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 2021-10-01 to 2025-10-01.)

Article	Citations
Taming Neural Networks with TUSLA: Nonconvex Learning via Adaptive Stochastic Gradient Langevin Algorithms	35
A Simple and Optimal Algorithm for Strict Circular Seriation	28
Spectral Barron Space for Deep Neural Network Approximation	26
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization	24
On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions	22
Randomized Nyström Approximation of Non-negative Self-Adjoint Operators	22
A Note on the Regularity of Images Generated by Convolutional Neural Networks	21
New Equivalences between Interpolation and SVMs: Kernels and Structured Features	20
Poisson Reweighted Laplacian Uncertainty Sampling for Graph-Based Active Learning	17
Block Majorization Minimization with Extrapolation and Application to ${\beta }$-NMF	15
Resolving the Mixing Time of the Langevin Algorithm to Its Stationary Distribution for Log-Concave Sampling	14
Learning Functions Varying along a Central Subspace	14
Quantitative Approximation Results for Complex-Valued Neural Networks	13
Efficient Algorithms for Regularized Nonnegative Scale-Invariant Low-Rank Approximation Models	13
Online Machine Teaching under Learner Uncertainty: Gradient Descent Learners of a Quadratic Loss	12
Nonbacktracking Spectral Clustering of Nonuniform Hypergraphs	12
Nonlinear Tomographic Reconstruction via Nonsmooth Optimization	11
Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning	11
Persistent Laplacians: Properties, Algorithms and Implications	11
Safe Rules for the Identification of Zeros in the Solutions of the SLOPE Problem	10
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	9
The GenCol Algorithm for High-Dimensional Optimal Transport: General Formulation and Application to Barycenters and Wasserstein Splines	9
Function-Space Optimality of Neural Architectures with Multivariate Nonlinearities	9

Inverse Evolution Layers: Physics-Informed Regularizers for Image Segmentation	9
Stochastic Variance-Reduced Majorization-Minimization Algorithms	9
A Variational Formulation of Accelerated Optimization on Riemannian Manifolds	9
Asymptotics of the Sketched Pseudoinverse	8
Covariance Alignment: From Maximum Likelihood Estimation to Gromov–Wasserstein	8
Group-Invariant Tensor Train Networks for Supervised Learning	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
Convergence of Gradient Descent for Recurrent Neural Networks: A Nonasymptotic Analysis	7
Numerical Considerations and a new implementation for invariant coordinate selection	7
Supervised Gromov–Wasserstein Optimal Transport with Metric-Preserving Constraints	7
Optimal Dorfman Group Testing for Symmetric Distributions	7
Bi-Invariant Dissimilarity Measures for Sample Distributions in Lie Groups	7
Benefit of Interpolation in Nearest Neighbor Algorithms	7
Finite-Time Analysis of Natural Actor-Critic for POMDPs	7
The Geometric Median and Applications to Robust Mean Estimation	7
A Nonlinear Matrix Decomposition for Mining the Zeros of Sparse Data	6
Adaptive Joint Distribution Learning	6
Computing Wasserstein Barycenters via Operator Splitting: The Method of Averaged Marginals	6
ABBA Neural Networks: Coping with Positivity, Expressivity, and Robustness	6
Efficient Identification of Butterfly Sparse Matrix Factorizations	6
LASSO Reloaded: A Variational Analysis Perspective with Applications to Compressed Sensing	6
Post-training Quantization for Neural Networks with Provable Guarantees	6
Robust Classification Under $\ell_0$ Attack for the Gaussian Mixture Model	5
Optimality Conditions for Nonsmooth Nonconvex-Nonconcave Min-Max Problems and Generative Adversarial Networks	5
Randomized Wasserstein Barycenter Computation: Resampling with Statistical Guarantees	5
Operator Shifting for General Noisy Matrix Systems	5
Accelerated and Instance-Optimal Policy Evaluation with Linear Function Approximation	5
Memory Capacity of Two Layer Neural Networks with Smooth Activations	5
Sequential Construction and Dimension Reduction of Gaussian Processes Under Inequality Constraints	5
Fast Kernel Summation in High Dimensions via Slicing and Fourier Transforms	5
Stochastic Gradient Descent for Streaming Linear and Rectified Linear Systems with Adversarial Corruptions	4
Convergence of a Constrained Vector Extrapolation Scheme	4
KL Convergence Guarantees for Score Diffusion Models under Minimal Data Assumptions	4
Insights into Kernel PCA with Application to Multivariate Extremes	4
Lipschitz-Regularized Gradient Flows and Generative Particle Algorithms for High-Dimensional Scarce Data	4
A Priori Estimates for Deep Residual Network in Continuous-Time Reinforcement Learning	4
Spectral Properties of Elementwise-Transformed Spiked Matrices	4
Sensitivity-Informed Provable Pruning of Neural Networks	4
Stability of Sequential Lateration and of Stress Minimization in the Presence of Noise	4
Approximating Probability Distributions by Using Wasserstein Generative Adversarial Networks	4
Ensemble Linear Interpolators: The Role of Ensembling	4
Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices	4
A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations	4
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning	4
A Generalized CUR Decomposition for Matrix Pairs	4
Nonlinear Weighted Directed Acyclic Graph and A Priori Estimates for Neural Networks	4
Network Online Change Point Localization	3
Simple Alternating Minimization Provably Solves Complete Dictionary Learning	3
First-Order Conditions for Optimization in the Wasserstein Space	3

