OOIR: Observatory of International Research

Papers

(The median citation count of SIAM Journal on Mathematics of Data Science is 2. 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 2022-01-01 to 2026-01-01.)

Article	Citations
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization	43
A Simple and Optimal Algorithm for Strict Circular Seriation	31
Taming Neural Networks with TUSLA: Nonconvex Learning via Adaptive Stochastic Gradient Langevin Algorithms	30
Spectral Barron Space for Deep Neural Network Approximation	28
On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions	25
A Note on the Regularity of Images Generated by Convolutional Neural Networks	23
Randomized Nyström Approximation of Non-negative Self-Adjoint Operators	23
New Equivalences between Interpolation and SVMs: Kernels and Structured Features	22
Poisson Reweighted Laplacian Uncertainty Sampling for Graph-Based Active Learning	18
Resolving the Mixing Time of the Langevin Algorithm to Its Stationary Distribution for Log-Concave Sampling	17
Block Majorization Minimization with Extrapolation and Application to ${\beta }$-NMF	17
Efficient Algorithms for Regularized Nonnegative Scale-Invariant Low-Rank Approximation Models	14
Learning Functions Varying along a Central Subspace	14
Deep Block Proximal Linearized Minimization Algorithm for Nonconvex Inverse Problems	13
Online Machine Teaching under Learner Uncertainty: Gradient Descent Learners of a Quadratic Loss	13
Nonlinear Tomographic Reconstruction via Nonsmooth Optimization	13
Quantitative Approximation Results for Complex-Valued Neural Networks	13
Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning	13
Nonbacktracking Spectral Clustering of Nonuniform Hypergraphs	12
Safe Rules for the Identification of Zeros in the Solutions of the SLOPE Problem	11
Nonlinear Meta-learning Can Guarantee Faster Rates	11
Persistent Laplacians: Properties, Algorithms and Implications	11
CA-PCA: Manifold Dimension Estimation, Adapted for Curvature	11
Convergence of a Piggyback-Style Method for the Differentiation of Solutions of Standard Saddle-Point Problems	10
Stochastic Variance-Reduced Majorization-Minimization Algorithms	10

Scalable Tensor Methods for Nonuniform Hypergraphs	10
Function-Space Optimality of Neural Architectures with Multivariate Nonlinearities	10
The Sample Complexity of Sparse Multireference Alignment and Single-Particle Cryo-Electron Microscopy	9
Inverse Evolution Layers: Physics-Informed Regularizers for Image Segmentation	9
Covariance Alignment: From Maximum Likelihood Estimation to Gromov–Wasserstein	9
A Variational Formulation of Accelerated Optimization on Riemannian Manifolds	9
Group-Invariant Tensor Train Networks for Supervised Learning	9
The GenCol Algorithm for High-Dimensional Optimal Transport: General Formulation and Application to Barycenters and Wasserstein Splines	9
Asymptotics of the Sketched Pseudoinverse	9
Bi-Invariant Dissimilarity Measures for Sample Distributions in Lie Groups	8
Finite-Time Analysis of Natural Actor-Critic for POMDPs	8
The Geometric Median and Applications to Robust Mean Estimation	8
On Neural Network Approximation of Ideal Adversarial Attack and Convergence of Adversarial Training	8
Random Multitype Spanning Forests for Synchronization on Sparse Graphs	8
Optimal Dorfman Group Testing for Symmetric Distributions	7
Benefit of Interpolation in Nearest Neighbor Algorithms	7
LASSO Reloaded: A Variational Analysis Perspective with Applications to Compressed Sensing	7
Post-training Quantization for Neural Networks with Provable Guarantees	7
A Nonlinear Matrix Decomposition for Mining the Zeros of Sparse Data	7
Numerical Considerations and a new implementation for invariant coordinate selection	7
Computing Wasserstein Barycenters via Operator Splitting: The Method of Averaged Marginals	7
Efficient Identification of Butterfly Sparse Matrix Factorizations	7
Convergence of Gradient Descent for Recurrent Neural Networks: A Nonasymptotic Analysis	7
Supervised Gromov–Wasserstein Optimal Transport with Metric-Preserving Constraints	7
ABBA Neural Networks: Coping with Positivity, Expressivity, and Robustness	7
Robust Classification Under $\ell_0$ Attack for the Gaussian Mixture Model	6
Phase Retrieval with Semialgebraic and ReLU Neural Network Priors	6
Randomized Wasserstein Barycenter Computation: Resampling with Statistical Guarantees	6
Memory Capacity of Two Layer Neural Networks with Smooth Activations	6
Operator Shifting for General Noisy Matrix Systems	6
Adaptive Joint Distribution Learning	6
Complete and Continuous Invariants of 1-Periodic Sequences in Polynomial Time	6
Fast Kernel Summation in High Dimensions via Slicing and Fourier Transforms	5
KL Convergence Guarantees for Score Diffusion Models under Minimal Data Assumptions	5
Stochastic Gradient Descent for Streaming Linear and Rectified Linear Systems with Adversarial Corruptions	5
Sequential Construction and Dimension Reduction of Gaussian Processes Under Inequality Constraints	5
Optimality Conditions for Nonsmooth Nonconvex-Nonconcave Min-Max Problems and Generative Adversarial Networks	5
Sensitivity-Informed Provable Pruning of Neural Networks	5
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning	5
Accelerated and Instance-Optimal Policy Evaluation with Linear Function Approximation	5
HADES: Fast Singularity Detection with Local Measure Comparison	5
A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations	5
Network Online Change Point Localization	4
Approximating Probability Distributions by Using Wasserstein Generative Adversarial Networks	4
A Generalized CUR Decomposition for Matrix Pairs	4
Stability of Sequential Lateration and of Stress Minimization in the Presence of Noise	4
On the Rates of Convergence for Learning with Convolutional Neural Networks	4
Ensemble Linear Interpolators: The Role of Ensembling	4
Lipschitz-Regularized Gradient Flows and Generative Particle Algorithms for High-Dimensional Scarce Data	4
Spectral Properties of Elementwise-Transformed Spiked Matrices	4

Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices	4
Insights into Kernel PCA with Application to Multivariate Extremes	4
Causal Structural Learning via Local Graphs	4
Block Bregman Majorization Minimization with Extrapolation	4
Convergence of a Constrained Vector Extrapolation Scheme	4
A Priori Estimates for Deep Residual Network in Continuous-Time Reinforcement Learning	4
Entropic Optimal Transport on Random Graphs	4
Optimization on Manifolds via Graph Gaussian Processes	3
Diffeomorphic Measure Matching with Kernels for Generative Modeling	3
Simple Alternating Minimization Provably Solves Complete Dictionary Learning	3
Positive Semi-definite Embedding for Dimensionality Reduction and Out-of-Sample Extensions	3
Nonlinear Weighted Directed Acyclic Graph and A Priori Estimates for Neural Networks	3
Approximate Message Passing with Rigorous Guarantees for Pooled Data and Quantitative Group Testing	3
First-Order Conditions for Optimization in the Wasserstein Space	3
Sharp Analysis of Sketch-and-Project Methods via a Connection to Randomized Singular Value Decomposition	2
Faster Rates for Compressed Federated Learning with Client-Variance Reduction	2
Approximate Q Learning for Controlled Diffusion Processes and Its Near Optimality	2
On the Nonconvexity of Push-Forward Constraints and Its Consequences in Machine Learning	2
Principles for Initialization and Architecture Selection in Graph Neural Networks with ReLU Activations	2
Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks	2
Online MCMC Thinning with Kernelized Stein Discrepancy	2
Determinantal Point Processes Implicitly Regularize Semiparametric Regression Problems	2
Enforcing Katz and PageRank Centrality Measures in Complex Networks	2
Stochastic Optimal Transport in Banach Spaces for Regularized Estimation of Multivariate Quantiles	2
The Common Intuition to Transfer Learning Can Win or Lose: Case Studies for Linear Regression	2
Binary Classification of Gaussian Mixtures: Abundance of Support Vectors, Benign Overfitting, and Regularization	2
Efficiency of ETA Prediction	2
Optimally Weighted PCA for High-Dimensional Heteroscedastic Data	2
Wasserstein-Based Projections with Applications to Inverse Problems	2
The Positivity of the Neural Tangent Kernel	2
Accelerated Bregman Primal-Dual Methods Applied to Optimal Transport and Wasserstein Barycenter Problems	2
Estimating a Potential Without the Agony of the Partition Function	2
Exploring Variance Reduction in Importance Sampling for Efficient DNN Training	2
An Adaptively Inexact First-Order Method for Bilevel Optimization with Application to Hyperparameter Learning	2
$k$-Variance: A Clustered Notion of Variance	2
${O({k})}$-Equivariant Dimensionality Reduction on Stiefel Manifolds	2
Applications of No-Collision Transportation Maps in Manifold Learning	2
Landmark Alternating Diffusion	2
Identifying 3D Genome Organization in Diploid Organisms via Euclidean Distance Geometry	2
Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing	2