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 2021-08-01 to 2025-08-01.)

Article	Citations
Spectral Barron Space for Deep Neural Network Approximation	35
A Simple and Optimal Algorithm for Strict Circular Seriation	26
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization	25
Taming Neural Networks with TUSLA: Nonconvex Learning via Adaptive Stochastic Gradient Langevin Algorithms	23
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
Efficient Algorithms for Regularized Nonnegative Scale-Invariant Low-Rank Approximation Models	19
Learning Functions Varying along a Central Subspace	19
A Note on the Regularity of Images Generated by Convolutional Neural Networks	15
Randomized Nyström Approximation of Non-negative Self-Adjoint Operators	15
New Equivalences between Interpolation and SVMs: Kernels and Structured Features	14
Poisson Reweighted Laplacian Uncertainty Sampling for Graph-Based Active Learning	13
Safe Rules for the Identification of Zeros in the Solutions of the SLOPE Problem	11
Persistent Laplacians: Properties, Algorithms and Implications	11
Nonlinear Tomographic Reconstruction via Nonsmooth Optimization	11
Quantitative Approximation Results for Complex-Valued Neural Networks	11
Nonbacktracking Spectral Clustering of Nonuniform Hypergraphs	11
Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning	10
Scalable Tensor Methods for Nonuniform Hypergraphs	10
Online Machine Teaching under Learner Uncertainty: Gradient Descent Learners of a Quadratic Loss	10
CA-PCA: Manifold Dimension Estimation, Adapted for Curvature	10
Convergence of a Piggyback-Style Method for the Differentiation of Solutions of Standard Saddle-Point Problems	10
Asymptotics of the Sketched Pseudoinverse	9
The GenCol Algorithm for High-Dimensional Optimal Transport: General Formulation and Application to Barycenters and Wasserstein Splines	8
Stochastic Variance-Reduced Majorization-Minimization Algorithms	8

The Sample Complexity of Sparse Multireference Alignment and Single-Particle Cryo-Electron Microscopy	8
Function-Space Optimality of Neural Architectures with Multivariate Nonlinearities	8
A Variational Formulation of Accelerated Optimization on Riemannian Manifolds	7
Finite-Time Analysis of Natural Actor-Critic for POMDPs	7
Supervised Gromov–Wasserstein Optimal Transport with Metric-Preserving Constraints	7
Optimal Dorfman Group Testing for Symmetric Distributions	7
Group-Invariant Tensor Train Networks for Supervised Learning	7
Inverse Evolution Layers: Physics-Informed Regularizers for Image Segmentation	7
A Nonlinear Matrix Decomposition for Mining the Zeros of Sparse Data	7
ABBA Neural Networks: Coping with Positivity, Expressivity, and Robustness	6
The Geometric Median and Applications to Robust Mean Estimation	6
Convergence of Gradient Descent for Recurrent Neural Networks: A Nonasymptotic Analysis	6
Efficient Identification of Butterfly Sparse Matrix Factorizations	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	6
Benefit of Interpolation in Nearest Neighbor Algorithms	6
Post-training Quantization for Neural Networks with Provable Guarantees	5
Randomized Wasserstein Barycenter Computation: Resampling with Statistical Guarantees	5
Operator Shifting for General Noisy Matrix Systems	5
Memory Capacity of Two Layer Neural Networks with Smooth Activations	5
LASSO Reloaded: A Variational Analysis Perspective with Applications to Compressed Sensing	5
KL Convergence Guarantees for Score Diffusion Models under Minimal Data Assumptions	5
Adaptive Joint Distribution Learning	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
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning	4
Fast Kernel Summation in High Dimensions via Slicing and Fourier Transforms	4
Spectral Properties of Elementwise-Transformed Spiked Matrices	4
A Generalized CUR Decomposition for Matrix Pairs	4
Sensitivity-Informed Provable Pruning of Neural Networks	4
Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices	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
Accelerated and Instance-Optimal Policy Evaluation with Linear Function Approximation	4
A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations	4
Optimality Conditions for Nonsmooth Nonconvex-Nonconcave Min-Max Problems and Generative Adversarial Networks	4
Convergence of a Constrained Vector Extrapolation Scheme	4
Insights into Kernel PCA with Application to Multivariate Extremes	3
Nonlinear Weighted Directed Acyclic Graph and A Priori Estimates for Neural Networks	3
Causal Structural Learning via Local Graphs	3
A Priori Estimates for Deep Residual Network in Continuous-Time Reinforcement Learning	3
Block Bregman Majorization Minimization with Extrapolation	3
Approximating Probability Distributions by Using Wasserstein Generative Adversarial Networks	3
Entropic Optimal Transport on Random Graphs	3
Ensemble Linear Interpolators: The Role of Ensembling	2
Efficiency of ETA Prediction	2
Diffeomorphic Measure Matching with Kernels for Generative Modeling	2
Sharp Analysis of Sketch-and-Project Methods via a Connection to Randomized Singular Value Decomposition	2
$k$-Variance: A Clustered Notion of Variance	2

