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 2021-11-01 to 2025-11-01.)

Article	Citations
Taming Neural Networks with TUSLA: Nonconvex Learning via Adaptive Stochastic Gradient Langevin Algorithms	36
A Simple and Optimal Algorithm for Strict Circular Seriation	29
Spectral Barron Space for Deep Neural Network Approximation	29
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization	26
On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions	23
Randomized Nyström Approximation of Non-negative Self-Adjoint Operators	22
A Note on the Regularity of Images Generated by Convolutional Neural Networks	22
New Equivalences between Interpolation and SVMs: Kernels and Structured Features	20
Poisson Reweighted Laplacian Uncertainty Sampling for Graph-Based Active Learning	17
Resolving the Mixing Time of the Langevin Algorithm to Its Stationary Distribution for Log-Concave Sampling	16
Block Majorization Minimization with Extrapolation and Application to ${\beta }$-NMF	16
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
Deep Block Proximal Linearized Minimization Algorithm for Nonconvex Inverse Problems	13
Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning	12
Nonlinear Tomographic Reconstruction via Nonsmooth Optimization	11
Nonbacktracking Spectral Clustering of Nonuniform Hypergraphs	11
Online Machine Teaching under Learner Uncertainty: Gradient Descent Learners of a Quadratic Loss	11
Persistent Laplacians: Properties, Algorithms and Implications	11
Safe Rules for the Identification of Zeros in the Solutions of the SLOPE Problem	10
CA-PCA: Manifold Dimension Estimation, Adapted for Curvature	10
Nonlinear Meta-learning Can Guarantee Faster Rates	9
A Variational Formulation of Accelerated Optimization on Riemannian Manifolds	9
Function-Space Optimality of Neural Architectures with Multivariate Nonlinearities	9

Scalable Tensor Methods for Nonuniform Hypergraphs	9
Asymptotics of the Sketched Pseudoinverse	9
Convergence of a Piggyback-Style Method for the Differentiation of Solutions of Standard Saddle-Point Problems	9
Stochastic Variance-Reduced Majorization-Minimization Algorithms	9
Inverse Evolution Layers: Physics-Informed Regularizers for Image Segmentation	9
Random Multitype Spanning Forests for Synchronization on Sparse Graphs	8
Covariance Alignment: From Maximum Likelihood Estimation to Gromov–Wasserstein	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
The Sample Complexity of Sparse Multireference Alignment and Single-Particle Cryo-Electron Microscopy	8
Numerical Considerations and a new implementation for invariant coordinate selection	7
The Geometric Median and Applications to Robust Mean Estimation	7
Finite-Time Analysis of Natural Actor-Critic for POMDPs	7
Benefit of Interpolation in Nearest Neighbor Algorithms	7
Bi-Invariant Dissimilarity Measures for Sample Distributions in Lie Groups	7
Convergence of Gradient Descent for Recurrent Neural Networks: A Nonasymptotic Analysis	7
On Neural Network Approximation of Ideal Adversarial Attack and Convergence of Adversarial Training	7
Optimal Dorfman Group Testing for Symmetric Distributions	6
LASSO Reloaded: A Variational Analysis Perspective with Applications to Compressed Sensing	6
Adaptive Joint Distribution Learning	6
Supervised Gromov–Wasserstein Optimal Transport with Metric-Preserving Constraints	6
Efficient Identification of Butterfly Sparse Matrix Factorizations	6
ABBA Neural Networks: Coping with Positivity, Expressivity, and Robustness	6
Memory Capacity of Two Layer Neural Networks with Smooth Activations	6
A Nonlinear Matrix Decomposition for Mining the Zeros of Sparse Data	6
Post-training Quantization for Neural Networks with Provable Guarantees	6
Computing Wasserstein Barycenters via Operator Splitting: The Method of Averaged Marginals	6
Complete and Continuous Invariants of 1-Periodic Sequences in Polynomial Time	5
Operator Shifting for General Noisy Matrix Systems	5
Phase Retrieval with Semialgebraic and ReLU Neural Network Priors	5
Accelerated and Instance-Optimal Policy Evaluation with Linear Function Approximation	5
Randomized Wasserstein Barycenter Computation: Resampling with Statistical Guarantees	5
A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations	5
Fast Kernel Summation in High Dimensions via Slicing and Fourier Transforms	5
Sequential Construction and Dimension Reduction of Gaussian Processes Under Inequality Constraints	5
Robust Classification Under $\ell_0$ Attack for the Gaussian Mixture Model	5
KL Convergence Guarantees for Score Diffusion Models under Minimal Data Assumptions	5
Insights into Kernel PCA with Application to Multivariate Extremes	4
Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices	4
Spectral Properties of Elementwise-Transformed Spiked Matrices	4
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning	4
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
Optimality Conditions for Nonsmooth Nonconvex-Nonconcave Min-Max Problems and 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
Sensitivity-Informed Provable Pruning of Neural Networks	4
Ensemble Linear Interpolators: The Role of Ensembling	3
Block Bregman Majorization Minimization with Extrapolation	3

