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2020 – today
- 2024
[j18]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): 258 (2024)- [c21]Ya-Wei Eileen Lin, Yuval Kluger, Ronen Talmon:
Hyperbolic Diffusion Procrustes Analysis for Intrinsic Representation of Hierarchical Data Sets. ICASSP 2024: 6325-6329 - [c20]Henry Li, Ronen Basri, Yuval Kluger:
Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps. ICLR 2024 - [i32]Boris Landa, Yuval Kluger, Rong Ma:
Entropic Optimal Transport Eigenmaps for Nonlinear Alignment and Joint Embedding of High-Dimensional Datasets. CoRR abs/2407.01718 (2024) - 2023
- [j17]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) - [c19]Mingze Dong, Yuval Kluger:
GEASS: Neural causal feature selection for high-dimensional biological data. ICLR 2023 - [c18]David Cohen, Tal Shnitzer, Yuval Kluger, Ronen Talmon:
Few-Sample Feature Selection via Feature Manifold Learning. ICML 2023: 6296-6319 - [c17]Mingze Dong, Yuval Kluger:
Towards Understanding and Reducing Graph Structural Noise for GNNs. ICML 2023: 8202-8226 - [c16]Junchen Yang, Ofir Lindenbaum, Yuval Kluger, Ariel Jaffe:
Multi-modal differentiable unsupervised feature selection. UAI 2023: 2400-2410 - [i31]Junchen Yang, Ofir Lindenbaum, Yuval Kluger, Ariel Jaffe:
Multi-modal Differentiable Unsupervised Feature Selection. CoRR abs/2303.09381 (2023) - [i30]Jonathan Patsenker, Henry Li, Yuval Kluger:
Exponential weight averaging as damped harmonic motion. CoRR abs/2310.13854 (2023) - 2022
- [j16]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: 111585 (2022) - [j15]Uri Shaham, Ofir Lindenbaum, Jonathan Svirsky, Yuval Kluger:
Deep unsupervised feature selection by discarding nuisance and correlated features. Neural Networks 152: 34-43 (2022) - [j14]Boris Landa, Thomas T. C. K. Zhang, Yuval Kluger:
Biwhitening Reveals the Rank of a Count Matrix. SIAM J. Math. Data Sci. 4(4): 1420-1446 (2022) - [c15]Yaniv Tenzer, Omer Dror, Boaz Nadler, Erhan Bilal, Yuval Kluger:
Crowdsourcing Regression: A Spectral Approach. AISTATS 2022: 5225-5242 - [c14]Ofir Lindenbaum, Moshe Salhov, Amir Averbuch, Yuval Kluger:
L0-Sparse Canonical Correlation Analysis. ICLR 2022 - [c13]Henry Li, Yuval Kluger:
Neural Inverse Transform Sampler. ICML 2022: 12813-12825 - [c12]Junchen Yang, Ofir Lindenbaum, Yuval Kluger:
Locally Sparse Neural Networks for Tabular Biomedical Data. ICML 2022: 25123-25153 - [i29]Henry Li, Yuval Kluger:
Neural Inverse Transform Sampler. CoRR abs/2206.11172 (2022) - [i28]David Cohen, Tal Shnitzer, Yuval Kluger, Ronen Talmon:
ManiFeSt: Manifold-based Feature Selection for Small Data Sets. CoRR abs/2207.08574 (2022) - [i27]Henry Li, Yuval Kluger:
Autoregressive Generative Modeling with Noise Conditional Maximum Likelihood Estimation. CoRR abs/2210.10715 (2022) - 2021
- [j13]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) - [j12]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): 113-141 (2021) - [j11]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): 388-413 (2021) - [c11]Ofir Lindenbaum, Uri Shaham, Erez Peterfreund, Jonathan Svirsky, Nicolas Casey, Yuval Kluger:
Differentiable Unsupervised Feature Selection based on a Gated Laplacian. NeurIPS 2021: 1530-1542 - [c10]Ya-Wei Eileen Lin, Yuval Kluger, Ronen Talmon:
