50 citations · 250 across the 16 of their papers we have counts for
16 papers
Group Invariant Spectral Embedding
Yeari Vigder, Paulina Hoyos, David Thong +3
Spectral embedding methods are widely used for dimensionality reduction and clustering of high-dimensional datasets with intrinsic low-dimensional structures. Although many dataset…
Optimized Sequential Testing for Binary Ensemble Classifiers
Joseph Kalman, Amit Moscovich
Ensemble classifiers are predictive models that combine the results of simpler base models, often by majority vote. A classic example is random forests, which combine the predictio…
Spectral Graph Filtering for Modality-Specific Representation Learning
Shira Yoffe, Amit Moscovich, Ariel Jaffe
Multimodal datasets, where measurements are obtained from multiple sensors, have become central to many scientific domains. In unsupervised settings, most representation learning m…
Hidden Markov modeling of single particle diffusion with stochastic tethering
Amit Federbush, Amit Moscovich, Yohai Bar-Sinai
The statistics of the diffusive motion of particles often serve as an experimental proxy for their interaction with the environment. However, inferring the physical properties from…
Manifold learning with arbitrary norms
Joe Kileel, Amit Moscovich, Nathan Zelesko +1
Manifold learning methods play a prominent role in nonlinear dimensionality reduction and other tasks involving high-dimensional data sets with low intrinsic dimensionality. Many o…
Wasserstein K-Means for Clustering Tomographic Projections
Rohan Rao, Amit Moscovich, Amit Singer
Motivated by the 2D class averaging problem in single-particle cryo-electron microscopy (cryo-EM), we present a k-means algorithm based on a rotationally-invariant Wasserstein metr…
Product Manifold Learning
Sharon Zhang, Amit Moscovich, Amit Singer
We consider problems of dimensionality reduction and learning data representations for continuous spaces with two or more independent degrees of freedom. Such problems occur, for e…
Fast calculation of p-values for one-sided Kolmogorov-Smirnov type statistics
Amit Moscovich
A novel method for computing exact p-values of one-sided statistics from the Kolmogorov-Smirnov family is presented. It covers the Higher Criticism statistic, one-sided weighted Ko…
Unsupervised particle sorting for high-resolution single-particle cryo-EM
Ye Zhou, Amit Moscovich, Tamir Bendory +1
Single-particle cryo-Electron Microscopy (EM) has become a popular technique for determining the structure of challenging biomolecules that are inaccessible to other technologies.…
Earthmover-based manifold learning for analyzing molecular conformation spaces
Nathan Zelesko, Amit Moscovich, Joe Kileel +1
In this paper, we propose a novel approach for manifold learning that combines the Earthmover's distance (EMD) with the diffusion maps method for dimensionality reduction. We demon…
Cryo-EM reconstruction of continuous heterogeneity by Laplacian spectral volumes
Amit Moscovich, Amit Halevi, Joakim Andén +1
Single-particle electron cryomicroscopy is an essential tool for high-resolution 3D reconstruction of proteins and other biological macromolecules. An important challenge in cryo-E…
On the cross-validation bias due to unsupervised pre-processing
Amit Moscovich, Saharon Rosset
Cross-validation is the de facto standard for predictive model evaluation and selection. In proper use, it provides an unbiased estimate of a model's predictive performance. Howeve…
Beyond Trees: Classification with Sparse Pairwise Dependencies
Yaniv Tenzer, Amit Moscovich, Mary Frances Dorn +2
Several classification methods assume that the underlying distributions follow tree-structured graphical models. Indeed, trees capture statistical dependencies between pairs of var…
Minimax-optimal semi-supervised regression on unknown manifolds
Amit Moscovich, Ariel Jaffe, Boaz Nadler
We consider semi-supervised regression when the predictor variables are drawn from an unknown manifold. A simple two step approach to this problem is to: (i) estimate the manifold…
Fast calculation of boundary crossing probabilities for Poisson processes
Amit Moscovich, Boaz Nadler
The boundary crossing probability of a Poisson process with jumps is a fundamental quantity with numerous applications. We present a fast algorithm to calculate…
On the exact Berk-Jones statistics and their p-value calculation
Amit Moscovich, Boaz Nadler, Clifford Spiegelman
Continuous goodness-of-fit testing is a classical problem in statistics. Despite having low power for detecting deviations at the tail of a distribution, the most popular test is b…