A random-permutations-based approach to fast read alignment. - Roy R. Lederman (BMC Bioinform., 2013)

Alternating diffusion for common manifold learning with application to sleep stage assessment. - Roy R. Lederman; Ronen Talmon; Hau-Tieng Wu; Yu-Lun Lo; Ronald R. Coifman (ICASSP, 2015)

Common Variable Learning and Invariant Representation Learning using Siamese Neural Networks. - Uri Shaham 001; Roy R. Lederman (CoRR, 2015)

On the Analytical and Numerical Properties of the Truncated Laplace Transform I. - Roy R. Lederman; Vladimir Rokhlin (SIAM J. Numer. Anal., 2015)

A Representation Theory Perspective on Simultaneous Alignment and Classification. - Roy R. Lederman; Amit Singer (CoRR, 2016)

On the Analytical and Numerical Properties of the Truncated Laplace Transform. Part II. - Roy R. Lederman; Vladimir Rokhlin (SIAM J. Numer. Anal., 2016)

Continuously heterogeneous hyper-objects in cryo-EM and 3-D movies of many temporal dimensions. - Roy R. Lederman; Amit Singer (CoRR, 2017)

Numerical Algorithms for the Computation of Generalized Prolate Spheroidal Functions. - Roy R. Lederman (CoRR, 2017)

Heterogeneous multireference alignment: A single pass approach. - Nicolas Boumal; Tamir Bendory; Roy R. Lederman; Amit Singer (CISS, 2018)

Learning by coincidence: Siamese networks and common variable learning. - Uri Shaham 001; Roy R. Lederman (Pattern Recognit., 2018)

Hyper-Molecules: on the Representation and Recovery of Dynamical Structures, with Application to Flexible Macro-Molecular Structures in Cryo-EM. - Roy R. Lederman; Joakim Andén; Amit Singer (CoRR, 2019)

Extreme Values of the Fiedler Vector on Trees. - Roy R. Lederman; Stefan Steinerberger (CoRR, 2019)

Non-Canonical Hamiltonian Monte Carlo. - James A. Brofos; Roy R. Lederman (CoRR, 2020)

Spectral Flow on the Manifold of SPD Matrices for Multimodal Data Processing. - Ori Katz; Roy R. Lederman; Ronen Talmon (CoRR, 2020)

Magnetic Manifold Hamiltonian Monte Carlo. - James A. Brofos; Roy R. Lederman (CoRR, 2020)

Evaluating the Implicit Midpoint Integrator for Riemannian Hamiltonian Monte Carlo. - James A. Brofos; Roy R. Lederman (ICML, 2021)

Manifold Density Estimation via Generalized Dequantization. - James A. Brofos; Marcus A. Brubaker; Roy R. Lederman (CoRR, 2021)

Maximum likelihood for high-noise group orbit estimation and single-particle cryo-EM. - Zhou Fan; Roy R. Lederman; Yi Sun 010; Tianhao Wang 017; Sheng Xu (CoRR, 2021)

Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals. - James A. Brofos; Marylou Gabrié; Marcus A. Brubaker; Roy R. Lederman (AISTATS, 2022)

Using VAEs to Learn Latent Variables: Observations on Applications in cryo-EM. - Daniel G. Edelberg; Roy R. Lederman (CoRR, 2023)

On Manifold Learning in Plato's Cave: Remarks on Manifold Learning and Physical Phenomena. - Roy R. Lederman; Bogdan Toader (CoRR, 2023)

Efficient high-resolution refinement in cryo-EM with stochastic gradient descent. - Bogdan Toader; Marcus A. Brubaker; Roy R. Lederman (CoRR, 2023)

Multimodal manifold learning using kernel interpolation along geodesic paths. - Ori Katz; Roy R. Lederman; Ronen Talmon (Inf. Fusion, 2025)

An Observation on Lloyd's k-Means Algorithm in High Dimensions. - David Silva-Sánchez; Roy R. Lederman (CoRR, 2025)