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 2022-06-01 to 2026-06-01.)

Article	Citations
Spectral Barron Space for Deep Neural Network Approximation	47
A Simple and Optimal Algorithm for Strict Circular Seriation	35
A Note on the Regularity of Images Generated by Convolutional Neural Networks	34
Taming Neural Networks with TUSLA: Nonconvex Learning via Adaptive Stochastic Gradient Langevin Algorithms	34
Learning Functions Varying along a Central Subspace	28
Efficient Algorithms for Regularized Nonnegative Scale-Invariant Low-Rank Approximation Models	22
On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions	21
Resolving the Mixing Time of the Langevin Algorithm to Its Stationary Distribution for Log-Concave Sampling	19
Poisson Reweighted Laplacian Uncertainty Sampling for Graph-Based Active Learning	17
Block Majorization Minimization with Extrapolation and Application to ${\beta }$-NMF	17
New Equivalences between Interpolation and SVMs: Kernels and Structured Features	15
Randomized Nyström Approximation of Non-negative Self-Adjoint Operators	15
Deep Block Proximal Linearized Minimization Algorithm for Nonconvex Inverse Problems	15
Online Machine Teaching under Learner Uncertainty: Gradient Descent Learners of a Quadratic Loss	14
Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning	12
Nonlinear Tomographic Reconstruction via Nonsmooth Optimization	12
Nonbacktracking Spectral Clustering of Nonuniform Hypergraphs	12
Safe Rules for the Identification of Zeros in the Solutions of the SLOPE Problem	12
Convergence of a Piggyback-Style Method for the Differentiation of Solutions of Standard Saddle-Point Problems	11
Persistent Laplacians: Properties, Algorithms and Implications	11
Scalable Tensor Methods for Nonuniform Hypergraphs	11
Nonlinear Meta-learning Can Guarantee Faster Rates	10
Covariance Alignment: From Maximum Likelihood Estimation to Gromov–Wasserstein	9
Function-Space Optimality of Neural Architectures with Multivariate Nonlinearities	9
Stochastic Variance-Reduced Majorization-Minimization Algorithms	9

Learning Memory Kernels in Generalized Langevin Equations	9
Asymptotics of the Sketched Pseudoinverse	9
CA-PCA: Manifold Dimension Estimation, Adapted for Curvature	9
A Notion of Uniqueness for the Adversarial Bayes Classifier	9
Convergence of Gradient Descent for Recurrent Neural Networks: A Nonasymptotic Analysis	8
Inverse Evolution Layers: Physics-Informed Regularizers for Image Segmentation	8
On Neural Network Approximation of Ideal Adversarial Attack and Convergence of Adversarial Training	8
Optimal Dorfman Group Testing for Symmetric Distributions	8
A Variational Formulation of Accelerated Optimization on Riemannian Manifolds	8
Group-Invariant Tensor Train Networks for Supervised Learning	8
The GenCol Algorithm for High-Dimensional Optimal Transport: General Formulation and Application to Barycenters and Wasserstein Splines	8
Bi-Invariant Dissimilarity Measures for Sample Distributions in Lie Groups	8
The Sample Complexity of Sparse Multireference Alignment and Single-Particle Cryo-Electron Microscopy	8
Random Multitype Spanning Forests for Synchronization on Sparse Graphs	8
Supervised Gromov–Wasserstein Optimal Transport with Metric-Preserving Constraints	8
Finite-Time Analysis of Natural Actor-Critic for POMDPs	7
Efficient Identification of Butterfly Sparse Matrix Factorizations	7
Benefit of Interpolation in Nearest Neighbor Algorithms	7
The Geometric Median and Applications to Robust Mean Estimation	7
Numerical Considerations and a new implementation for invariant coordinate selection	7
ABBA Neural Networks: Coping with Positivity, Expressivity, and Robustness	6
Computing Wasserstein Barycenters via Operator Splitting: The Method of Averaged Marginals	6
Post-training Quantization for Neural Networks with Provable Guarantees	6
LASSO Reloaded: A Variational Analysis Perspective with Applications to Compressed Sensing	6
Operator Shifting for General Noisy Matrix Systems	6
Complete and Continuous Invariants of 1-Periodic Sequences in Polynomial Time	5
Memory Capacity of Two Layer Neural Networks with Smooth Activations	5
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning	5
Adaptive Joint Distribution Learning	5
Phase Retrieval with Semialgebraic and ReLU Neural Network Priors	5
Optimality Conditions for Nonsmooth Nonconvex-Nonconcave Min-Max Problems and Generative Adversarial Networks	5
Sequential Construction and Dimension Reduction of Gaussian Processes Under Inequality Constraints	5
Accelerated and Instance-Optimal Policy Evaluation with Linear Function Approximation	4
Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices	4
HADES: Fast Singularity Detection with Local Measure Comparison	4
Fast Kernel Summation in High Dimensions via Slicing and Fourier Transforms	4
Spectral Properties of Elementwise-Transformed Spiked Matrices	4
KL Convergence Guarantees for Score Diffusion Models under Minimal Data Assumptions	4
Stability of Sequential Lateration and of Stress Minimization in the Presence of Noise	4
Convergence of a Constrained Vector Extrapolation Scheme	4
Kernel Interpolation on Generalized Sparse Grids	3
On the Rates of Convergence for Learning with Convolutional Neural Networks	3
Stochastic Gradient Descent for Streaming Linear and Rectified Linear Systems with Adversarial Corruptions	3
Network Online Change Point Localization	3
Entropic Optimal Transport on Random Graphs	3
Diffeomorphic Measure Matching with Kernels for Generative Modeling	3
Efficiency of ETA Prediction	3
Exploring Variance Reduction in Importance Sampling for Efficient DNN Training	3
First-Order Conditions for Optimization in the Wasserstein Space	3
Robust Reinforcement Learning with Dynamic Distortion Risk Measures	3