Optimization on Manifolds via Graph Gaussian Processes	2
Network Online Change Point Localization	2
An Adaptively Inexact First-Order Method for Bilevel Optimization with Application to Hyperparameter Learning	2
Binary Classification of Gaussian Mixtures: Abundance of Support Vectors, Benign Overfitting, and Regularization	2
The Common Intuition to Transfer Learning Can Win or Lose: Case Studies for Linear Regression	2
Estimating a Potential Without the Agony of the Partition Function	2
Approximate Message Passing with Rigorous Guarantees for Pooled Data and Quantitative Group Testing	2
Lipschitz-Regularized Gradient Flows and Generative Particle Algorithms for High-Dimensional Scarce Data	2
First-Order Conditions for Optimization in the Wasserstein Space	2
Approximate Q Learning for Controlled Diffusion Processes and Its Near Optimality	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
Positive Semi-definite Embedding for Dimensionality Reduction and Out-of-Sample Extensions	2
Simple Alternating Minimization Provably Solves Complete Dictionary Learning	2
${O({k})}$-Equivariant Dimensionality Reduction on Stiefel Manifolds	1
Applications of No-Collision Transportation Maps in Manifold Learning	1
Randomly Initialized Alternating Least Squares: Fast Convergence for Matrix Sensing	1
The Positivity of the Neural Tangent Kernel	1
Overcomplete Order-3 Tensor Decomposition, Blind Deconvolution, and Gaussian Mixture Models	1
When Big Data Actually Are Low-Rank, or Entrywise Approximation of Certain Function-Generated Matrices	1
Corrigendum: Post-training Quantization for Neural Networks with Provable Guarantees	1
Finding Planted Cliques Using Gradient Descent	1
Federated Primal Dual Fixed Point Algorithm	1
Fast Cluster Detection in Networks by First Order Optimization	1
Energy-Based Sequential Sampling for Low-Rank PSD-Matrix Approximation	1
Wasserstein-Based Projections with Applications to Inverse Problems	1
Principles for Initialization and Architecture Selection in Graph Neural Networks with ReLU Activations	1
Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks	1
Online MCMC Thinning with Kernelized Stein Discrepancy	1
Target Network and Truncation Overcome the Deadly Triad in $\boldsymbol{Q}$-Learning	1
Core-Periphery Detection in Hypergraphs	1
What Kinds of Functions Do Deep Neural Networks Learn? Insights from Variational Spline Theory	1
Two Steps at a Time---Taking GAN Training in Stride with Tseng's Method	1
Intrinsic Dimension Adaptive Partitioning for Kernel Methods	1
Nonparametric Finite Mixture Models with Possible Shape Constraints: A Cubic Newton Approach	1
Identifying 3D Genome Organization in Diploid Organisms via Euclidean Distance Geometry	1
Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing	1
Landmark Alternating Diffusion	1
Optimally Weighted PCA for High-Dimensional Heteroscedastic Data	1
On the Nonconvexity of Push-Forward Constraints and Its Consequences in Machine Learning	1
Fredholm Integral Equations for Function Approximation and the Training of Neural Networks	1
Gradient Descent in the Absence of Global Lipschitz Continuity of the Gradients	1
High-Dimensional Analysis of Double Descent for Linear Regression with Random Projections	1
A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictors	1
Structural Balance and Random Walks on Complex Networks with Complex Weights	1
Approximation Bounds for Sparse Programs	1
On Design of Polyhedral Estimates in Linear Inverse Problems	1
Determinantal Point Processes Implicitly Regularize Semiparametric Regression Problems	1