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ORCIDSIAM Journal on Mathematics of Data Science, Volume 5
Volume 5, Number 1, March 2023
- Francesco Tudisco
, Desmond J. Higham:
Core-Periphery Detection in Hypergraphs. 1-21 - Léon Zheng, Elisa Riccietti, Rémi Gribonval:
Efficient Identification of Butterfly Sparse Matrix Factorizations. 22-49 - Christian Bayer, Peter K. Friz, Nikolas Tapia:
Stability of Deep Neural Networks via Discrete Rough Paths. 50-76 - Adam Li, Ronan Perry, Chester Huynh, Tyler M. Tomita, Ronak Mehta, Jesús Arroyo, Jesse Patsolic, Benjamin Falk, Sridevi V. Sarma, Joshua T. Vogelstein:
Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks. 77-96 - Aurore Archimbaud, Zlatko Drmac, Klaus Nordhausen, Una Radojicic, Anne Ruiz-Gazen:
Numerical Considerations and a new implementation for invariant coordinate selection. 97-121 - Farzan Farnia, William W. Wang, Subhro Das, Ali Jadbabaie:
GAT-GMM: Generative Adversarial Training for Gaussian Mixture Models. 122-146 - Clément Elvira, Cédric Herzet:
Safe Rules for the Identification of Zeros in the Solutions of the SLOPE Problem. 147-173 - Tianjiao Li, Guanghui Lan, Ashwin Pananjady:
Accelerated and Instance-Optimal Policy Evaluation with Linear Function Approximation. 174-200 - Mikhael Carmona, Victor Chepoi, Guyslain Naves, Pascal Préa:
A Simple and Optimal Algorithm for Strict Circular Seriation. 201-221 - David Hong, Fan Yang, Jeffrey A. Fessler, Laura Balzano:
Optimally Weighted PCA for High-Dimensional Heteroscedastic Data. 222-250
Volume 5, Number 2, June 2023
- Philip S. Chodrow, Nicole Eikmeier, Jamie Haddock:
Nonbacktracking Spectral Clustering of Nonuniform Hypergraphs. 251-279 - Wenyu Chen, Mathias Drton, Ali Shojaie:
Causal Structural Learning via Local Graphs. 280-305 - Sebastian Neumayer, Alexis Goujon, Pakshal Bohra, Michael Unser:
Approximation of Lipschitz Functions Using Deep Spline Neural Networks. 306-322 - Attila Lovas, Iosif Lytras, Miklós Rásonyi, Sotirios Sabanis:
Taming Neural Networks with TUSLA: Nonconvex Learning via Adaptive Stochastic Gradient Langevin Algorithms. 323-345 - Guillaume Huguet, Alexander Tong, Bastian Rieck, Jessie Huang, Manik Kuchroo, Matthew J. Hirn, Guy Wolf, Smita Krishnaswamy:
Time-Inhomogeneous Diffusion Geometry and Topology. 346-372 - Jinjie Zhang, Yixuan Zhou, Rayan Saab:
Post-training Quantization for Neural Networks with Provable Guarantees. 373-399 - Ery Arias-Castro, Wanli Qiao:
Moving Up the Cluster Tree with the Gradient Flow. 400-421 - Shayan Aziznejad, Joaquim Campos, Michael Unser:
Measuring Complexity of Learning Schemes Using Hessian-Schatten Total Variation. 422-445 - Yatong Bai, Tanmay Gautam, Somayeh Sojoudi:
Efficient Global Optimization of Two-Layer ReLU Networks: Quadratic-Time Algorithms and Adversarial Training. 446-474 - Keaton Hamm, Nick Henscheid, Shujie Kang:
Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning. 475-501 - Filipa Valdeira, Ricardo Ferreira, Alessandra Micheletti, Cláudia Soares:
Probabilistic Registration for Gaussian Process Three-Dimensional Shape Modelling in the Presence of Extensive Missing Data. 502-527 - Gilles Mordant, Axel Munk:
Statistical Analysis of Random Objects Via Metric Measure Laplacians. 528-557 - Hong Ye Tan, Subhadip Mukherjee, Junqi Tang, Carola-Bibiane Schönlieb:
