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ORCIDSIAM Journal on Mathematics of Data Science, Volume 7
Volume 7, Number 1, 2025
- Gage DeZoort
, Boris Hanin:
Principles for Initialization and Architecture Selection in Graph Neural Networks with ReLU Activations. 1-27 - Damir Filipovic, Michael D. Multerer, Paul Schneider:
Adaptive Joint Distribution Learning. 28-54 - Chaoyu Liu, Zhonghua Qiao, Chao Li, Carola-Bibiane Schönlieb:
Inverse Evolution Layers: Physics-Informed Regularizers for Image Segmentation. 55-85 - Giovanni Conforti, Alain Durmus, Marta Gentiloni Silveri:
KL Convergence Guarantees for Score Diffusion Models under Minimal Data Assumptions. 86-109 - Rahul Parhi, Michael Unser:
Function-Space Optimality of Neural Architectures with Multivariate Nonlinearities. 110-135 - Arvind K. Saibaba, Agnieszka Miedlar:
Randomized Low-Rank Approximations beyond Gaussian Random Matrices. 136-162 - Haoyue Wang, Shibal Ibrahim, Rahul Mazumder:
Nonparametric Finite Mixture Models with Possible Shape Constraints: A Cubic Newton Approach. 163-188 - Aimee Maurais, Terrence Alsup, Benjamin Peherstorfer, Youssef M. Marzouk:
Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices. 189-223 - Julia Lindberg, Carlos Améndola, Jose Israel Rodriguez:
Estimating Gaussian Mixtures Using Sparse Polynomial Moment Systems. 224-252 - Jiamin Liu, Junzhuo Gao, Heng Lian:
Kernel-Based Regularized Learning with Random Projections: Beyond Least Squares. 253-273 - Nicolas Lanzetti, Saverio Bolognani, Florian Dörfler:
First-Order Conditions for Optimization in the Wasserstein Space. 274-300 - Zixuan Cang, Yaqi Wu, Yanxiang Zhao:
Supervised Gromov-Wasserstein Optimal Transport with Metric-Preserving Constraints. 301-328 - Csaba Tóth, Harald Oberhauser, Zoltán Szabó:
Random Fourier Signature Features. 329-354 - Anna C. Gilbert, Kevin O'Neill:
CA-PCA: Manifold Dimension Estimation, Adapted for Curvature. 355-383
Volume 7, Number 2, 2025
- Gianluca Fabiani:
Random Projection Neural Networks of Best Approximation: Convergence Theory and Practical Applications. 385-409 - Andrew Lee, Harlin Lee, Jose A. Perea, Nikolas Schonsheck, Madeleine Weinstein:
\({O({k})}\)-Equivariant Dimensionality Reduction on Stiefel Manifolds. 410-437 - Mingqi Wu, Qiang Sun:
Ensemble Linear Interpolators: The Role of Ensembling. 438-467 - Jeremy E. Cohen, Valentin Leplat:
Efficient Algorithms for Regularized Nonnegative Scale-Invariant Low-Rank Approximation Models. 468-494
Volume 7, Number 3, 2025
- Geyu Liang, Gavin Zhang, Salar Fattahi, Richard Y. Zhang:
Simple Alternating Minimization Provably Solves Complete Dictionary Learning. 855-883 - Belen Martin-Urcelay, Christopher J. Rozell, Matthieu R. Bloch:
Online Machine Teaching under Learner Uncertainty: Gradient Descent Learners of a Quadratic Loss. 884-905 - Mohammad Sadegh Salehi, Subhadip Mukherjee, Lindon Roberts, Matthias J. Ehrhardt:
An Adaptively Inexact First-Order Method for Bilevel Optimization with Application to Hyperparameter Learning. 906-936 - Biraj Pandey, Bamdad Hosseini, Pau Batlle, Houman Owhadi:
Diffeomorphic Measure Matching with Kernels for Generative Modeling. 937-964 - Konstantinos E. Nikolakakis, Amin Karbasi, Dionysis Kalogerias:
Select without Fear: Almost All Minibatch Schedules Generalize Optimally. 965-992 - Jason M. Altschuler, Kunal Talwar:
Resolving the Mixing Time of the Langevin Algorithm to Its Stationary Distribution for Log-Concave Sampling. 993-1020 - Suzanna Parkinson, Greg Ongie, Rebecca Willett:
ReLU Neural Networks with Linear Layers Are Biased towards Single- and Multi-index Models. 1021-1052 - Yulei Liao, Pingbing Ming:
Spectral Barron Space for Deep Neural Network Approximation. 1053-1076 - Ery Arias-Castro, Siddharth Vishwanath:
Stability of Sequential Lateration and of Stress Minimization in the Presence of Noise. 1077-1097 - Stanislav Budzinskiy:
When Big Data Actually Are Low-Rank, or Entrywise Approximation of Certain Function-Generated Matrices. 1098-1122
Volume 7, Number 4, 2025
- Andraz Jelincic, Jiajie Tao, William F. Turner, Thomas Cass, James M. Foster, Hao Ni:
Generative Modeling of Lévy Area for High Order SDE Simulation. 1541-1567 - Rajdeep Haldar, Qifan Song:
On Neural Network Approximation of Ideal Adversarial Attack and Convergence of Adversarial Training. 1568-1593 - Dimitri Meunier, Zhu Li, Arthur Gretton, Samory Kpotufe:
Nonlinear Meta-learning Can Guarantee Faster Rates. 1594-1615 - Sebastian Hofmann, Alfio Borzì:
The Pontryagin Maximum Principle for Training Convolutional Neural Networks. 1616-1642 - Vitaliy A. Kurlin:
Complete and Continuous Invariants of 1-Periodic Sequences in Polynomial Time. 1643-1663 - Bernard Bercu, Jérémie Bigot, Gauthier Thurin:
Stochastic Optimal Transport in Banach Spaces for Regularized Estimation of Multivariate Quantiles. 1664-1689 - Erdong Guo, David Draper:
Representation Theorems for Matrix Product States. 1690-1704 - Tamir Bendory, Nadav Dym, Dan Edidin, Arun Suresh:
Phase Retrieval with Semialgebraic and ReLU Neural Network Priors. 1705-1728 - Chaoyan Huang, Zhongming Wu, Yanqi Cheng, Tieyong Zeng, Carola-Bibiane Schönlieb, Angelica I. Avilés-Rivero:
Deep Block Proximal Linearized Minimization Algorithm for Nonconvex Inverse Problems. 1729-1754 - Yunfei Yang, Han Feng, Ding-Xuan Zhou:
On the Rates of Convergence for Learning with Convolutional Neural Networks. 1755-1772 - Rishi Sonthalia, Anna Seigal, Guido Montúfar:
Supermodular Rank: Set Function Decomposition and Optimization. 1773-1800
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