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 2021-08-01 to 2025-08-01.)

Article	Citations
Spectral Barron Space for Deep Neural Network Approximation	35
A Simple and Optimal Algorithm for Strict Circular Seriation	26
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization	25
Taming Neural Networks with TUSLA: Nonconvex Learning via Adaptive Stochastic Gradient Langevin Algorithms	23
On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions	21
Learning Functions Varying along a Central Subspace	19
Resolving the Mixing Time of the Langevin Algorithm to Its Stationary Distribution for Log-Concave Sampling	19
Efficient Algorithms for Regularized Nonnegative Scale-Invariant Low-Rank Approximation Models	19
A Note on the Regularity of Images Generated by Convolutional Neural Networks	15
Randomized Nyström Approximation of Non-negative Self-Adjoint Operators	15
New Equivalences between Interpolation and SVMs: Kernels and Structured Features	14
Poisson Reweighted Laplacian Uncertainty Sampling for Graph-Based Active Learning	13
Quantitative Approximation Results for Complex-Valued Neural Networks	11
Nonbacktracking Spectral Clustering of Nonuniform Hypergraphs	11
Safe Rules for the Identification of Zeros in the Solutions of the SLOPE Problem	11
Persistent Laplacians: Properties, Algorithms and Implications	11
Nonlinear Tomographic Reconstruction via Nonsmooth Optimization	11
CA-PCA: Manifold Dimension Estimation, Adapted for Curvature	10
Convergence of a Piggyback-Style Method for the Differentiation of Solutions of Standard Saddle-Point Problems	10
Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning	10
Scalable Tensor Methods for Nonuniform Hypergraphs	10
Online Machine Teaching under Learner Uncertainty: Gradient Descent Learners of a Quadratic Loss	10
Asymptotics of the Sketched Pseudoinverse	9
The Sample Complexity of Sparse Multireference Alignment and Single-Particle Cryo-Electron Microscopy	8
Function-Space Optimality of Neural Architectures with Multivariate Nonlinearities	8

The GenCol Algorithm for High-Dimensional Optimal Transport: General Formulation and Application to Barycenters and Wasserstein Splines	8
Stochastic Variance-Reduced Majorization-Minimization Algorithms	8
Supervised Gromov–Wasserstein Optimal Transport with Metric-Preserving Constraints	7
Optimal Dorfman Group Testing for Symmetric Distributions	7
Group-Invariant Tensor Train Networks for Supervised Learning	7
Inverse Evolution Layers: Physics-Informed Regularizers for Image Segmentation	7
A Nonlinear Matrix Decomposition for Mining the Zeros of Sparse Data	7
A Variational Formulation of Accelerated Optimization on Riemannian Manifolds	7
Finite-Time Analysis of Natural Actor-Critic for POMDPs	7
Numerical Considerations and a new implementation for invariant coordinate selection	6
Computing Wasserstein Barycenters via Operator Splitting: The Method of Averaged Marginals	6
Benefit of Interpolation in Nearest Neighbor Algorithms	6
ABBA Neural Networks: Coping with Positivity, Expressivity, and Robustness	6
The Geometric Median and Applications to Robust Mean Estimation	6
Convergence of Gradient Descent for Recurrent Neural Networks: A Nonasymptotic Analysis	6
Efficient Identification of Butterfly Sparse Matrix Factorizations	6
Bi-Invariant Dissimilarity Measures for Sample Distributions in Lie Groups	6
Adaptive Joint Distribution Learning	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
Post-training Quantization for Neural Networks with Provable Guarantees	5
Randomized Wasserstein Barycenter Computation: Resampling with Statistical Guarantees	5
Operator Shifting for General Noisy Matrix Systems	5
Memory Capacity of Two Layer Neural Networks with Smooth Activations	5
LASSO Reloaded: A Variational Analysis Perspective with Applications to Compressed Sensing	5
KL Convergence Guarantees for Score Diffusion Models under Minimal Data Assumptions	5