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 2021-11-01 to 2025-11-01.)

Article	Citations
Taming Neural Networks with TUSLA: Nonconvex Learning via Adaptive Stochastic Gradient Langevin Algorithms	36
A Simple and Optimal Algorithm for Strict Circular Seriation	29
Spectral Barron Space for Deep Neural Network Approximation	29
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization	26
On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions	23
Randomized Nyström Approximation of Non-negative Self-Adjoint Operators	22
A Note on the Regularity of Images Generated by Convolutional Neural Networks	22
New Equivalences between Interpolation and SVMs: Kernels and Structured Features	20
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
Block Majorization Minimization with Extrapolation and Application to ${\beta }$-NMF	16
Learning Functions Varying along a Central Subspace	14
Efficient Algorithms for Regularized Nonnegative Scale-Invariant Low-Rank Approximation Models	13
Deep Block Proximal Linearized Minimization Algorithm for Nonconvex Inverse Problems	13
Quantitative Approximation Results for Complex-Valued Neural Networks	13
Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning	12
Nonbacktracking Spectral Clustering of Nonuniform Hypergraphs	11
Online Machine Teaching under Learner Uncertainty: Gradient Descent Learners of a Quadratic Loss	11
Persistent Laplacians: Properties, Algorithms and Implications	11
Nonlinear Tomographic Reconstruction via Nonsmooth Optimization	11
CA-PCA: Manifold Dimension Estimation, Adapted for Curvature	10
Safe Rules for the Identification of Zeros in the Solutions of the SLOPE Problem	10
Nonlinear Meta-learning Can Guarantee Faster Rates	9
A Variational Formulation of Accelerated Optimization on Riemannian Manifolds	9
Function-Space Optimality of Neural Architectures with Multivariate Nonlinearities	9

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