Hyperbolic Procrustes Analysis Using Riemannian Geometry. NeurIPS 2021: 5959-5971 - [i26]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 abs/2102.13276 (2021) - [i25]Boris Landa, Thomas T. C. K. Zhang, Yuval Kluger:
Biwhitening Reveals the Rank of a Count Matrix. CoRR abs/2103.13840 (2021) - [i24]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 abs/2104.12241 (2021) - [i23]Junchen Yang, Ofir Lindenbaum, Yuval Kluger:
Locally Sparse Networks for Interpretable Predictions. CoRR abs/2106.06468 (2021) - [i22]Yariv Aizenbud, Ofir Lindenbaum, Yuval Kluger:
Probabilistic Robust Autoencoders for Anomaly Detection. CoRR abs/2110.00494 (2021) - [i21]Uri Shaham, Ofir Lindenbaum, Jonathan Svirsky, Yuval Kluger:
Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Features. CoRR abs/2110.05306 (2021) - 2020
- [j10]Ariel Jaffe, Yuval Kluger, Ofir Lindenbaum, Jonathan Patsenker, Erez Peterfreund, Stefan Steinerberger:
The Spectral Underpinning of word2vec. Frontiers Appl. Math. Stat. 6: 593406 (2020) - [j9]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): 458-476 (2020) - [c9]Yutaro Yamada, Ofir Lindenbaum, Sahand Negahban, Yuval Kluger:
Feature Selection using Stochastic Gates. ICML 2020: 10648-10659 - [i20]Ariel Jaffe, Yuval Kluger, Ofir Lindenbaum, Jonathan Patsenker, Erez Peterfreund, Stefan Steinerberger:
The Spectral Underpinning of word2vec. CoRR abs/2002.12317 (2020) - [i19]Ariel Jaffe, Noah Amsel, Boaz Nadler, Joseph T. Chang, Yuval Kluger:
Spectral neighbor joining for reconstruction of latent tree models. CoRR abs/2002.12547 (2020) - [i18]Boris Landa, Ronald R. Coifman, Yuval Kluger:
Doubly-Stochastic Normalization of the Gaussian Kernel is Robust to Heteroskedastic Noise. CoRR abs/2006.00402 (2020) - [i17]Ofir Lindenbaum, Uri Shaham, Jonathan Svirsky, Erez Peterfreund, Yuval Kluger:
Let the Data Choose its Features: Differentiable Unsupervised Feature Selection. CoRR abs/2007.04728 (2020) - [i16]Ofir Lindenbaum, Moshe Salhov, Amir Averbuch, Yuval Kluger:
Deep Gated Canonical Correlation Analysis. CoRR abs/2010.05620 (2020)
2010 – 2019
- 2019
- [c8]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: 124-139 - [i15]Dmitry Kobak, George C. Linderman, Stefan Steinerberger, Yuval Kluger, Philipp Berens:
Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations. CoRR abs/1902.05804 (2019) - 2018
- [j8]Huamin Li, Yuval Kluger, Mark Tygert:
Randomized algorithms for distributed computation of principal component analysis and singular value decomposition. Adv. Comput. Math. 44(5): 1651-1672 (2018) - [j7]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): 451-466 (2018) - [c7]Uri Shaham, Kelly P. Stanton, Henry Li, Ronen Basri, Boaz Nadler, Yuval Kluger:
SpectralNet: Spectral Clustering using Deep Neural Networks. ICLR (Poster) 2018 - [c6]Ariel Jaffe, Roi Weiss, Shai Carmi, Yuval Kluger, Boaz Nadler:
Learning Binary Latent Variable Models: A Tensor Eigenpair Approach. ICML 2018: 2201-2210 - [i14]Uri Shaham, Kelly P. Stanton, Henry Li, Boaz Nadler, Ronen Basri, Yuval Kluger:
SpectralNet: Spectral Clustering using Deep Neural Networks. CoRR abs/1801.01587 (2018) - [i13]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 abs/1803.10840 (2018) - [i12]Yutaro Yamada, Ofir Lindenbaum, Sahand Negahban, Yuval Kluger:
Deep supervised feature selection using Stochastic Gates. CoRR abs/1810.04247 (2018) - 2017