Data-Driven Mirror Descent with Input-Convex Neural Networks. 558-587
Volume 5, Number 3, September 2023
- Boris Landa, Xiuyuan Cheng:
Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling. 589-614 - Erhan Bayraktar, Ali Devran Kara:
Approximate Q Learning for Controlled Diffusion Processes and Its Near Optimality. 615-638 - Eustasio del Barrio, Alberto González-Sanz, Jean-Michel Loubes, Jonathan Niles-Weed:
An Improved Central Limit Theorem and Fast Convergence Rates for Entropic Transportation Costs. 639-669 - Andreas Habring, Martin Holler:
A Note on the Regularity of Images Generated by Convolutional Neural Networks. 670-692 - Jie Jiang, Xiaojun Chen:
Optimality Conditions for Nonsmooth Nonconvex-Nonconcave Min-Max Problems and Generative Adversarial Networks. 693-722 - Liwei Jiang, Yudong Chen, Lijun Ding:
Algorithmic Regularization in Model-Free Overparametrized Asymmetric Matrix Factorization. 723-744 - Bora Yongacoglu, Gürdal Arslan, Serdar Yüksel:
Satisficing Paths and Independent Multiagent Reinforcement Learning in Stochastic Games. 745-773 - Kiryung Lee, Dominik Stöger:
Randomly Initialized Alternating Least Squares: Fast Convergence for Matrix Sensing. 774-799 - Henri Riihimäki:
Simplicial \({\boldsymbol{q}}\) -Connectivity of Directed Graphs with Applications to Network Analysis. 800-828
Volume 5, Number 4, December 2023
- Brent Sprangers, Nick Vannieuwenhoven:
Group-Invariant Tensor Train Networks for Supervised Learning. 829-853 - Daniel Beaglehole, Mikhail Belkin, Parthe Pandit:
On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions. 854-872 - Michael Perlmutter, Alexander Tong, Feng Gao, Guy Wolf, Matthew J. Hirn:
Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms. 873-898 - Gero Friesecke, Maximilian Penka:
The GenCol Algorithm for High-Dimensional Optimal Transport: General Formulation and Application to Barycenters and Wasserstein Splines. 899-919 - Ramchandran Muthukumar, Jeremias Sulam:
Adversarial Robustness of Sparse Local Lipschitz Predictors. 920-948 - Yihang Gao, Michael K. Ng, Mingjie Zhou:
Approximating Probability Distributions by Using Wasserstein Generative Adversarial Networks. 949-976 - Nikhil Ghosh, Mikhail Belkin:
A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictors. 977-1004 - Eldad Haber, Moshe Eliasof, Luis Tenorio:
Estimating a Potential Without the Agony of the Partition Function. 1005-1027 - Nicolas Keriven:
Entropic Optimal Transport on Random Graphs. 1028-1050 - Matteo Cacciola, Antonio Frangioni, Xinlin Li, Andrea Lodi:
Deep Neural Networks Pruning via the Structured Perspective Regularization. 1051-1077 - Zaiwei Chen, John-Paul Clarke, Siva Theja Maguluri:
Target Network and Truncation Overcome the Deadly Triad in \(\boldsymbol{Q}\)-Learning. 1078-1101 - Aaron Berk, Simone Brugiapaglia, Tim Hoheisel:
LASSO Reloaded: A Variational Analysis Perspective with Applications to Compressed Sensing. 1102-1129 - Yifan Zhang, Joe Kileel:
Moment Estimation for Nonparametric Mixture Models through Implicit Tensor Decomposition. 1130-1159 - Kevin Miller, Jeff Calder:
Poisson Reweighted Laplacian Uncertainty Sampling for Graph-Based Active Learning. 1160-1190
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