Causal Structural Learning via Local Graphs	3
Approximate Message Passing with Rigorous Guarantees for Pooled Data and Quantitative Group Testing	3
Entropic Optimal Transport on Random Graphs	3
Block Bregman Majorization Minimization with Extrapolation	3
Optimization on Manifolds via Graph Gaussian Processes	3
Accelerated Bregman Primal-Dual Methods Applied to Optimal Transport and Wasserstein Barycenter Problems	2
Efficiency of ETA Prediction	2
The Common Intuition to Transfer Learning Can Win or Lose: Case Studies for Linear Regression	2
On the Nonconvexity of Push-Forward Constraints and Its Consequences in Machine Learning	2
Sharp Analysis of Sketch-and-Project Methods via a Connection to Randomized Singular Value Decomposition	2
Estimating a Potential Without the Agony of the Partition Function	2
Binary Classification of Gaussian Mixtures: Abundance of Support Vectors, Benign Overfitting, and Regularization	2
Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks	2
Diffeomorphic Measure Matching with Kernels for Generative Modeling	2
Approximate Q Learning for Controlled Diffusion Processes and Its Near Optimality	2
${O({k})}$-Equivariant Dimensionality Reduction on Stiefel Manifolds	2
Optimally Weighted PCA for High-Dimensional Heteroscedastic Data	2
$k$-Variance: A Clustered Notion of Variance	2
Positive Semi-definite Embedding for Dimensionality Reduction and Out-of-Sample Extensions	2
Applications of No-Collision Transportation Maps in Manifold Learning	2
Faster Rates for Compressed Federated Learning with Client-Variance Reduction	2
Wasserstein-Based Projections with Applications to Inverse Problems	2
An Adaptively Inexact First-Order Method for Bilevel Optimization with Application to Hyperparameter Learning	2
Enforcing Katz and PageRank Centrality Measures in Complex Networks	1
Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing	1
Finding Planted Cliques Using Gradient Descent	1
The Positivity of the Neural Tangent Kernel	1
Biwhitening Reveals the Rank of a Count Matrix	1
Statistical Methods for Minimax Estimation in Linear Models with Unknown Design Over Finite Alphabets	1
Sharp Estimates on Random Hyperplane Tessellations	1
Overcomplete Order-3 Tensor Decomposition, Blind Deconvolution, and Gaussian Mixture Models	1
Federated Primal Dual Fixed Point Algorithm	1
What Kinds of Functions Do Deep Neural Networks Learn? Insights from Variational Spline Theory	1
High-Dimensional Analysis of Double Descent for Linear Regression with Random Projections	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
Determinantal Point Processes Implicitly Regularize Semiparametric Regression Problems	1
Principles for Initialization and Architecture Selection in Graph Neural Networks with ReLU Activations	1
Fast Cluster Detection in Networks by First Order Optimization	1
Corrigendum: Post-training Quantization for Neural Networks with Provable Guarantees	1
Random Projection Neural Networks of Best Approximation: Convergence Theory and Practical Applications	1
Two Steps at a Time---Taking GAN Training in Stride with Tseng's Method	1
Topological Fingerprints for Audio Identification	1
On Design of Polyhedral Estimates in Linear Inverse Problems	1
Core-Periphery Detection in Hypergraphs	1
A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictors	1
Target Network and Truncation Overcome the Deadly Triad in $\boldsymbol{Q}$-Learning	1
ReLU Neural Networks with Linear Layers Are Biased towards Single- and Multi-index Models	1
Randomly Initialized Alternating Least Squares: Fast Convergence for Matrix Sensing	1
Online MCMC Thinning with Kernelized Stein Discrepancy	1
Identifying 3D Genome Organization in Diploid Organisms via Euclidean Distance Geometry	1
Landmark Alternating Diffusion	1
Structural Balance and Random Walks on Complex Networks with Complex Weights	1
Data-Driven Mirror Descent with Input-Convex Neural Networks	1
Overparameterization and Generalization Error: Weighted Trigonometric Interpolation	1
Clustering in Pure-Attention Hardmax Transformers and Its Role in Sentiment Analysis	1
Gradient Descent in the Absence of Global Lipschitz Continuity of the Gradients	1
Approximation Bounds for Sparse Programs	1
Maximum a Posteriori Inference for Factor Graphs via Benders’ Decomposition	1
When Big Data Actually Are Low-Rank, or Entrywise Approximation of Certain Function-Generated Matrices	1
Nonparametric Finite Mixture Models with Possible Shape Constraints: A Cubic Newton Approach	1
Intrinsic Dimension Adaptive Partitioning for Kernel Methods	1