- [j6]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): 2539-2546 (2017) - [j5]Huamin Li, Uri Shaham, Kelly P. Stanton, Yi Yao, Ruth R. Montgomery, Yuval Kluger:
Gating mass cytometry data by deep learning. Bioinform. 33(21): 3423-3430 (2017) - [j4]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): 28:1-28:14 (2017) - [i11]Omer Dror, Boaz Nadler, Erhan Bilal, Yuval Kluger:
Unsupervised Ensemble Regression. CoRR abs/1703.02965 (2017) - [i10]Gal Mishne, Ronen Talmon, Israel Cohen, Ronald R. Coifman, Yuval Kluger:
Data-Driven Tree Transforms and Metrics. CoRR abs/1708.05768 (2017) - [i9]George C. Linderman, Gal Mishne, Yuval Kluger, Stefan Steinerberger:
Randomized Near Neighbor Graphs, Giant Components, and Applications in Data Science. CoRR abs/1711.04712 (2017) - [i8]George C. Linderman, Manas Rachh, Jeremy G. Hoskins, Stefan Steinerberger, Yuval Kluger:
Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding. CoRR abs/1712.09005 (2017) - 2016
- [c5]Ariel Jaffe, Ethan Fetaya, Boaz Nadler, Tingting Jiang, Yuval Kluger:
Unsupervised Ensemble Learning with Dependent Classifiers. AISTATS 2016: 351-360 - [c4]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: 30-39 - [i7]Uri Shaham, Xiuyuan Cheng, Omer Dror, Ariel Jaffe, Boaz Nadler, Joseph T. Chang, Yuval Kluger:
A Deep Learning Approach to Unsupervised Ensemble Learning. CoRR abs/1602.02285 (2016) - [i6]Jared Katzman, Uri Shaham, Alexander Cloninger, Jonathan Bates, Tingting Jiang, Yuval Kluger:
Deep Survival: A Deep Cox Proportional Hazards Network. CoRR abs/1606.00931 (2016) - [i5]Huamin Li, Yuval Kluger, Mark Tygert:
Randomized algorithms for distributed computation of principal component analysis and singular value decomposition. CoRR abs/1612.08709 (2016) - 2015
- [c3]Ariel Jaffe, Boaz Nadler, Yuval Kluger:
Estimating the accuracies of multiple classifiers without labeled data. AISTATS 2015 - [i4]Ariel Jaffe, Ethan Fetaya, Boaz Nadler, Tingting Jiang, Yuval Kluger:
Unsupervised Ensemble Learning with Dependent Classifiers. CoRR abs/1510.05830 (2015) - 2014
- [i3]Ariel Jaffe, Boaz Nadler, Yuval Kluger:
Estimating the Accuracies of Multiple Classifiers Without Labeled Data. CoRR abs/1407.7644 (2014) - [i2]Arthur Szlam, Yuval Kluger, Mark Tygert:
An implementation of a randomized algorithm for principal component analysis. CoRR abs/1412.3510 (2014) - 2013
- [i1]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 abs/1303.3257 (2013)
2000 – 2009
- 2007
- [c2]Yiming Zhou, John Ferguson, Joseph T. Chang, Yuval Kluger:
Inter- and Intra-Combinatorial Regulation by Transcriptions Factors and MicroRNAs. BIOCOMP 2007: 598-604 - 2006
- [j3]Hyunsoo Kim, William Hu, Yuval Kluger:
Unraveling condition specific gene transcriptional regulatory networks in Saccharomyces cerevisiae. BMC Bioinform. 7: 165 (2006) - [j2]David Tuck, Harriet Kluger, Yuval Kluger:
Characterizing disease states from topological properties of transcriptional regulatory networks. BMC Bioinform. 7: 236 (2006) - [c1]Yuval Kluger, Harriet Kluger, David Tuck:
Association between pathways in regulatory networks. EMBC 2006: 2036-2040 - 2001
- [j1]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): 2884-2898 (2001)
Coauthor Index
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