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 2022-08-01 to 2026-08-01.)

Article	Citations
Spectral Barron Space for Deep Neural Network Approximation	37
A Simple and Optimal Algorithm for Strict Circular Seriation	29
Taming Neural Networks with TUSLA: Nonconvex Learning via Adaptive Stochastic Gradient Langevin Algorithms	23
Learning Functions Varying along a Central Subspace	22
Efficient Algorithms for Regularized Nonnegative Scale-Invariant Low-Rank Approximation Models	19
Block Majorization Minimization with Extrapolation and Application to \({\beta }\)-NMF	17
On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions	17
Poisson Reweighted Laplacian Uncertainty Sampling for Graph-Based Active Learning	16
Deep Block Proximal Linearized Minimization Algorithm for Nonconvex Inverse Problems	15
New Equivalences between Interpolation and SVMs: Kernels and Structured Features	15
Resolving the Mixing Time of the Langevin Algorithm to Its Stationary Distribution for Log-Concave Sampling	14
Randomized Nyström Approximation of Non-negative Self-Adjoint Operators	14
Online Machine Teaching under Learner Uncertainty: Gradient Descent Learners of a Quadratic Loss	13
Detection and Estimation of Vertexwise Latent Position Shifts Across Networks	13
A Note on the Regularity of Images Generated by Convolutional Neural Networks	13
Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning	11
Nonlinear Tomographic Reconstruction via Nonsmooth Optimization	11
CA-PCA: Manifold Dimension Estimation, Adapted for Curvature	10
Nonbacktracking Spectral Clustering of Nonuniform Hypergraphs	10
Nonlinear Meta-learning Can Guarantee Faster Rates	10
Safe Rules for the Identification of Zeros in the Solutions of the SLOPE Problem	10
Can We Spot a Fake?	10
Scalable Tensor Methods for Nonuniform Hypergraphs	9
Function-Space Optimality of Neural Architectures with Multivariate Nonlinearities	9
A Notion of Uniqueness for the Adversarial Bayes Classifier	9

Asymptotics of the Sketched Pseudoinverse	9
Learning Memory Kernels in Generalized Langevin Equations	9
Stochastic Variance-Reduced Majorization-Minimization Algorithms	9
The Sample Complexity of Sparse Multireference Alignment and Single-Particle Cryo-Electron Microscopy	8
Group-Invariant Tensor Train Networks for Supervised Learning	8
Covariance Alignment: From Maximum Likelihood Estimation to Gromov–Wasserstein	8
Inverse Evolution Layers: Physics-Informed Regularizers for Image Segmentation	8
The GenCol Algorithm for High-Dimensional Optimal Transport: General Formulation and Application to Barycenters and Wasserstein Splines	8
Learning Algorithm Hyperparameters for Fast Parametric Convex Optimization	7
The Geometric Median and Applications to Robust Mean Estimation	7
Finite-Time Analysis of Natural Actor-Critic for POMDPs	7
Random Multitype Spanning Forests for Synchronization on Sparse Graphs	7
Supervised Gromov–Wasserstein Optimal Transport with Metric-Preserving Constraints	7
On Neural Network Approximation of Ideal Adversarial Attack and Convergence of Adversarial Training	6
Optimal Dorfman Group Testing for Symmetric Distributions	6
Convergence of Gradient Descent for Recurrent Neural Networks: A Nonasymptotic Analysis	6
Bi-Invariant Dissimilarity Measures for Sample Distributions in Lie Groups	6
Numerical Considerations and a new implementation for invariant coordinate selection	6
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	5
Efficient Identification of Butterfly Sparse Matrix Factorizations	5
Phase Retrieval with Semialgebraic and ReLU Neural Network Priors	5
Post-training Quantization for Neural Networks with Provable Guarantees	5
ABBA Neural Networks: Coping with Positivity, Expressivity, and Robustness	5
Operator Shifting for General Noisy Matrix Systems	5
Adaptive Joint Distribution Learning	4
HADES: Fast Singularity Detection with Local Measure Comparison	4
KL Convergence Guarantees for Score Diffusion Models under Minimal Data Assumptions	4
Spectral Properties of Elementwise-Transformed Spiked Matrices	4
A Priori Estimates for Deep Residual Network in Continuous-Time Reinforcement Learning	4
Optimality Conditions for Nonsmooth Nonconvex-Nonconcave Min-Max Problems and Generative Adversarial Networks	4
Fast Kernel Summation in High Dimensions via Slicing and Fourier Transforms	4
Stability of Sequential Lateration and of Stress Minimization in the Presence of Noise	4
Stochastic Gradient Descent for Streaming Linear and Rectified Linear Systems with Adversarial Corruptions	4
Robust Reinforcement Learning with Dynamic Distortion Risk Measures	4
Complete and Continuous Invariants of 1-Periodic Sequences in Polynomial Time	4
Accelerated and Instance-Optimal Policy Evaluation with Linear Function Approximation	4
Precise Asymptotics for Spectral Methods in Mixed Generalized Linear Models	4
Stable Gradient-Adjusted Root Mean Square Propagation on Least Squares Problem	4
Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices	4
An Adaptively Inexact First-Order Method for Bilevel Optimization with Application to Hyperparameter Learning	3
Optimization on Manifolds via Graph Gaussian Processes	3
Entropic Optimal Transport on Random Graphs	3
On the Rates of Convergence for Learning with Convolutional Neural Networks	3
Causal Structural Learning via Local Graphs	3
Approximate Q Learning for Controlled Diffusion Processes and Its Near Optimality	3
Exploring Variance Reduction in Importance Sampling for Efficient DNN Training	3
Fast and Simple Multiclass Data Segmentation: An Eigendecomposition and Projection-Free Approach	3
Efficiency of ETA Prediction	3

