Yuval Kluger

Publication Activity (10 Years)

Years Active: 2001-2024
Publications (10 Years): 61

Top Topics

Ensemble Learning

Randomized Algorithm

Small Data Sets

Feature Selection

Top Venues

SIAM J. Math. Data Sci.

Publications

Henry Li, Ronen Basri, Yuval Kluger
Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps. ICLR (2024)
Ya-Wei Eileen Lin, Yuval Kluger, Ronen Talmon
Hyperbolic Diffusion Procrustes Analysis for Intrinsic Representation of Hierarchical Data Sets. ICASSP (2024)
Boris Landa, Yuval Kluger, Rong Ma
Entropic Optimal Transport Eigenmaps for Nonlinear Alignment and Joint Embedding of High-Dimensional Datasets. CoRR (2024)
Yutaro Yamada, Fred Weiying Zhang, Yuval Kluger, Ilker Yildirim
Three-Dimensional Reconstruction Pre-Training as a Prior to Improve Robustness to Adversarial Attacks and Spurious Correlation. Entropy 26 (3) (2024)
David Cohen, Tal Shnitzer, Yuval Kluger, Ronen Talmon
Few-Sample Feature Selection via Feature Manifold Learning. ICML (2023)
Micha Sam Brickman Raredon, Junchen Yang, Neeharika Kothapalli, Wesley Lewis, Naftali Kaminski, Laura E. Niklason, Yuval Kluger
Comprehensive visualization of cell-cell interactions in single-cell and spatial transcriptomics with NICHES. Bioinform. 39 (1) (2023)
Junchen Yang, Ofir Lindenbaum, Yuval Kluger, Ariel Jaffe
Multi-modal Differentiable Unsupervised Feature Selection. CoRR (2023)
Junchen Yang, Ofir Lindenbaum, Yuval Kluger, Ariel Jaffe
Multi-modal differentiable unsupervised feature selection. UAI (2023)
Jonathan Patsenker, Henry Li, Yuval Kluger
Exponential weight averaging as damped harmonic motion. CoRR (2023)
Mingze Dong, Yuval Kluger
GEASS: Neural causal feature selection for high-dimensional biological data. ICLR (2023)
Mingze Dong, Yuval Kluger
Towards Understanding and Reducing Graph Structural Noise for GNNs. ICML (2023)
Boris Landa, Thomas T. C. K. Zhang, Yuval Kluger
Biwhitening Reveals the Rank of a Count Matrix. SIAM J. Math. Data Sci. 4 (4) (2022)
Yaniv Tenzer, Omer Dror, Boaz Nadler, Erhan Bilal, Yuval Kluger
Crowdsourcing Regression: A Spectral Approach. AISTATS (2022)
James Garritano, Yuval Kluger, Vladimir Rokhlin, Kirill Serkh
On the efficient evaluation of the azimuthal Fourier components of the Green's function for Helmholtz's equation in cylindrical coordinates. J. Comput. Phys. 471 (2022)
Henry Li, Yuval Kluger
Neural Inverse Transform Sampler. CoRR (2022)
Henry Li, Yuval Kluger
Neural Inverse Transform Sampler. ICML (2022)
Junchen Yang, Ofir Lindenbaum, Yuval Kluger
Locally Sparse Neural Networks for Tabular Biomedical Data. ICML (2022)
Henry Li, Yuval Kluger
Autoregressive Generative Modeling with Noise Conditional Maximum Likelihood Estimation. CoRR (2022)
Ofir Lindenbaum, Moshe Salhov, Amir Averbuch, Yuval Kluger
L0-Sparse Canonical Correlation Analysis. ICLR (2022)
Uri Shaham, Ofir Lindenbaum, Jonathan Svirsky, Yuval Kluger
Deep unsupervised feature selection by discarding nuisance and correlated features. Neural Networks 152 (2022)
David Cohen, Tal Shnitzer, Yuval Kluger, Ronen Talmon
ManiFeSt: Manifold-based Feature Selection for Small Data Sets. CoRR (2022)
Ariel Jaffe, Noah Amsel, Yariv Aizenbud, Boaz Nadler, Joseph T. Chang, Yuval Kluger
Spectral Neighbor Joining for Reconstruction of Latent Tree Models. SIAM J. Math. Data Sci. 3 (1) (2021)
James Garritano, Yuval Kluger, Vladimir Rokhlin, Kirill Serkh
On the Efficient Evaluation of the Azimuthal Fourier Components of the Green's Function for Helmholtz's Equation in Cylindrical Coordinates. CoRR (2021)
Ya-Wei Eileen Lin, Tal Shnitzer, Ronen Talmon, Franz Villarroel-Espindola, Shruti Desai, Kurt A. Schalper, Yuval Kluger