Approximating Probability Distributions by Using Wasserstein Generative Adversarial Networks	3
Nonlinear Weighted Directed Acyclic Graph and A Priori Estimates for Neural Networks	3
Causal Structural Learning via Local Graphs	3
A Generalized CUR Decomposition for Matrix Pairs	3
Lipschitz-Regularized Gradient Flows and Generative Particle Algorithms for High-Dimensional Scarce Data	3
Entropic Optimal Transport on Random Graphs	3
Applications of No-Collision Transportation Maps in Manifold Learning	2
An Adaptively Inexact First-Order Method for Bilevel Optimization with Application to Hyperparameter Learning	2
Approximate Q Learning for Controlled Diffusion Processes and Its Near Optimality	2
Approximate Message Passing with Rigorous Guarantees for Pooled Data and Quantitative Group Testing	2
First-Order Conditions for Optimization in the Wasserstein Space	2
Binary Classification of Gaussian Mixtures: Abundance of Support Vectors, Benign Overfitting, and Regularization	2
Faster Rates for Compressed Federated Learning with Client-Variance Reduction	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
Positive Semi-definite Embedding for Dimensionality Reduction and Out-of-Sample Extensions	2
Network Online Change Point Localization	2
Efficiency of ETA Prediction	2
$k$-Variance: A Clustered Notion of Variance	2
Sharp Analysis of Sketch-and-Project Methods via a Connection to Randomized Singular Value Decomposition	2
Optimally Weighted PCA for High-Dimensional Heteroscedastic Data	2
${O({k})}$-Equivariant Dimensionality Reduction on Stiefel Manifolds	2
Diffeomorphic Measure Matching with Kernels for Generative Modeling	2
Optimization on Manifolds via Graph Gaussian Processes	2
Simple Alternating Minimization Provably Solves Complete Dictionary Learning	2
The Common Intuition to Transfer Learning Can Win or Lose: Case Studies for Linear Regression	2
Stochastic Optimal Transport in Banach Spaces for Regularized Estimation of Multivariate Quantiles	2
Statistical Methods for Minimax Estimation in Linear Models with Unknown Design Over Finite Alphabets	1
Gradient Descent in the Absence of Global Lipschitz Continuity of the Gradients	1
Energy-Based Sequential Sampling for Low-Rank PSD-Matrix Approximation	1
ReLU Neural Networks with Linear Layers Are Biased towards Single- and Multi-index Models	1
Two Steps at a Time---Taking GAN Training in Stride with Tseng's Method	1
When Big Data Actually Are Low-Rank, or Entrywise Approximation of Certain Function-Generated Matrices	1
Fredholm Integral Equations for Function Approximation and the Training of Neural Networks	1
Fast Cluster Detection in Networks by First Order Optimization	1
Maximum a Posteriori Inference for Factor Graphs via Benders’ Decomposition	1
Determinantal Point Processes Implicitly Regularize Semiparametric Regression Problems	1
Online MCMC Thinning with Kernelized Stein Discrepancy	1
Accelerated Bregman Primal-Dual Methods Applied to Optimal Transport and Wasserstein Barycenter Problems	1
Finding Planted Cliques Using Gradient Descent	1
A Generative Variational Model for Inverse Problems in Imaging	1
Biwhitening Reveals the Rank of a Count Matrix	1
Structural Balance and Random Walks on Complex Networks with Complex Weights	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
Nonparametric Finite Mixture Models with Possible Shape Constraints: A Cubic Newton Approach	1
What Kinds of Functions Do Deep Neural Networks Learn? Insights from Variational Spline Theory	1
On Design of Polyhedral Estimates in Linear Inverse Problems	1
Wasserstein-Based Projections with Applications to Inverse Problems	1
A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictors	1
Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing	1
Landmark Alternating Diffusion	1
Intrinsic Dimension Adaptive Partitioning for Kernel Methods	1
Identifying 3D Genome Organization in Diploid Organisms via Euclidean Distance Geometry	1
Data-Driven Mirror Descent with Input-Convex Neural Networks	1
Random Projection Neural Networks of Best Approximation: Convergence Theory and Practical Applications	1
Sharp Estimates on Random Hyperplane Tessellations	1
Overparameterization and Generalization Error: Weighted Trigonometric Interpolation	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
Randomly Initialized Alternating Least Squares: Fast Convergence for Matrix Sensing	1
Core-Periphery Detection in Hypergraphs	1
Approximation Bounds for Sparse Programs	1
Enforcing Katz and PageRank Centrality Measures in Complex Networks	1
Clustering in Pure-Attention Hardmax Transformers and Its Role in Sentiment Analysis	1
The Positivity of the Neural Tangent Kernel	1
Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks	1
Principles for Initialization and Architecture Selection in Graph Neural Networks with ReLU Activations	1
Corrigendum: Post-training Quantization for Neural Networks with Provable Guarantees	1
SketchySGD: Reliable Stochastic Optimization via Randomized Curvature Estimates	1