Minimax-optimal semi-supervised regression on unknown manifolds. - Amit Moscovich; Ariel Jaffe; Boaz Nadler (AISTATS, 2017)

Semiparametric Classification of Forest Graphical Models. - Mary Frances Dorn; Amit Moscovich; Boaz Nadler; Clifford H. Spiegelman (CoRR, 2018)

Rescaling and other forms of unsupervised preprocessing introduce bias into cross-validation. - Amit Moscovich; Saharon Rosset (CoRR, 2019)

Cryo-EM reconstruction of continuous heterogeneity by Laplacian spectral volumes. - Amit Moscovich; Amit Halevi; Joakim Andén; Amit Singer (CoRR, 2019)

Unsupervised particle sorting for high-resolution single-particle cryo-EM. - Ye Zhou; Amit Moscovich; Tamir Bendory; Alberto Bartesaghi (CoRR, 2019)

Earthmover-Based Manifold Learning for Analyzing Molecular Conformation Spaces. - Nathan Zelesko; Amit Moscovich; Joe Kileel 001; Amit Singer (ISBI, 2020)

Wasserstein K-Means for Clustering Tomographic Projections. - Rohan Rao; Amit Moscovich; Amit Singer (CoRR, 2020)

Manifold learning with arbitrary norms. - Joe Kileel 001; Amit Moscovich; Nathan Zelesko; Amit Singer (CoRR, 2020)

Beyond Trees: Classification with Sparse Pairwise Dependencies. - Yaniv Tenzer; Amit Moscovich; Mary Frances Dorn; Boaz Nadler; Clifford H. Spiegelman (J. Mach. Learn. Res., 2020)

Product Manifold Learning. - Sharon Zhang; Amit Moscovich; Amit Singer (AISTATS, 2021)

Data-driven determination of number of discrete conformations in single-particle cryo-EM. - Ye Zhou; Amit Moscovich; Alberto Bartesaghi (Comput. Methods Programs Biomed., 2022)

Fast calculation of p-values for one-sided Kolmogorov-Smirnov type statistics. - Amit Moscovich (Comput. Stat. Data Anal., 2023)

Quantization-based Bounds on the Wasserstein Metric. - Jonathan Bobrutsky; Amit Moscovich (CoRR, 2025)