Simple Alternating Minimization Provably Solves Complete Dictionary Learning	3
Lipschitz-Regularized Gradient Flows and Generative Particle Algorithms for High-Dimensional Scarce Data	3
Kernel Interpolation on Generalized Sparse Grids	3
Approximate Message Passing with Rigorous Guarantees for Pooled Data and Quantitative Group Testing	3
An Extrapolated and Provably Convergent Algorithm for Nonlinear Matrix Decomposition with the ReLU Function	3
First-Order Conditions for Optimization in the Wasserstein Space	3
Stochastic Optimal Transport in Banach Spaces for Regularized Estimation of Multivariate Quantiles	3
On Bellman Equations for Continuous-Time Policy Evaluation: High-Order Discretization and Function Approximation	3
Diffeomorphic Measure Matching with Kernels for Generative Modeling	3
Network Online Change Point Localization	3
Insights into Kernel PCA with Application to Multivariate Extremes	3
Approximating Probability Distributions by Using Wasserstein Generative Adversarial Networks	3
Ensemble Linear Interpolators: The Role of Ensembling	3
Sharp Analysis of Sketch-and-Project Methods via a Connection to Randomized Singular Value Decomposition	3
The Common Intuition to Transfer Learning Can Win or Lose: Case Studies for Linear Regression	3
On Design of Polyhedral Estimates in Linear Inverse Problems	2
Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks	2
On the Nonconvexity of Push-Forward Constraints and Its Consequences in Machine Learning	2
Estimating a Potential Without the Agony of the Partition Function	2
Landmark Alternating Diffusion	2
Determinantal Point Processes Implicitly Regularize Semiparametric Regression Problems	2
The Positivity of the Neural Tangent Kernel	2
Fredholm Integral Equations for Function Approximation and the Training of Neural Networks	2
Optimally Weighted PCA for High-Dimensional Heteroscedastic Data	2
Faster Rates for Compressed Federated Learning with Client-Variance Reduction	2
Improving Implicit Regularization of SGD with Preconditioning for Least Square Problems	2
Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing	2
Denoising Guarantees for Optimized Sampling Schemes in Compressed Sensing	2
Online MCMC Thinning with Kernelized Stein Discrepancy	2
Exact Bayesian Gaussian Cox Processes Using Random Integrals	2
\({O({k})}\)-Equivariant Dimensionality Reduction on Stiefel Manifolds	2
Accelerated Bregman Primal-Dual Methods Applied to Optimal Transport and Wasserstein Barycenter Problems	2
Applications of No-Collision Transportation Maps in Manifold Learning	2
Principles for Initialization and Architecture Selection in Graph Neural Networks with ReLU Activations	2
Enforcing Katz and PageRank Centrality Measures in Complex Networks	2