huamin Li; Khadir Raddassi; Ruth R. Montgomery; Yuval Kluger (Bioinform., 2017)

Unsupervised Ensemble Regression. - Omer Dror; Boaz Nadler; Erhan Bilal; Yuval Kluger (CoRR, 2017)

Randomized Near Neighbor Graphs, Giant Components, and Applications in Data Science. - George C. Linderman; Gal Mishne; Yuval Kluger; Stefan Steinerberger (CoRR, 2017)

Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding. - George C. Linderman; Manas Rachh; Jeremy G. Hoskins; Stefan Steinerberger; Yuval Kluger (CoRR, 2017)

Algorithm 971: An Implementation of a Randomized Algorithm for Principal Component Analysis. - Huamin Li; George C. Linderman; Arthur Szlam; Kelly P. Stanton; Yuval Kluger; Mark Tygert (ACM Trans. Math. Softw., 2017)

SpectralNet: Spectral Clustering using Deep Neural Networks. - Uri Shaham 001; Kelly P. Stanton; Henry Li; Ronen Basri; Boaz Nadler; Yuval Kluger (ICLR (Poster), 2018)

Learning Binary Latent Variable Models: A Tensor Eigenpair Approach. - Ariel Jaffe; Roi Weiss; Shai Carmi; Yuval Kluger; Boaz Nadler (ICML, 2018)

Randomized algorithms for distributed computation of principal component analysis and singular value decomposition. - Huamin Li; Yuval Kluger; Mark Tygert (Adv. Comput. Math., 2018)

Defending against Adversarial Images using Basis Functions Transformations. - Uri Shaham 001; James Garritano; Yutaro Yamada; Ethan Weinberger; Alex Cloninger; Xiuyuan Cheng; Kelly P. Stanton; Yuval Kluger (CoRR, 2018)

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

Data-Driven Tree Transforms and Metrics. - Gal Mishne; Ronen Talmon; Israel Cohen; Ronald R. Coifman; Yuval Kluger (IEEE Trans. Signal Inf. Process. over Networks, 2018)

Heavy-Tailed Kernels Reveal a Finer Cluster Structure in t-SNE Visualisations. - Dmitry Kobak; George C. Linderman; Stefan Steinerberger; Yuval Kluger; Philipp Berens (ECML/PKDD (1), 2019)

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

Spectral neighbor joining for reconstruction of latent tree models. - Ariel Jaffe; Noah Amsel; Boaz Nadler; Joseph T. Chang; Yuval Kluger (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)

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

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

Randomized near-neighbor graphs, giant components and applications in data science. - Ariel Jaffe; Yuval Kluger; George C. Linderman; Gal Mishne; Stefan Steinerberger (J. Appl. Probab., 2020)

Hyperbolic Procrustes Analysis Using Riemannian Geometry. - Ya-Wei Eileen Lin; Yuval Kluger; Ronen Talmon (NeurIPS, 2021)

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

Spectral Top-Down Recovery of Latent Tree Models. - Yariv Aizenbud; Ariel Jaffe; Meng Wang; Amber Hu; Noah Amsel; Boaz Nadler; Joseph T. Chang; Yuval Kluger (CoRR, 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)

Graph of graphs analysis for multiplexed data with application to imaging mass cytometry. - Ya-Wei Eileen Lin; Tal Shnitzer; Ronen Talmon; Franz Villarroel-Espindola; Shruti Desai; Kurt A. Schalper; Yuval Kluger (PLoS Comput. Biol., 2021)

Spectral Neighbor Joining for Reconstruction of Latent Tree Models. - Ariel Jaffe; Noah Amsel; Yariv Aizenbud; Boaz Nadler; Joseph T. Chang; Yuval Kluger (SIAM J. Math. Data Sci., 2021)

Doubly Stochastic Normalization of the Gaussian Kernel Is Robust to Heteroskedastic Noise. - Boris Landa; Ronald R. Coifman; Yuval Kluger (SIAM J. Math. Data Sci., 2021)

Crowdsourcing Regression: A Spectral Approach. - Yaniv Tenzer; Omer Dror; Boaz Nadler; Erhan Bilal; Yuval Kluger (AISTATS, 2022)

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

Neural Inverse Transform Sampler. - Henry Li; Yuval Kluger (ICML, 2022)

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

ManiFeSt: Manifold-based Feature Selection for Small Data Sets. - David Cohen; Tal Shnitzer; Yuval Kluger; Ronen Talmon (CoRR, 2022)

Autoregressive Generative Modeling with Noise Conditional Maximum Likelihood Estimation. - Henry Li; Yuval Kluger (CoRR, 2022)

On the efficient evaluation of the azimuthal Fourier components of the Green's function for Helmholtz's equation in cylindrical coordinates. - James Garritano; Yuval Kluger; Vladimir Rokhlin; Kirill Serkh (J. Comput. Phys., 2022)

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

Biwhitening Reveals the Rank of a Count Matrix. - Boris Landa; Thomas T. C. K. Zhang; Yuval Kluger (SIAM J. Math. Data Sci., 2022)

GEASS: Neural causal feature selection for high-dimensional biological data. - Mingze Dong; Yuval Kluger (ICLR, 2023)

Few-Sample Feature Selection via Feature Manifold Learning. - David Cohen; Tal Shnitzer; Yuval Kluger; Ronen Talmon (ICML, 2023)

Towards Understanding and Reducing Graph Structural Noise for GNNs. - Mingze Dong; Yuval Kluger (ICML, 2023)

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

Exponential weight averaging as damped harmonic motion. - Jonathan Patsenker; Henry Li; Yuval Kluger (CoRR, 2023)

Comprehensive visualization of cell-cell interactions in single-cell and spatial transcriptomics with NICHES. - Micha Sam Brickman Raredon; Junchen Yang; Neeharika Kothapalli; Wesley Lewis; Naftali Kaminski; Laura E. Niklason; Yuval Kluger (Bioinform., 2023)

Hyperbolic Diffusion Procrustes Analysis for Intrinsic Representation of Hierarchical Data Sets. - Ya-Wei Eileen Lin; Yuval Kluger; Ronen Talmon (ICASSP, 2024)

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps. - Henry Li; Ronen Basri; Yuval Kluger (ICLR, 2024)

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

Entropic Optimal Transport Eigenmaps for Nonlinear Alignment and Joint Embedding of High-Dimensional Datasets. - Boris Landa; Yuval Kluger; Rong Ma (CoRR, 2024)

Three-Dimensional Reconstruction Pre-Training as a Prior to Improve Robustness to Adversarial Attacks and Spurious Correlation. - Yutaro Yamada; Fred Weiying Zhang; Yuval Kluger; Ilker Yildirim (Entropy, 2024)

Dual Diffusion for Unified Image Generation and Understanding. - Zijie Li; Henry Li; Yichun Shi; Amir Barati Farimani; Yuval Kluger; Linjie Yang; Peng Wang (CVPR, 2025)

Supervised fine-tuning of pre-trained antibody language models improves antigen specificity prediction. - Meng Wang; Jonathan Patsenker; Henry Li; Yuval Kluger; Steven H. Kleinstein (PLoS Comput. Biol., 2025)

Euclidean Distance Deflation Under High-Dimensional Heteroskedastic Noise. - Keyi Li; Yuval Kluger; Boris Landa (CoRR, 2025)

Injecting Measurement Information Yields a Fast and Noise-Robust Diffusion-Based Inverse Problem Solver. - Jonathan Patsenker; Henry Li; Myeongseob Ko; Ruoxi Jia 001; Yuval Kluger (CoRR, 2025)