OOIR: Observatory of International Research

Papers

(The TQCC of SIAM Journal on Mathematics of Data Science is 4. 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
Reversible Gromov–Monge Sampler for Simulation-Based Inference	11
Estimating Gaussian Mixtures Using Sparse Polynomial Moment Systems	11
Randomly Initialized Alternating Least Squares: Fast Convergence for Matrix Sensing	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	10
A Nonlinear Matrix Decomposition for Mining the Zeros of Sparse Data	9
Spectral Triadic Decompositions of Real-World Networks	9
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization	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

Balancing Geometry and Density: Path Distances on High-Dimensional Data	6
Optimal Dorfman Group Testing for Symmetric Distributions	6
Network Online Change Point Localization	6
Measuring Complexity of Learning Schemes Using Hessian-Schatten Total Variation	6
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
Supervised Gromov–Wasserstein Optimal Transport with Metric-Preserving Constraints	5
Adversarial Robustness of Sparse Local Lipschitz Predictors	5
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
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