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 2021-12-01 to 2025-12-01.)

Article	Citations
Spectral Barron Space for Deep Neural Network Approximation	40
A Simple and Optimal Algorithm for Strict Circular Seriation	31
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization	29
Taming Neural Networks with TUSLA: Nonconvex Learning via Adaptive Stochastic Gradient Langevin Algorithms	28
Randomized Nyström Approximation of Non-negative Self-Adjoint Operators	23
On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions	23
A Note on the Regularity of Images Generated by Convolutional Neural Networks	23
New Equivalences between Interpolation and SVMs: Kernels and Structured Features	22
Poisson Reweighted Laplacian Uncertainty Sampling for Graph-Based Active Learning	17
Block Majorization Minimization with Extrapolation and Application to ${\beta }$-NMF	17
Resolving the Mixing Time of the Langevin Algorithm to Its Stationary Distribution for Log-Concave Sampling	16
Learning Functions Varying along a Central Subspace	14
Deep Block Proximal Linearized Minimization Algorithm for Nonconvex Inverse Problems	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	13
Quantitative Approximation Results for Complex-Valued Neural Networks	13
Nonlinear Tomographic Reconstruction via Nonsmooth Optimization	12
Persistent Laplacians: Properties, Algorithms and Implications	11
Nonbacktracking Spectral Clustering of Nonuniform Hypergraphs	11
Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning	11
Safe Rules for the Identification of Zeros in the Solutions of the SLOPE Problem	11
Nonlinear Meta-learning Can Guarantee Faster Rates	10
CA-PCA: Manifold Dimension Estimation, Adapted for Curvature	10
Scalable Tensor Methods for Nonuniform Hypergraphs	9
Function-Space Optimality of Neural Architectures with Multivariate Nonlinearities	9

Inverse Evolution Layers: Physics-Informed Regularizers for Image Segmentation	9
Convergence of a Piggyback-Style Method for the Differentiation of Solutions of Standard Saddle-Point Problems	9
Asymptotics of the Sketched Pseudoinverse	9
Covariance Alignment: From Maximum Likelihood Estimation to Gromov–Wasserstein	9
Stochastic Variance-Reduced Majorization-Minimization Algorithms	9
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
On Neural Network Approximation of Ideal Adversarial Attack and Convergence of Adversarial Training	8
Random Multitype Spanning Forests for Synchronization on Sparse Graphs	8
Convergence of Gradient Descent for Recurrent Neural Networks: A Nonasymptotic Analysis	8
Group-Invariant Tensor Train Networks for Supervised Learning	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
The Geometric Median and Applications to Robust Mean Estimation	7
Supervised Gromov–Wasserstein Optimal Transport with Metric-Preserving Constraints	7
Benefit of Interpolation in Nearest Neighbor Algorithms	7
Computing Wasserstein Barycenters via Operator Splitting: The Method of Averaged Marginals	7
Optimal Dorfman Group Testing for Symmetric Distributions	7
Numerical Considerations and a new implementation for invariant coordinate selection	7
Efficient Identification of Butterfly Sparse Matrix Factorizations	7
Finite-Time Analysis of Natural Actor-Critic for POMDPs	7
A Nonlinear Matrix Decomposition for Mining the Zeros of Sparse Data	7
ABBA Neural Networks: Coping with Positivity, Expressivity, and Robustness	6
Robust Classification Under $\ell_0$ Attack for the Gaussian Mixture Model	6
Complete and Continuous Invariants of 1-Periodic Sequences in Polynomial Time	6
Post-training Quantization for Neural Networks with Provable Guarantees	6
Operator Shifting for General Noisy Matrix Systems	6
Randomized Wasserstein Barycenter Computation: Resampling with Statistical Guarantees	6
LASSO Reloaded: A Variational Analysis Perspective with Applications to Compressed Sensing	6
Adaptive Joint Distribution Learning	6
Sequential Construction and Dimension Reduction of Gaussian Processes Under Inequality Constraints	6
Fast Kernel Summation in High Dimensions via Slicing and Fourier Transforms	5
Sensitivity-Informed Provable Pruning of Neural Networks	5
KL Convergence Guarantees for Score Diffusion Models under Minimal Data Assumptions	5
Accelerated and Instance-Optimal Policy Evaluation with Linear Function Approximation	5
Memory Capacity of Two Layer Neural Networks with Smooth Activations	5
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning	5
HADES: Fast Singularity Detection with Local Measure Comparison	5
Phase Retrieval with Semialgebraic and ReLU Neural Network Priors	5
A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations	5
Optimality Conditions for Nonsmooth Nonconvex-Nonconcave Min-Max Problems and Generative Adversarial Networks	5
On the Rates of Convergence for Learning with Convolutional Neural Networks	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
Approximating Probability Distributions by Using Wasserstein Generative Adversarial Networks	4
A Priori Estimates for Deep Residual Network in Continuous-Time Reinforcement Learning	4
Convergence of a Constrained Vector Extrapolation Scheme	4
A Generalized CUR Decomposition for Matrix Pairs	4
Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices	4
Spectral Properties of Elementwise-Transformed Spiked Matrices	4

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