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 2021-04-01 to 2025-04-01.)

Article	Citations
Moment Estimation for Nonparametric Mixture Models through Implicit Tensor Decomposition	30
Entropic Optimal Transport on Random Graphs	23
Finite-Time Analysis of Natural Actor-Critic for POMDPs	17
Benefit of Interpolation in Nearest Neighbor Algorithms	15
Bi-Invariant Dissimilarity Measures for Sample Distributions in Lie Groups	15
Numerical Considerations and a new implementation for invariant coordinate selection	13
Max-Affine Regression via First-Order Methods	12
Private Sampling: A Noiseless Approach for Generating Differentially Private Synthetic Data	12
Randomly Initialized Alternating Least Squares: Fast Convergence for Matrix Sensing	11
Reversible Gromov–Monge Sampler for Simulation-Based Inference	11
Estimating Gaussian Mixtures Using Sparse Polynomial Moment Systems	11
On Design of Polyhedral Estimates in Linear Inverse Problems	10
A Simple and Optimal Algorithm for Strict Circular Seriation	10
The Geometric Median and Applications to Robust Mean Estimation	10
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization	9
A Nonlinear Matrix Decomposition for Mining the Zeros of Sparse Data	9
Spectral Triadic Decompositions of Real-World Networks	9
Causal Structural Learning via Local Graphs	8
Energy-Based Sequential Sampling for Low-Rank PSD-Matrix Approximation	8
Nonlinear Weighted Directed Acyclic Graph and A Priori Estimates for Neural Networks	8
Fast Cluster Detection in Networks by First Order Optimization	7
Taming Neural Networks with TUSLA: Nonconvex Learning via Adaptive Stochastic Gradient Langevin Algorithms	7
Equivariant Neural Networks for Indirect Measurements	7
Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks	7
Approximation of Lipschitz Functions Using Deep Spline Neural Networks	7

Network Online Change Point Localization	6
Measuring Complexity of Learning Schemes Using Hessian-Schatten Total Variation	6
Balancing Geometry and Density: Path Distances on High-Dimensional Data	6
Optimal Dorfman Group Testing for Symmetric Distributions	6
Supervised Gromov–Wasserstein Optimal Transport with Metric-Preserving Constraints	5
Adversarial Robustness of Sparse Local Lipschitz Predictors	5
Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling	5
Structural Balance and Random Walks on Complex Networks with Complex Weights	5
Fredholm Integral Equations for Function Approximation and the Training of Neural Networks	5
Lipschitz-Regularized Gradient Flows and Generative Particle Algorithms for High-Dimensional Scarce Data	5
Block Bregman Majorization Minimization with Extrapolation	5
New Equivalences between Interpolation and SVMs: Kernels and Structured Features	4
ABBA Neural Networks: Coping with Positivity, Expressivity, and Robustness	4
Learning Functions Varying along a Central Subspace	4
On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions	4
Target Network and Truncation Overcome the Deadly Triad in $\boldsymbol{Q}$-Learning	4
Local Versions of Sum-of-Norms Clustering	4
Approximate Message Passing with Rigorous Guarantees for Pooled Data and Quantitative Group Testing	4
Approximation Bounds for Sparse Programs	4
Poisson Reweighted Laplacian Uncertainty Sampling for Graph-Based Active Learning	4
Markov Kernels Local Aggregation for Noise Vanishing Distribution Sampling	4
Computing Wasserstein Barycenters via Operator Splitting: The Method of Averaged Marginals	3
A Diffusion Process Perspective on Posterior Contraction Rates for Parameters	3
Efficient Identification of Butterfly Sparse Matrix Factorizations	3
Intrinsic Dimension Adaptive Partitioning for Kernel Methods	3
GNMR: A Provable One-Line Algorithm for Low Rank Matrix Recovery	3
Stability of Deep Neural Networks via Discrete Rough Paths	3
Quantitative Approximation Results for Complex-Valued Neural Networks	3
Optimization on Manifolds via Graph Gaussian Processes	3
A Note on the Regularity of Images Generated by Convolutional Neural Networks	3
Two Steps at a Time---Taking GAN Training in Stride with Tseng's Method	2
First-Order Conditions for Optimization in the Wasserstein Space	2
Post-training Quantization for Neural Networks with Provable Guarantees	2
What Kinds of Functions Do Deep Neural Networks Learn? Insights from Variational Spline Theory	2
Spectral Discovery of Jointly Smooth Features for Multimodal Data	2
Federated Primal Dual Fixed Point Algorithm	2
LASSO Reloaded: A Variational Analysis Perspective with Applications to Compressed Sensing	2
Algorithmic Regularization in Model-Free Overparametrized Asymmetric Matrix Factorization	2
Positive Semi-definite Embedding for Dimensionality Reduction and Out-of-Sample Extensions	2
A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictors	2
Nonparametric Finite Mixture Models with Possible Shape Constraints: A Cubic Newton Approach	2
Rigorous Dynamical Mean-Field Theory for Stochastic Gradient Descent Methods	2
High-Dimensional Analysis of Double Descent for Linear Regression with Random Projections	2
Operator Shifting for General Noisy Matrix Systems	2
Efficiency of ETA Prediction	2
Nonbacktracking Spectral Clustering of Nonuniform Hypergraphs	1
Sequential Construction and Dimension Reduction of Gaussian Processes Under Inequality Constraints	1
Time-Inhomogeneous Diffusion Geometry and Topology	1
Subgradient Langevin Methods for Sampling from Nonsmooth Potentials	1
$k$-Variance: A Clustered Notion of Variance	1

Memory Capacity of Two Layer Neural Networks with Smooth Activations	1
Robust Classification Under $\ell_0$ Attack for the Gaussian Mixture Model	1
Wasserstein Barycenters Are NP-Hard to Compute	1
Adaptive Joint Distribution Learning	1
Persistent Laplacians: Properties, Algorithms and Implications	1
Safe Rules for the Identification of Zeros in the Solutions of the SLOPE Problem	1
Core-Periphery Detection in Hypergraphs	1
Efficient Global Optimization of Two-Layer ReLU Networks: Quadratic-Time Algorithms and Adversarial Training	1
Binary Classification of Gaussian Mixtures: Abundance of Support Vectors, Benign Overfitting, and Regularization	1
Three-Operator Splitting for Learning to Predict Equilibria in Convex Games	1
Randomized Wasserstein Barycenter Computation: Resampling with Statistical Guarantees	1
Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning	1
Corrigendum: Post-training Quantization for Neural Networks with Provable Guarantees	1
Joint Community Detection and Rotational Synchronization via Semidefinite Programming	1
An Improved Central Limit Theorem and Fast Convergence Rates for Entropic Transportation Costs	1
Nonasymptotic Bounds for Adversarial Excess Risk under Misspecified Models	1