New Data Science Papers: SIAM Journal on Mathematics of Data Science, Vol. 2

Society for Industrial and Applied Mathematics (SIAM) recently launched SIAM Journal on Mathematics of Data Science (SIMODS), which began publishing work in 2019. Read a SIMODS top article and then consider submitting a paper of your own.

SIAM Journal on Mathematics of Data Science

SIMODS publishes work that advances mathematical, statistical, and computational methods in the context of data and information sciences. 

Click below to read the most frequently downloaded articles from SIMODS newest volume.

• Layer-Parallel Training of Deep Residual Neural Networks (Stefanie Günther, Lars Ruthotto, Jacob B. Schroder, Eric C. Cyr, and Nicolas R. Gauger)
• On Gradient-Based Learning in Continuous Games (Eric Mazumdar , Lillian J. Ratliff, and S. Shankar Sastry)
• Randomized Algorithms for Low-Rank Tensor Decompositions in the Tucker Format (Rachel Minster, Arvind K. Saibaba, and Misha E. Kilmer)
• A Bayesian Framework for Persistent Homology (Vasileios Maroulas, Farzana Nasrin, and Christopher Oballe)
• Fast Convex Pruning of Deep Neural Networks (Alireza Aghasi, Afshin Abdi, and Justin Romberg)
• Phase Retrieval with Sparse Phase Constraint (Nguyen Hieu Thao , David Russell Luke, Oleg Soloviev, and Michel Verhaegen)
• Detecting Overlapping Communities in Networks Using Spectral Methods (Yuan Zhang, Elizaveta Levina, and Ji Zhu
• Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Nonconvex Optimization (Mahesh Chandra Mukkamala, Peter Ochs, Thomas Pock, and Shoham Sabach)
• Graph Powering and Spectral Robustness (Emmanuel Abbe, Enric Boix-Adserà, Peter Ralli, and Colin Sandon)
• EnResNet: ResNets Ensemble via the Feynman--Kac Formalism for Adversarial Defense and Beyond (Bao Wang, Binjie Yuan, Zuoqiang Shi, and Stanley J. Osher)
• ISLET: Fast and Optimal Low-Rank Tensor Regression via Importance Sketching (Anru R. Zhang , Yuetian Luo, Garvesh Raskutti, and Ming Yuan)
• Compressive Sensing for Cut Improvement and Local Clustering (Ming-Jun Lai  and Daniel Mckenzie)
• Blind Identification of Stochastic Block Models from Dynamical Observations (Michael T. Schaub , Santiago Segarra, and John N. Tsitsiklis)
• Accelerated Gossip in Networks of Given Dimension Using Jacobi Polynomial Iterations (Raphaël Berthier, Francis Bach, and Pierre Gaillard)
• SCOTT: Shape-Location Combined Tracking with Optimal Transport (Xinye Zheng, Jianbo Ye, James Z. Wang , and Jia Li)
• Matching Component Analysis for Transfer Learning (Charles Clum, Dustin G. Mixon, and Theresa Scarnati)
• Geometry and Symmetry in Short-and-Sparse Deconvolution (Han-Wen Kuo, Yuqian Zhang, Yenson Lau, and John Wright)
• Analysis of the Generalization Error: Empirical Risk Minimization over Deep Artificial Neural Networks Overcomes the Curse of Dimensionality in the Numerical Approximation of Black--Scholes Partial Differential Equations (Julius Berner, Philipp Grohs, and Arnulf Jentzen)
• Multilayer Modularity Belief Propagation to Assess Detectability of Community Structure (William H. Weir , Benjamin Walker, Lenka Zdeborová, and Peter J. Mucha)
• A Rigorous Theory of Conditional Mean Embeddings (Ilja Klebanov, Ingmar Schuster, and T. J. Sullivan) 

Is your work relevant to mathematical, statistical, and computational methods in the context of data science? Submit your next manuscript to SIMODS here. Learn more about SIAM Journals.