Yuval Kluger

According to our database, Yuval Kluger authored at least 53 papers between 2001 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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., January, 2023

Exponential weight averaging as damped harmonic motion.

Jonathan Patsenker, Henry Li, Yuval Kluger

CoRR, 2023

Multi-modal differentiable unsupervised feature selection.

Junchen Yang, Ofir Lindenbaum, Yuval Kluger, Ariel Jaffe

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Towards Understanding and Reducing Graph Structural Noise for GNNs.

Mingze Dong, Yuval Kluger

Proceedings of the International Conference on Machine Learning, 2023

Few-Sample Feature Selection via Feature Manifold Learning.

David Cohen, Tal Shnitzer, Yuval Kluger, Ronen Talmon

Proceedings of the International Conference on Machine Learning, 2023

GEASS: Neural causal feature selection for high-dimensional biological data.

Mingze Dong, Yuval Kluger

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Biwhitening Reveals the Rank of a Count Matrix.

Boris Landa, Thomas T. C. K. Zhang, Yuval Kluger

SIAM J. Math. Data Sci., December, 2022

Deep unsupervised feature selection by discarding nuisance and correlated features.

Uri Shaham, Ofir Lindenbaum, Jonathan Svirsky, Yuval Kluger

Neural Networks, 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

Autoregressive Generative Modeling with Noise Conditional Maximum Likelihood Estimation.

Henry Li, Yuval Kluger

CoRR, 2022

ManiFeSt: Manifold-based Feature Selection for Small Data Sets.

David Cohen, Tal Shnitzer, Yuval Kluger, Ronen Talmon

CoRR, 2022

Locally Sparse Neural Networks for Tabular Biomedical Data.

Junchen Yang, Ofir Lindenbaum, Yuval Kluger

Proceedings of the International Conference on Machine Learning, 2022

Neural Inverse Transform Sampler.

Henry Li, Yuval Kluger

Proceedings of the International Conference on Machine Learning, 2022

L0-Sparse Canonical Correlation Analysis.

Ofir Lindenbaum, Moshe Salhov, Amir Averbuch, Yuval Kluger

Proceedings of the Tenth International Conference on Learning Representations, 2022

Crowdsourcing Regression: A Spectral Approach.

Yaniv Tenzer, Omer Dror, Boaz Nadler, Erhan Bilal, Yuval Kluger

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

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

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

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

Probabilistic Robust Autoencoders for Anomaly Detection.

Yariv Aizenbud, Ofir Lindenbaum, Yuval Kluger

CoRR, 2021

Locally Sparse Networks for Interpretable Predictions.

Junchen Yang, Ofir Lindenbaum, Yuval Kluger

CoRR, 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

Differentiable Unsupervised Feature Selection based on a Gated Laplacian.

Ofir Lindenbaum, Uri Shaham, Erez Peterfreund, Jonathan Svirsky, Nicolas Casey, Yuval Kluger

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Hyperbolic Procrustes Analysis Using Riemannian Geometry.

Ya-Wei Eileen Lin, Yuval Kluger, Ronen Talmon

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

The Spectral Underpinning of word2vec.

Ariel Jaffe, Yuval Kluger, Ofir Lindenbaum, Jonathan Patsenker, Erez Peterfreund, Stefan Steinerberger

Frontiers Appl. Math. Stat., 2020

Deep Gated Canonical Correlation Analysis.

Ofir Lindenbaum, Moshe Salhov, Amir Averbuch, Yuval Kluger

CoRR, 2020

Let the Data Choose its Features: Differentiable Unsupervised Feature Selection.

Ofir Lindenbaum, Uri Shaham, Jonathan Svirsky, Erez Peterfreund, Yuval Kluger

CoRR, 2020

Spectral neighbor joining for reconstruction of latent tree models.

Ariel Jaffe, Noah Amsel, Boaz Nadler, Joseph T. Chang, Yuval Kluger

CoRR, 2020

Feature Selection using Stochastic Gates.

