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 2021-02-01 to 2025-02-01.)

Article	Citations
Moment Estimation for Nonparametric Mixture Models through Implicit Tensor Decomposition	28
Entropic Optimal Transport on Random Graphs	18
Finite-Time Analysis of Natural Actor-Critic for POMDPs	15
Bi-Invariant Dissimilarity Measures for Sample Distributions in Lie Groups	13
Benefit of Interpolation in Nearest Neighbor Algorithms	12
Private Sampling: A Noiseless Approach for Generating Differentially Private Synthetic Data	12
Reversible Gromov–Monge Sampler for Simulation-Based Inference	11
Max-Affine Regression via First-Order Methods	11
A Simple and Optimal Algorithm for Strict Circular Seriation	10
The Geometric Median and Applications to Robust Mean Estimation	10
On Design of Polyhedral Estimates in Linear Inverse Problems	9
A Nonlinear Matrix Decomposition for Mining the Zeros of Sparse Data	9
Numerical Considerations and a new implementation for invariant coordinate selection	9
Block Bregman Majorization Minimization with Extrapolation	8
Nonlinear Weighted Directed Acyclic Graph and A Priori Estimates for Neural Networks	8
Causal Structural Learning via Local Graphs	7
Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling	7
Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks	7
Equivariant Neural Networks for Indirect Measurements	7
Energy-Based Sequential Sampling for Low-Rank PSD-Matrix Approximation	7
Adversarial Robustness of Sparse Local Lipschitz Predictors	7
Taming Neural Networks with TUSLA: Nonconvex Learning via Adaptive Stochastic Gradient Langevin Algorithms	6
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization	6
Optimal Dorfman Group Testing for Symmetric Distributions	6
Balancing Geometry and Density: Path Distances on High-Dimensional Data	6

Approximation of Lipschitz Functions Using Deep Spline Neural Networks	5
Spectral Triadic Decompositions of Real-World Networks	5
Fredholm Integral Equations for Function Approximation and the Training of Neural Networks	5
Network Online Change Point Localization	5
Fast Cluster Detection in Networks by First Order Optimization	4
Approximation Bounds for Sparse Programs	4
Structural Balance and Random Walks on Complex Networks with Complex Weights	4
Lipschitz-Regularized Gradient Flows and Generative Particle Algorithms for High-Dimensional Scarce Data	4
Local Versions of Sum-of-Norms Clustering	4
Quantitative Approximation Results for Complex-Valued Neural Networks	4
Randomly Initialized Alternating Least Squares: Fast Convergence for Matrix Sensing	4
Measuring Complexity of Learning Schemes Using Hessian-Schatten Total Variation	4
Poisson Reweighted Laplacian Uncertainty Sampling for Graph-Based Active Learning	4
Optimization on Manifolds via Graph Gaussian Processes	3
Learning Functions Varying along a Central Subspace	3
New Equivalences between Interpolation and SVMs: Kernels and Structured Features	3
Markov Kernels Local Aggregation for Noise Vanishing Distribution Sampling	3
Stability of Deep Neural Networks via Discrete Rough Paths	3
Efficient Identification of Butterfly Sparse Matrix Factorizations	3
Intrinsic Dimension Adaptive Partitioning for Kernel Methods	3
A Diffusion Process Perspective on Posterior Contraction Rates for Parameters	3
ABBA Neural Networks: Coping with Positivity, Expressivity, and Robustness	2
Computing Wasserstein Barycenters via Operator Splitting: The Method of Averaged Marginals	2
Two Steps at a Time---Taking GAN Training in Stride with Tseng's Method	2
Memory Capacity of Two Layer Neural Networks with Smooth Activations	2
Approximate Q Learning for Controlled Diffusion Processes and Its Near Optimality	2
On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions	2
Rigorous Dynamical Mean-Field Theory for Stochastic Gradient Descent Methods	2
Algorithmic Regularization in Model-Free Overparametrized Asymmetric Matrix Factorization	2
A Note on the Regularity of Images Generated by Convolutional Neural Networks	2
Safe Rules for the Identification of Zeros in the Solutions of the SLOPE Problem	2
Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning	2
GNMR: A Provable One-Line Algorithm for Low Rank Matrix Recovery	2
Post-training Quantization for Neural Networks with Provable Guarantees	2
Approximate Message Passing with Rigorous Guarantees for Pooled Data and Quantitative Group Testing	2
LASSO Reloaded: A Variational Analysis Perspective with Applications to Compressed Sensing	2
Nonparametric Finite Mixture Models with Possible Shape Constraints: A Cubic Newton Approach	2
Binary Classification of Gaussian Mixtures: Abundance of Support Vectors, Benign Overfitting, and Regularization	2
Federated Primal Dual Fixed Point Algorithm	2
Target Network and Truncation Overcome the Deadly Triad in $\boldsymbol{Q}$-Learning	2
Nonbacktracking Spectral Clustering of Nonuniform Hypergraphs	1
Positive Semi-definite Embedding for Dimensionality Reduction and Out-of-Sample Extensions	1
$k$-Variance: A Clustered Notion of Variance	1
Core-Periphery Detection in Hypergraphs	1
Time-Inhomogeneous Diffusion Geometry and Topology	1
An Improved Central Limit Theorem and Fast Convergence Rates for Entropic Transportation Costs	1
Robust Classification Under $\ell_0$ Attack for the Gaussian Mixture Model	1
Sequential Construction and Dimension Reduction of Gaussian Processes Under Inequality Constraints	1
Nonasymptotic Bounds for Adversarial Excess Risk under Misspecified Models	1
High-Dimensional Analysis of Double Descent for Linear Regression with Random Projections	1

Operator Shifting for General Noisy Matrix Systems	1
Three-Operator Splitting for Learning to Predict Equilibria in Convex Games	1
Autodifferentiable Ensemble Kalman Filters	1
Wasserstein Barycenters Are NP-Hard to Compute	1
Randomized Wasserstein Barycenter Computation: Resampling with Statistical Guarantees	1
A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictors	1
Subgradient Langevin Methods for Sampling from Nonsmooth Potentials	1
Efficiency of ETA Prediction	1
Spectral Discovery of Jointly Smooth Features for Multimodal Data	1
Efficient Global Optimization of Two-Layer ReLU Networks: Quadratic-Time Algorithms and Adversarial Training	1
Corrigendum: Post-training Quantization for Neural Networks with Provable Guarantees	1
What Kinds of Functions Do Deep Neural Networks Learn? Insights from Variational Spline Theory	1
Joint Community Detection and Rotational Synchronization via Semidefinite Programming	1