Lipschitz-Regularized Gradient Flows and Generative Particle Algorithms for High-Dimensional Scarce Data	3
Insights into Kernel PCA with Application to Multivariate Extremes	3
Nonlinear Weighted Directed Acyclic Graph and A Priori Estimates for Neural Networks	3
Optimization on Manifolds via Graph Gaussian Processes	3
$k$-Variance: A Clustered Notion of Variance	3
An Adaptively Inexact First-Order Method for Bilevel Optimization with Application to Hyperparameter Learning	3
Stochastic Optimal Transport in Banach Spaces for Regularized Estimation of Multivariate Quantiles	3
Ensemble Linear Interpolators: The Role of Ensembling	3
Approximating Probability Distributions by Using Wasserstein Generative Adversarial Networks	3
A Priori Estimates for Deep Residual Network in Continuous-Time Reinforcement Learning	3
Simple Alternating Minimization Provably Solves Complete Dictionary Learning	3
Causal Structural Learning via Local Graphs	3
Approximate Message Passing with Rigorous Guarantees for Pooled Data and Quantitative Group Testing	3
Approximate Q Learning for Controlled Diffusion Processes and Its Near Optimality	3
The Common Intuition to Transfer Learning Can Win or Lose: Case Studies for Linear Regression	3
Sharp Analysis of Sketch-and-Project Methods via a Connection to Randomized Singular Value Decomposition	3
Principles for Initialization and Architecture Selection in Graph Neural Networks with ReLU Activations	2
Online MCMC Thinning with Kernelized Stein Discrepancy	2
Accelerated Bregman Primal-Dual Methods Applied to Optimal Transport and Wasserstein Barycenter Problems	2
Faster Rates for Compressed Federated Learning with Client-Variance Reduction	2
On the Nonconvexity of Push-Forward Constraints and Its Consequences in Machine Learning	2
The Positivity of the Neural Tangent Kernel	2
Enforcing Katz and PageRank Centrality Measures in Complex Networks	2
On Design of Polyhedral Estimates in Linear Inverse Problems	2
Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks	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
Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing	2
Fredholm Integral Equations for Function Approximation and the Training of Neural Networks	2
Applications of No-Collision Transportation Maps in Manifold Learning	2
${O({k})}$-Equivariant Dimensionality Reduction on Stiefel Manifolds	2
Optimally Weighted PCA for High-Dimensional Heteroscedastic Data	2
Energy-Based Sequential Sampling for Low-Rank PSD-Matrix Approximation	2