Papers
1,821 papers found
Deep Network Approximation: Achieving Arbitrary Accuracy with Fixed Number of Neurons
Shijun Zhang, Zuowei Shen, Haizhao Yang
Scalable Gaussian-process regression and variable selection using Vecchia approximations
Jian Cao, Joseph Guinness, Marc G. Genton et al.
Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search
Benjamin Moseley, Joshua R. Wang
Sparse PCA: a Geometric Approach
Dimitris Bertsimas, Driss Lahlou Kitane
Density estimation on low-dimensional manifolds: an inflation-deflation approach
Christian Horvat, Jean-Pascal Pfister
HiGrad: Uncertainty Quantification for Online Learning and Stochastic Approximation
Weijie J. Su, Yuancheng Zhu
Optimal Convergence Rates for Distributed Nystroem Approximation
Jian Li, Yong Liu, Weiping Wang
The Proximal ID Algorithm
Ilya Shpitser, Zach Wood-Doughty, Eric J. Tchetgen Tchetgen
Elastic Gradient Descent, an Iterative Optimization Method Approximating the Solution Paths of the Elastic Net
Oskar Allerbo, Johan Jonasson, Rebecka Jörnsten
Universal Approximation Property of Invertible Neural Networks
Isao Ishikawa, Takeshi Teshima, Koichi Tojo et al.
Learning Optimal Feedback Operators and their Sparse Polynomial Approximations
Karl Kunisch, Donato Vásquez-Varas, Daniel Walter
Radial Basis Approximation of Tensor Fields on Manifolds: From Operator Estimation to Manifold Learning
John Harlim, Shixiao Willing Jiang, John Wilson Peoples
Limitations on approximation by deep and shallow neural networks
Guergana Petrova, Przemyslaw Wojtaszczyk
On Unbalanced Optimal Transport: Gradient Methods, Sparsity and Approximation Error
Quang Minh Nguyen, Hoang H. Nguyen, Yi Zhou et al.
A Comparison of Continuous-Time Approximations to Stochastic Gradient Descent
Stefan Ankirchner, Stefan Perko
Deep Network Approximation: Beyond ReLU to Diverse Activation Functions
Shijun Zhang, Jianfeng Lu, Hongkai Zhao
Stochastic Approximation with Decision-Dependent Distributions: Asymptotic Normality and Optimality
Joshua Cutler, Mateo Díaz, Dmitriy Drusvyatskiy
The Non-Overlapping Statistical Approximation to Overlapping Group Lasso
Mingyu Qi, Tianxi Li
Fermat Distances: Metric Approximation, Spectral Convergence, and Clustering Algorithms
Nicolás García Trillos, Anna Little, Daniel McKenzie et al.
Mean-Field Approximation of Cooperative Constrained Multi-Agent Reinforcement Learning (CMARL)
Washim Uddin Mondal, Vaneet Aggarwal, Satish V. Ukkusuri
Wasserstein Proximal Coordinate Gradient Algorithms
Rentian Yao, Xiaohui Chen, Yun Yang
Deep Neural Network Approximation of Invariant Functions through Dynamical Systems
Qianxiao Li, Ting Lin, Zuowei Shen
Zeroth-order Stochastic Approximation Algorithms for DR-submodular Optimization
Yuefang Lian, Xiao Wang, Dachuan Xu et al.
A Random Matrix Approach to Low-Multilinear-Rank Tensor Approximation
Hugo Lebeau, Florent Chatelain, Romain Couillet