Jonathan W. Siegel

According to our database, Jonathan W. Siegel authored at least 35 papers between 2018 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Embedding Dimension Lower Bounds for Universality of Deep Sets and Janossy Pooling.

Ali Syed, Aditya Nambiar, Jonathan W. Siegel

CoRR, May, 2026

Quantitative Approximation Rates for Group Equivariant Learning.

Jonathan W. Siegel, Snir Hordan, Hannah Lawrence, Ali Syed, Nadav Dym

CoRR, February, 2026

Approximation Rates for Shallow ReLU\(^k\) Neural Networks on Sobolev Spaces via the Radon Transform.

Tong Mao, Jonathan W. Siegel, Jinchao Xu

SIAM J. Math. Anal., 2026

2025

In-Context Multi-Operator Learning with DeepOSets.

Shao-Ting Chiu, Aditya Nambiar, Ali Syed, Jonathan W. Siegel, Ulisses Braga-Neto

CoRR, December, 2025

Nearly optimal bounds on the Fourier sampling numbers of Besov spaces.

Jonathan W. Siegel

CoRR, August, 2025

Sharp Convergence Rates for Matching Pursuit.

Jason M. Klusowski, Jonathan W. Siegel

IEEE Trans. Inf. Theory, July, 2025

Optimal Recovery Meets Minimax Estimation.

Ronald A. DeVore, Robert D. Nowak, Rahul Parhi, Guergana Petrova, Jonathan W. Siegel

CoRR, February, 2025

On the expressiveness and spectral bias of KANs.

Yixuan Wang, Jonathan W. Siegel, Ziming Liu, Thomas Y. Hou

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Sharp Bounds on the Approximation Rates, Metric Entropy, and n-Widths of Shallow Neural Networks.

Jonathan W. Siegel, Jinchao Xu

Found. Comput. Math., April, 2024

Sharp lower bounds on the manifold widths of Sobolev and Besov spaces.

Jonathan W. Siegel

J. Complex., 2024

Approximation Rates for Shallow ReLU^k Neural Networks on Sobolev Spaces via the Radon Transform.

Tong Mao, Jonathan W. Siegel, Jinchao Xu

CoRR, 2024

Convergence and error control of consistent PINNs for elliptic PDEs.

Andrea Bonito, Ronald A. DeVore, Guergana Petrova, Jonathan W. Siegel

CoRR, 2024

Nesterov acceleration despite very noisy gradients.

Kanan Gupta, Jonathan W. Siegel, Stephan Wojtowytsch

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Equivariant Frames and the Impossibility of Continuous Canonicalization.

Nadav Dym, Hannah Lawrence, Jonathan W. Siegel

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Greedy training algorithms for neural networks and applications to PDEs.

Jonathan W. Siegel, Qingguo Hong, Xianlin Jin, Wenrui Hao, Jinchao Xu

J. Comput. Phys., July, 2023

Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev and Besov Spaces.

Jonathan W. Siegel

J. Mach. Learn. Res., 2023

A qualitative difference between gradient flows of convex functions in finite- and infinite-dimensional Hilbert spaces.

Jonathan W. Siegel, Stephan Wojtowytsch

CoRR, 2023

Weighted variation spaces and approximation by shallow ReLU networks.

Ronald A. DeVore, Robert D. Nowak, Rahul Parhi, Jonathan W. Siegel

CoRR, 2023

Optimal Approximation of Zonoids and Uniform Approximation by Shallow Neural Networks.

Jonathan W. Siegel

CoRR, 2023

Entropy-based convergence rates of greedy algorithms.

Yuwen Li, Jonathan W. Siegel

CoRR, 2023

Achieving acceleration despite very noisy gradients.

Kanan Gupta, Jonathan W. Siegel, Stephan Wojtowytsch

CoRR, 2023

Sharp Lower Bounds on Interpolation by Deep ReLU Neural Networks at Irregularly Spaced Data.

Jonathan W. Siegel

CoRR, 2023

2022

Optimal Convergence Rates for the Orthogonal Greedy Algorithm.

Jonathan W. Siegel, Jinchao Xu

IEEE Trans. Inf. Theory, 2022

Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev Spaces.

Jonathan W. Siegel

CoRR, 2022

On the Activation Function Dependence of the Spectral Bias of Neural Networks.

Qingguo Hong, Qinyang Tan, Jonathan W. Siegel, Jinchao Xu

CoRR, 2022

2021

An efficient greedy training algorithm for neural networks and applications in PDEs.

Wenrui Hao, Xianlin Jin, Jonathan W. Siegel, Jinchao Xu

CoRR, 2021

Characterization of the Variation Spaces Corresponding to Shallow Neural Networks.

Jonathan W. Siegel, Jinchao Xu

CoRR, 2021

Improved Convergence Rates for the Orthogonal Greedy Algorithm.

Jonathan W. Siegel, Jinchao Xu

CoRR, 2021

Optimal Approximation Rates and Metric Entropy of ReLU$^k$ and Cosine Networks.

Jonathan W. Siegel, Jinchao Xu

CoRR, 2021

2020

Approximation rates for neural networks with general activation functions.

Jonathan W. Siegel, Jinchao Xu

Neural Networks, 2020

Accuracy, Efficiency and Optimization of Signal Fragmentation.

Russel Caflisch, Hung-Hsu Chou, Jonathan W. Siegel

Multiscale Model. Simul., 2020

High-Order Approximation Rates for Neural Networks with ReLU^k Activation Functions.

Jonathan W. Siegel, Jinchao Xu

CoRR, 2020

Training Sparse Neural Networks using Compressed Sensing.

Jonathan W. Siegel, Jianhong Chen, Jinchao Xu

CoRR, 2020

2019

On the Approximation Properties of Neural Networks.

Jonathan W. Siegel, Jinchao Xu

CoRR, 2019

2018

Accelerated First-Order Optimization with Orthogonality Constraints

Jonathan W. Siegel

PhD thesis, 2018