Graph of graphs analysis for multiplexed data with application to imaging mass cytometry. PLoS Comput. Biol. 17 (3) (2021)
Boris Landa, Ronald R. Coifman, Yuval Kluger
Doubly Stochastic Normalization of the Gaussian Kernel Is Robust to Heteroskedastic Noise. SIAM J. Math. Data Sci. 3 (1) (2021)
Ya-Wei Eileen Lin, Yuval Kluger, Ronen Talmon
Hyperbolic Procrustes Analysis Using Riemannian Geometry. NeurIPS (2021)
Junchen Yang, Ofir Lindenbaum, Yuval Kluger
Locally Sparse Networks for Interpretable Predictions. CoRR (2021)
Ofir Lindenbaum, Uri Shaham, Erez Peterfreund, Jonathan Svirsky, Nicolas Casey, Yuval Kluger
Differentiable Unsupervised Feature Selection based on a Gated Laplacian. NeurIPS (2021)
Uri Shaham, Ofir Lindenbaum, Jonathan Svirsky, Yuval Kluger
Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Features. CoRR (2021)
Yariv Aizenbud, Ariel Jaffe, Meng Wang, Amber Hu, Noah Amsel, Boaz Nadler, Joseph T. Chang, Yuval Kluger
Spectral Top-Down Recovery of Latent Tree Models. CoRR (2021)
Boris Landa, Thomas T. C. K. Zhang, Yuval Kluger
Biwhitening Reveals the Rank of a Count Matrix. CoRR (2021)
Yariv Aizenbud, Ofir Lindenbaum, Yuval Kluger
Probabilistic Robust Autoencoders for Anomaly Detection. CoRR (2021)
Ariel Jaffe, Noah Amsel, Boaz Nadler, Joseph T. Chang, Yuval Kluger
Spectral neighbor joining for reconstruction of latent tree models. CoRR (2020)
Ariel Jaffe, Yuval Kluger, Ofir Lindenbaum, Jonathan Patsenker, Erez Peterfreund, Stefan Steinerberger
The Spectral Underpinning of word2vec. CoRR (2020)
Boris Landa, Ronald R. Coifman, Yuval Kluger
Doubly-Stochastic Normalization of the Gaussian Kernel is Robust to Heteroskedastic Noise. CoRR (2020)
Yutaro Yamada, Ofir Lindenbaum, Sahand Negahban, Yuval Kluger
Feature Selection using Stochastic Gates. ICML (2020)
Ariel Jaffe, Yuval Kluger, George C. Linderman, Gal Mishne, Stefan Steinerberger
Randomized near-neighbor graphs, giant components and applications in data science. J. Appl. Probab. 57 (2) (2020)
Ofir Lindenbaum, Moshe Salhov, Amir Averbuch, Yuval Kluger
Deep Gated Canonical Correlation Analysis. CoRR (2020)
Ariel Jaffe, Yuval Kluger, Ofir Lindenbaum, Jonathan Patsenker, Erez Peterfreund, Stefan Steinerberger
The Spectral Underpinning of word2vec. Frontiers Appl. Math. Stat. 6 (2020)
Ofir Lindenbaum, Uri Shaham, Jonathan Svirsky, Erez Peterfreund, Yuval Kluger
Let the Data Choose its Features: Differentiable Unsupervised Feature Selection. CoRR (2020)
Dmitry Kobak, George C. Linderman, Stefan Steinerberger, Yuval Kluger, Philipp Berens
Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations. CoRR (2019)
Dmitry Kobak, George C. Linderman, Stefan Steinerberger, Yuval Kluger, Philipp Berens
Heavy-Tailed Kernels Reveal a Finer Cluster Structure in t-SNE Visualisations. ECML/PKDD (1) (2019)
Huamin Li, Yuval Kluger, Mark Tygert
Randomized algorithms for distributed computation of principal component analysis and singular value decomposition. Adv. Comput. Math. 44 (5) (2018)
Uri Shaham, Kelly P. Stanton, Henry Li, Boaz Nadler, Ronen Basri, Yuval Kluger
SpectralNet: Spectral Clustering using Deep Neural Networks. CoRR (2018)
Ariel Jaffe, Roi Weiss, Shai Carmi, Yuval Kluger, Boaz Nadler
Learning Binary Latent Variable Models: A Tensor Eigenpair Approach. ICML (2018)
Uri Shaham, Kelly P. Stanton, Henry Li, Ronen Basri, Boaz Nadler, Yuval Kluger
SpectralNet: Spectral Clustering using Deep Neural Networks. ICLR (Poster) (2018)
Uri Shaham, James Garritano, Yutaro Yamada, Ethan Weinberger, Alex Cloninger, Xiuyuan Cheng, Kelly P. Stanton, Yuval Kluger
Defending against Adversarial Images using Basis Functions Transformations. CoRR (2018)
Yutaro Yamada, Ofir Lindenbaum, Sahand Negahban, Yuval Kluger
Deep supervised feature selection using Stochastic Gates. CoRR (2018)