Yutaro Yamada, Ofir Lindenbaum, Sahand Negahban, Yuval Kluger

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Heavy-Tailed Kernels Reveal a Finer Cluster Structure in t-SNE Visualisations.

Dmitry Kobak, George C. Linderman, Stefan Steinerberger, Yuval Kluger, Philipp Berens

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

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

Deep supervised feature selection using Stochastic Gates.

Yutaro Yamada, Ofir Lindenbaum, Sahand Negahban, Yuval Kluger

CoRR, 2018

Defending against Adversarial Images using Basis Functions Transformations.

Uri Shaham, James Garritano, Yutaro Yamada, Ethan Weinberger, Alex Cloninger, Xiuyuan Cheng, Kelly P. Stanton, Yuval Kluger

CoRR, 2018

Randomized algorithms for distributed computation of principal component analysis and singular value decomposition.

Huamin Li, Yuval Kluger, Mark Tygert

Adv. Comput. Math., 2018

Learning Binary Latent Variable Models: A Tensor Eigenpair Approach.

Ariel Jaffe, Roi Weiss, Shai Carmi, Yuval Kluger, Boaz Nadler

Proceedings of the 35th International Conference on Machine Learning, 2018

SpectralNet: Spectral Clustering using Deep Neural Networks.

Uri Shaham, Kelly P. Stanton, Henry Li, Ronen Basri, Boaz Nadler, Yuval Kluger

Proceedings of the 6th International Conference on Learning Representations, 2018

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

Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding.

George C. Linderman, Manas Rachh, Jeremy G. Hoskins, Stefan Steinerberger, 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

Unsupervised Ensemble Regression.

Omer Dror, Boaz Nadler, Erhan Bilal, Yuval Kluger

CoRR, 2017

Removal of batch effects using distribution-matching residual networks.

Uri Shaham, Kelly P. Stanton, Jun Zhao, Huamin Li, Khadir Raddassi, Ruth R. Montgomery, Yuval Kluger

Bioinform., 2017

Gating mass cytometry data by deep learning.

Huamin Li, Uri Shaham, Kelly P. Stanton, Yi Yao, Ruth R. Montgomery, Yuval Kluger

Bioinform., 2017

2016

Deep Survival: A Deep Cox Proportional Hazards Network.

Jared Katzman, Uri Shaham, Alexander Cloninger, Jonathan Bates, Tingting Jiang, Yuval Kluger

CoRR, 2016

A Deep Learning Approach to Unsupervised Ensemble Learning.

Uri Shaham, Xiuyuan Cheng, Omer Dror, Ariel Jaffe, Boaz Nadler, Joseph T. Chang, Yuval Kluger

Proceedings of the 33nd International Conference on Machine Learning, 2016

Unsupervised Ensemble Learning with Dependent Classifiers.

Ariel Jaffe, Ethan Fetaya, Boaz Nadler, Tingting Jiang, Yuval Kluger

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Estimating the accuracies of multiple classifiers without labeled data.

Ariel Jaffe, Boaz Nadler, Yuval Kluger

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

An implementation of a randomized algorithm for principal component analysis.

Arthur Szlam, Yuval Kluger, Mark Tygert

CoRR, 2014

2013

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

2007

Inter- and Intra-Combinatorial Regulation by Transcriptions Factors and MicroRNAs.

Yiming Zhou, John Ferguson, Joseph T. Chang, Yuval Kluger

Proceedings of the International Conference on Bioinformatics & Computational Biology, 2007

2006

Characterizing disease states from topological properties of transcriptional regulatory networks.

David Tuck, Harriet Kluger, Yuval Kluger

BMC Bioinform., 2006

Unraveling condition specific gene transcriptional regulatory networks in Saccharomyces cerevisiae.

Hyunsoo Kim, William Hu, Yuval Kluger

BMC Bioinform., 2006

Association between pathways in regulatory networks.

Yuval Kluger, Harriet Kluger, David Tuck

Proceedings of the 28th International Conference of the IEEE Engineering in Medicine and Biology Society, 2006

2001

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