OOIR: Observatory of International Research

Papers

(The TQCC of SIAM Journal on Mathematics of Data Science is 5. 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 2021-12-01 to 2025-12-01.)

Article	Citations
Spectral Barron Space for Deep Neural Network Approximation	40
A Simple and Optimal Algorithm for Strict Circular Seriation	31
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization	29
Taming Neural Networks with TUSLA: Nonconvex Learning via Adaptive Stochastic Gradient Langevin Algorithms	28
A Note on the Regularity of Images Generated by Convolutional Neural Networks	23
Randomized Nyström Approximation of Non-negative Self-Adjoint Operators	23
On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions	23
New Equivalences between Interpolation and SVMs: Kernels and Structured Features	22
Block Majorization Minimization with Extrapolation and Application to ${\beta }$-NMF	17
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
Learning Functions Varying along a Central Subspace	14
Efficient Algorithms for Regularized Nonnegative Scale-Invariant Low-Rank Approximation Models	13
Online Machine Teaching under Learner Uncertainty: Gradient Descent Learners of a Quadratic Loss	13
Quantitative Approximation Results for Complex-Valued Neural Networks	13
Deep Block Proximal Linearized Minimization Algorithm for Nonconvex Inverse Problems	13
Nonlinear Tomographic Reconstruction via Nonsmooth Optimization	12
Nonbacktracking Spectral Clustering of Nonuniform Hypergraphs	11
Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning	11
Safe Rules for the Identification of Zeros in the Solutions of the SLOPE Problem	11
Persistent Laplacians: Properties, Algorithms and Implications	11
CA-PCA: Manifold Dimension Estimation, Adapted for Curvature	10
Nonlinear Meta-learning Can Guarantee Faster Rates	10
Convergence of a Piggyback-Style Method for the Differentiation of Solutions of Standard Saddle-Point Problems	9
Asymptotics of the Sketched Pseudoinverse	9

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