Gal Mishne, Ronen Talmon, Israel Cohen, Ronald R. Coifman, Yuval Kluger
Data-Driven Tree Transforms and Metrics. IEEE Trans. Signal Inf. Process. over Networks 4 (3) (2018)
Huamin Li, Uri Shaham, Kelly P. Stanton, Yi Yao, Ruth R. Montgomery, Yuval Kluger
Gating mass cytometry data by deep learning. Bioinform. 33 (21) (2017)
Uri Shaham, Kelly P. Stanton, Jun Zhao, Huamin Li, Khadir Raddassi, Ruth R. Montgomery, Yuval Kluger
Removal of batch effects using distribution-matching residual networks. Bioinform. 33 (16) (2017)
George C. Linderman, Manas Rachh, Jeremy G. Hoskins, Stefan Steinerberger, Yuval Kluger
Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding. CoRR (2017)
Omer Dror, Boaz Nadler, Erhan Bilal, Yuval Kluger
Unsupervised Ensemble Regression. CoRR (2017)
Gal Mishne, Ronen Talmon, Israel Cohen, Ronald R. Coifman, Yuval Kluger
Data-Driven Tree Transforms and Metrics. CoRR (2017)
George C. Linderman, Gal Mishne, Yuval Kluger, Stefan Steinerberger
Randomized Near Neighbor Graphs, Giant Components, and Applications in Data Science. CoRR (2017)
Huamin Li, George C. Linderman, Arthur Szlam, Kelly P. Stanton, Yuval Kluger, Mark Tygert
Algorithm 971: An Implementation of a Randomized Algorithm for Principal Component Analysis. ACM Trans. Math. Softw. 43 (3) (2017)
Jared Katzman, Uri Shaham, Alexander Cloninger, Jonathan Bates, Tingting Jiang, Yuval Kluger
Deep Survival: A Deep Cox Proportional Hazards Network. CoRR (2016)
Ariel Jaffe, Ethan Fetaya, Boaz Nadler, Tingting Jiang, Yuval Kluger
Unsupervised Ensemble Learning with Dependent Classifiers. AISTATS (2016)
Uri Shaham, Xiuyuan Cheng, Omer Dror, Ariel Jaffe, Boaz Nadler, Joseph T. Chang, Yuval Kluger
A Deep Learning Approach to Unsupervised Ensemble Learning. CoRR (2016)
Huamin Li, Yuval Kluger, Mark Tygert
Randomized algorithms for distributed computation of principal component analysis and singular value decomposition. CoRR (2016)
Uri Shaham, Xiuyuan Cheng, Omer Dror, Ariel Jaffe, Boaz Nadler, Joseph T. Chang, Yuval Kluger
A Deep Learning Approach to Unsupervised Ensemble Learning. ICML (2016)
Ariel Jaffe, Boaz Nadler, Yuval Kluger
Estimating the accuracies of multiple classifiers without labeled data. AISTATS (2015)
Ariel Jaffe, Ethan Fetaya, Boaz Nadler, Tingting Jiang, Yuval Kluger
Unsupervised Ensemble Learning with Dependent Classifiers. CoRR (2015)
Ariel Jaffe, Boaz Nadler, Yuval Kluger
Estimating the Accuracies of Multiple Classifiers Without Labeled Data. CoRR (2014)
Arthur Szlam, Yuval Kluger, Mark Tygert
An implementation of a randomized algorithm for principal component analysis. CoRR (2014)
Fabio Parisi, Francesco Strino, Boaz Nadler, Yuval Kluger
The student's dilemma: ranking and improving prediction at test time without access to training data CoRR (2013)
Yiming Zhou, John Ferguson, Joseph T. Chang, Yuval Kluger
Inter- and Intra-Combinatorial Regulation by Transcriptions Factors and MicroRNAs. BIOCOMP (2007)
Hyunsoo Kim, William Hu, Yuval Kluger
Unraveling condition specific gene transcriptional regulatory networks in Saccharomyces cerevisiae. BMC Bioinform. 7 (2006)
David Tuck, Harriet Kluger, Yuval Kluger
Characterizing disease states from topological properties of transcriptional regulatory networks. BMC Bioinform. 7 (2006)
Yuval Kluger, Harriet Kluger, David Tuck
Association between pathways in regulatory networks. EMBC (2006)
Paul Bertone, Yuval Kluger, Ning Lan, Deyou Zheng, Dinesh Christendat, Adelinda Yee, Aled M. Edwards, Cheryl H. Arrowsmith, Gaetano T. Montelione, Mark Gerstein
SPINE: an integrated tracking database and data mining approach for identifying feasible targets in high-throughput structural proteomics. Nucleic Acids Res. 29 (13) (2001)