OOIR: Observatory of International Research

Papers

(The median citation count of SIAM Journal on Mathematics of Data Science is 1. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2021-05-01 to 2025-05-01.)

Article	Citations
A Simple and Optimal Algorithm for Strict Circular Seriation	31
Taming Neural Networks with TUSLA: Nonconvex Learning via Adaptive Stochastic Gradient Langevin Algorithms	24
Quantitative Approximation Results for Complex-Valued Neural Networks	18
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization	18
New Equivalences between Interpolation and SVMs: Kernels and Structured Features	18
On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions	17
A Note on the Regularity of Images Generated by Convolutional Neural Networks	16
Learning Functions Varying along a Central Subspace	15
Efficient Algorithms for Regularized Nonnegative Scale-Invariant Low-Rank Approximation Models	15
Safe Rules for the Identification of Zeros in the Solutions of the SLOPE Problem	11
Nonbacktracking Spectral Clustering of Nonuniform Hypergraphs	11
Persistent Laplacians: Properties, Algorithms and Implications	11
Poisson Reweighted Laplacian Uncertainty Sampling for Graph-Based Active Learning	11
Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning	11
Convergence of a Piggyback-Style Method for the Differentiation of Solutions of Standard Saddle-Point Problems	10
CA-PCA: Manifold Dimension Estimation, Adapted for Curvature	10
Scalable Tensor Methods for Nonuniform Hypergraphs	10
The GenCol Algorithm for High-Dimensional Optimal Transport: General Formulation and Application to Barycenters and Wasserstein Splines	9
A Variational Formulation of Accelerated Optimization on Riemannian Manifolds	9
Asymptotics of the Sketched Pseudoinverse	9
Stochastic Variance-Reduced Majorization-Minimization Algorithms	8
Inverse Evolution Layers: Physics-Informed Regularizers for Image Segmentation	8
Group-Invariant Tensor Train Networks for Supervised Learning	7
The Sample Complexity of Sparse Multireference Alignment and Single-Particle Cryo-Electron Microscopy	7
Benefit of Interpolation in Nearest Neighbor Algorithms	7

Function-Space Optimality of Neural Architectures with Multivariate Nonlinearities	7
The Geometric Median and Applications to Robust Mean Estimation	6
Optimal Dorfman Group Testing for Symmetric Distributions	6
Supervised Gromov–Wasserstein Optimal Transport with Metric-Preserving Constraints	6
Bi-Invariant Dissimilarity Measures for Sample Distributions in Lie Groups	6
Finite-Time Analysis of Natural Actor-Critic for POMDPs	6
Numerical Considerations and a new implementation for invariant coordinate selection	6
ABBA Neural Networks: Coping with Positivity, Expressivity, and Robustness	5
Post-training Quantization for Neural Networks with Provable Guarantees	5
Efficient Identification of Butterfly Sparse Matrix Factorizations	5
A Nonlinear Matrix Decomposition for Mining the Zeros of Sparse Data	5
Computing Wasserstein Barycenters via Operator Splitting: The Method of Averaged Marginals	5
LASSO Reloaded: A Variational Analysis Perspective with Applications to Compressed Sensing	5
Memory Capacity of Two Layer Neural Networks with Smooth Activations	4
Robust Classification Under $\ell_0$ Attack for the Gaussian Mixture Model	4
Accelerated and Instance-Optimal Policy Evaluation with Linear Function Approximation	4
Optimality Conditions for Nonsmooth Nonconvex-Nonconcave Min-Max Problems and Generative Adversarial Networks	4
Randomized Wasserstein Barycenter Computation: Resampling with Statistical Guarantees	4
Sequential Construction and Dimension Reduction of Gaussian Processes Under Inequality Constraints	4
KL Convergence Guarantees for Score Diffusion Models under Minimal Data Assumptions	4
Adaptive Joint Distribution Learning	4
Operator Shifting for General Noisy Matrix Systems	4
Sensitivity-Informed Provable Pruning of Neural Networks	4
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning	4
Fast Kernel Summation in High Dimensions via Slicing and Fourier Transforms	3
Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices	3
Entropic Optimal Transport on Random Graphs	3
A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations	3
A Generalized CUR Decomposition for Matrix Pairs	3
Stochastic Gradient Descent for Streaming Linear and Rectified Linear Systems with Adversarial Corruptions	3
Spectral Properties of Elementwise-Transformed Spiked Matrices	3
Convergence of a Constrained Vector Extrapolation Scheme	3
Approximating Probability Distributions by Using Wasserstein Generative Adversarial Networks	3
Binary Classification of Gaussian Mixtures: Abundance of Support Vectors, Benign Overfitting, and Regularization	2
Faster Rates for Compressed Federated Learning with Client-Variance Reduction	2
Optimization on Manifolds via Graph Gaussian Processes	2
Causal Structural Learning via Local Graphs	2
Network Online Change Point Localization	2
Efficiency of ETA Prediction	2
Approximate Q Learning for Controlled Diffusion Processes and Its Near Optimality	2
$k$-Variance: A Clustered Notion of Variance	2
Block Bregman Majorization Minimization with Extrapolation	2
Ensemble Linear Interpolators: The Role of Ensembling	2
Approximate Message Passing with Rigorous Guarantees for Pooled Data and Quantitative Group Testing	2
Positive Semi-definite Embedding for Dimensionality Reduction and Out-of-Sample Extensions	2
The Common Intuition to Transfer Learning Can Win or Lose: Case Studies for Linear Regression	2
Lipschitz-Regularized Gradient Flows and Generative Particle Algorithms for High-Dimensional Scarce Data	2
Nonlinear Weighted Directed Acyclic Graph and A Priori Estimates for Neural Networks	2
First-Order Conditions for Optimization in the Wasserstein Space	2
Determinantal Point Processes Implicitly Regularize Semiparametric Regression Problems	1

Accelerated Bregman Primal-Dual Methods Applied to Optimal Transport and Wasserstein Barycenter Problems	1
Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing	1
Sharp Analysis of Sketch-and-Project Methods via a Connection to Randomized Singular Value Decomposition	1
Federated Primal Dual Fixed Point Algorithm	1
On Design of Polyhedral Estimates in Linear Inverse Problems	1
Randomly Initialized Alternating Least Squares: Fast Convergence for Matrix Sensing	1
Principles for Initialization and Architecture Selection in Graph Neural Networks with ReLU Activations	1
Online MCMC Thinning with Kernelized Stein Discrepancy	1
Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks	1
${O({k})}$-Equivariant Dimensionality Reduction on Stiefel Manifolds	1
Identifying 3D Genome Organization in Diploid Organisms via Euclidean Distance Geometry	1
Nonparametric Finite Mixture Models with Possible Shape Constraints: A Cubic Newton Approach	1
Structural Balance and Random Walks on Complex Networks with Complex Weights	1
Fast Cluster Detection in Networks by First Order Optimization	1
Wasserstein-Based Projections with Applications to Inverse Problems	1
Applications of No-Collision Transportation Maps in Manifold Learning	1
Optimally Weighted PCA for High-Dimensional Heteroscedastic Data	1
Estimating a Potential Without the Agony of the Partition Function	1
The Positivity of the Neural Tangent Kernel	1
Intrinsic Dimension Adaptive Partitioning for Kernel Methods	1
Fredholm Integral Equations for Function Approximation and the Training of Neural Networks	1
Energy-Based Sequential Sampling for Low-Rank PSD-Matrix Approximation	1