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ORCIDSIAM Journal on Mathematics of Data Science, Volume 6
Volume 6, Number 1, March 2024
- Hwanwoo Kim
, Daniel Sanz-Alonso, Ruiyi Yang:
Optimization on Manifolds via Graph Gaussian Processes. 1-25 - Francis R. Bach:
High-Dimensional Analysis of Double Descent for Linear Regression with Random Projections. 26-50 - Alec Koppel, Joe Eappen, Sujay Bhatt, Cole Hawkins, Sumitra Ganesh:
Online MCMC Thinning with Kernelized Stein Discrepancy. 51-75 - Anatoli B. Juditsky, Arkadi Nemirovski:
On Design of Polyhedral Estimates in Linear Inverse Problems. 76-96 - Elisa Negrini, Levon Nurbekyan:
Applications of No-Collision Transportation Maps in Manifold Learning. 97-126 - Michal Derezinski, Elizaveta Rebrova:
Sharp Analysis of Sketch-and-Project Methods via a Connection to Randomized Singular Value Decomposition. 127-153 - Haoyu Zhao, Konstantin Burlachenko, Zhize Li, Peter Richtárik:
Faster Rates for Compressed Federated Learning with Client-Variance Reduction. 154-175 - Yi Yu, Oscar Hernan Madrid Padilla, Daren Wang, Alessandro Rinaldo:
Network Online Change Point Localization. 176-198 - Daniel LeJeune, Pratik Patil, Hamid Javadi, Richard G. Baraniuk, Ryan J. Tibshirani:
Asymptotics of the Sketched Pseudoinverse. 199-225
Volume 6, Number 2, 2024
- Chiwei Yan, James Johndrow, Dawn Woodard, Yanwei Sun:
Efficiency of ETA Prediction. 227-253 - Tamir Bendory, Dan Edidin:
The Sample Complexity of Sparse Multireference Alignment and Single-Particle Cryo-Electron Microscopy. 254-282 - YoonHaeng Hur, Wenxuan Guo, Tengyuan Liang:
Reversible Gromov-Monge Sampler for Simulation-Based Inference. 283-310 - Adrien Vacher, Boris Muzellec, Francis R. Bach, François-Xavier Vialard, Alessandro Rudi:
Optimal Estimation of Smooth Transport Maps with Kernel SoS. 311-342 - Hao Liu, Wenjing Liao:
Learning Functions Varying along a Central Subspace. 343-371 - Yu Tian, Renaud Lambiotte:
Structural Balance and Random Walks on Complex Networks with Complex Weights. 372-399 - Cédric Gerbelot, Emanuele Troiani, Francesca Mignacco, Florent Krzakala, Lenka Zdeborová:
Rigorous Dynamical Mean-Field Theory for Stochastic Gradient Descent Methods. 400-427 - Stanislav Minsker, Mohamed Ndaoud, Lang Wang:
Robust and Tuning-Free Sparse Linear Regression via Square-Root Slope. 428-453 - Yehuda Dar, Daniel LeJeune, Richard G. Baraniuk:
The Common Intuition to Transfer Learning Can Win or Lose: Case Studies for Linear Regression. 454-480 - Sinan G. Aksoy, Ilya Amburg, Stephen J. Young:
Scalable Tensor Methods for Nonuniform Hypergraphs. 481-503 - Stanislav Minsker, Nate Strawn:
The Geometric Median and Applications to Robust Mean Estimation. 504-533 - Seonho Kim, Kiryung Lee:
Max-Affine Regression via First-Order Methods. 534-552 - Wenlong Mou, Nhat Ho, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan:
A Diffusion Process Perspective on Posterior Contraction Rates for Parameters. 553-577
Volume 6, Number 3, 2024
- Matthias Beckmann, Nick Heilenkötter:
Equivariant Neural Networks for Indirect Measurements. 579-601 - Vivak Patel, Albert S. Berahas:
Gradient Descent in the Absence of Global Lipschitz Continuity of the Gradients. 602-626 - Daniel McKenzie, Howard Heaton, Qiuwei Li, Samy Wu Fung, Stanley J. Osher, Wotao Yin:
Three-Operator Splitting for Learning to Predict Equilibria in Convex Games. 627-648 - Ana Neacsu, Jean-Christophe Pesquet, Vlad Vasilescu, Corneliu Burileanu:
ABBA Neural Networks: Coping with Positivity, Expressivity, and Robustness. 649-678 - Liam Madden, Christos Thrampoulidis:
Memory Capacity of Two Layer Neural Networks with Smooth Activations. 679-702 - Sabyasachi Basu, Suman Kalyan Bera, C. Seshadhri:
Spectral Triadic Decompositions of Real-World Networks. 703-730 - Nicholas C. Landolfi, Sanjay Lall:
Optimal Dorfman Group Testing for Symmetric Distributions. 731-760 - Chiraag Kaushik, Andrew D. McRae, Mark A. Davenport, Vidya Muthukumar:
New Equivalences between Interpolation and SVMs: Kernels and Structured Features. 761-787 - Yatong Bai, Brendon G. Anderson, Aerin Kim, Somayeh Sojoudi:
Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing. 788-814 - Wojciech Reise, Ximena Fernández, Maria Dominguez, Heather A. Harrington, Mariano Beguerisse-Díaz:
Topological Fingerprints for Audio Identification. 815-841 - Jinjie Zhang, Yixuan Zhou, Rayan Saab:
Corrigendum: Post-training Quantization for Neural Networks with Provable Guarantees. 842-846 - Matthew Hutchings, Bertrand Gauthier:
Energy-Based Sequential Sampling for Low-Rank PSD-Matrix Approximation. 1055-1077
Volume 6, Number 4, 2024
- Changyu Liu, Yuling Jiao, Junhui Wang, Jian Huang:
Nonasymptotic Bounds for Adversarial Excess Risk under Misspecified Models. 847-868 - Semih Cayci, Niao He, R. Srikant:
Finite-Time Analysis of Natural Actor-Critic for POMDPs. 869-896 - Andreas Habring, Martin Holler, Thomas Pock:
Subgradient Langevin Methods for Sampling from Nonsmooth Potentials. 897-925 - Duy-Nhat Phan, Sedi Bartz, Nilabja Guha, Hung M. Phan:
Stochastic Variance-Reduced Majorization-Minimization Algorithms. 926-952 - Daniela Angela Parletta, Andrea Paudice, Massimiliano Pontil, Saverio Salzo:
High Probability Bounds for Stochastic Subgradient Schemes with Heavy Tailed Noise]. 953-977 - Yifei Wang, Peng Chen, Mert Pilanci, Wuchen Li:
Optimal Neural Network Approximation of Wasserstein Gradient Direction via Convex Optimization. 978-999 - Daniel W. Mimouni, Paul Malisani, Jiamin Zhu, Welington de Oliveira:
Computing Wasserstein Barycenters via Operator Splitting: The Method of Averaged Marginals. 1000-1026 - Nelvin Tan, Pablo Pascual Cobo, Jonathan Scarlett, Ramji Venkataramanan:
Approximate Message Passing with Rigorous Guarantees for Pooled Data and Quantitative Group Testing. 1027-1054 - Patrick Gelß, Aizhan Issagali, Ralf Kornhuber:
Fredholm Integral Equations for Function Approximation and the Training of Neural Networks. 1078-1108 - Johannes Hertrich:
Fast Kernel Summation in High Dimensions via Slicing and Fourier Transforms. 1109-1137 - Ya-Nan Zhu, Jingwei Liang, Xiaoqun Zhang:
Federated Primal Dual Fixed Point Algorithm. 1138-1172 - Zachary Frangella, Pratik Rathore, Shipu Zhao, Madeleine Udell:
SketchySGD: Reliable Stochastic Optimization via Randomized Curvature Estimates. 1173-1204 - Hyemin Gu, Panagiota Birmpa, Yannis Pantazis, Luc Rey-Bellet, Markos A. Katsoulakis:
Lipschitz-Regularized Gradient Flows and Generative Particle Algorithms for High-Dimensional Scarce Data. 1205-1235
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