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Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Features
Uri Shaham, Ofir Lindenbaum, Jonathan Svirsky +1
Modern datasets often contain large subsets of correlated features and nuisance features, which are not or loosely related to the main underlying structures of the data. Nuisance f…
Probabilistic Robust Autoencoders for Outlier Detection
Ofir Lindenbaum, Yariv Aizenbud, Yuval Kluger
Anomalies (or outliers) are prevalent in real-world empirical observations and potentially mask important underlying structures. Accurate identification of anomalous samples is cru…
Locally Sparse Neural Networks for Tabular Biomedical Data
Junchen Yang, Ofir Lindenbaum, Yuval Kluger
Tabular datasets with low-sample-size or many variables are prevalent in biomedicine. Practitioners in this domain prefer linear or tree-based models over neural networks since the…
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 +1
In this manuscript, we develop an efficient algorithm to evaluate the azimuthal Fourier components of the Green's function for the Helmholtz equation in cylindrical coordinates. A…
Biwhitening Reveals the Rank of a Count Matrix
Boris Landa, Thomas T. C. K. Zhang, Yuval Kluger
Estimating the rank of a corrupted data matrix is an important task in data analysis, most notably for choosing the number of components in PCA. Significant progress on this task w…
Spectral Top-Down Recovery of Latent Tree Models
Yariv Aizenbud, Ariel Jaffe, Meng Wang +5
Modeling the distribution of high dimensional data by a latent tree graphical model is a prevalent approach in multiple scientific domains. A common task is to infer the underlying…