Jonathan W. Siegel

38 papers A* 3Journal 34

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Approximation Rates for Shallow ReLU$^k$ Neural Networks on Sobolev Spaces via the Radon Transform. SIAM J. Math. Anal. Tong Mao, Jonathan W. Siegel, Jinchao Xu
2026	J	`jnl`	Quantitative Approximation Rates for Group Equivariant Learning. CoRR Jonathan W. Siegel, Snir Hordan, Hannah Lawrence, Ali Syed, Nadav Dym
2025	J	`jnl`	In-Context Multi-Operator Learning with DeepOSets. CoRR Shao-Ting Chiu, Aditya Nambiar, Ali Syed, Jonathan W. Siegel, Ulisses Braga-Neto
2025	J	`jnl`	Nearly optimal bounds on the Fourier sampling numbers of Besov spaces. CoRR Jonathan W. Siegel
2025	A*	`conf`	On the expressiveness and spectral bias of KANs. ICLR Yixuan Wang, Jonathan W. Siegel, Ziming Liu, Thomas Y. Hou
2025	J	`jnl`	Optimal Recovery Meets Minimax Estimation. CoRR Ronald A. DeVore, Robert D. Nowak, Rahul Parhi, Guergana Petrova, Jonathan W. Siegel
2025	J	`jnl`	Sharp Convergence Rates for Matching Pursuit. IEEE Trans. Inf. Theory Jason M. Klusowski, Jonathan W. Siegel
2024	J	`jnl`	Approximation Rates for Shallow ReLU CoRR Tong Mao, Jonathan W. Siegel, Jinchao Xu
2024	J	`jnl`	Convergence and error control of consistent PINNs for elliptic PDEs. CoRR Andrea Bonito, Ronald A. DeVore, Guergana Petrova, Jonathan W. Siegel
2024	A*	`conf`	Equivariant Frames and the Impossibility of Continuous Canonicalization. ICML Nadav Dym, Hannah Lawrence, Jonathan W. Siegel
2024	J	`jnl`	Equivariant Frames and the Impossibility of Continuous Canonicalization. CoRR Nadav Dym, Hannah Lawrence, Jonathan W. Siegel
2024	A*	`conf`	Nesterov acceleration despite very noisy gradients. NeurIPS Kanan Gupta, Jonathan W. Siegel, Stephan Wojtowytsch
2024	J	`jnl`	On the expressiveness and spectral bias of KANs. CoRR Yixuan Wang, Jonathan W. Siegel, Ziming Liu, Thomas Y. Hou
2024	J	`jnl`	Sharp Bounds on the Approximation Rates, Metric Entropy, and n-Widths of Shallow Neural Networks. Found. Comput. Math. Jonathan W. Siegel, Jinchao Xu
2024	J	`jnl`	Sharp Lower Bounds on the Manifold Widths of Sobolev and Besov Spaces. CoRR Jonathan W. Siegel
2024	J	`jnl`	Sharp lower bounds on the manifold widths of Sobolev and Besov spaces. J. Complex. Jonathan W. Siegel
2023	J	`jnl`	A qualitative difference between gradient flows of convex functions in finite- and infinite-dimensional Hilbert spaces. CoRR Jonathan W. Siegel, Stephan Wojtowytsch
2023	J	`jnl`	Achieving acceleration despite very noisy gradients. CoRR Kanan Gupta, Jonathan W. Siegel, Stephan Wojtowytsch
2023	J	`jnl`	Entropy-based convergence rates of greedy algorithms. CoRR Yuwen Li, Jonathan W. Siegel
2023	J	`jnl`	Greedy training algorithms for neural networks and applications to PDEs. J. Comput. Phys. Jonathan W. Siegel, Qingguo Hong, Xianlin Jin, Wenrui Hao, Jinchao Xu
2023	J	`jnl`	Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev and Besov Spaces. J. Mach. Learn. Res. Jonathan W. Siegel
2023	J	`jnl`	Optimal Approximation of Zonoids and Uniform Approximation by Shallow Neural Networks. CoRR Jonathan W. Siegel
2023	J	`jnl`	Sharp Convergence Rates for Matching Pursuit. CoRR Jason M. Klusowski, Jonathan W. Siegel
2023	J	`jnl`	Sharp Lower Bounds on Interpolation by Deep ReLU Neural Networks at Irregularly Spaced Data. CoRR Jonathan W. Siegel
2023	J	`jnl`	Weighted variation spaces and approximation by shallow ReLU networks. CoRR Ronald A. DeVore, Robert D. Nowak, Rahul Parhi, Jonathan W. Siegel
2022	J	`jnl`	On the Activation Function Dependence of the Spectral Bias of Neural Networks. CoRR Qingguo Hong, Qinyang Tan, Jonathan W. Siegel, Jinchao Xu
2022	J	`jnl`	Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev Spaces. CoRR Jonathan W. Siegel
2022	J	`jnl`	Optimal Convergence Rates for the Orthogonal Greedy Algorithm. IEEE Trans. Inf. Theory Jonathan W. Siegel, Jinchao Xu
2021	J	`jnl`	An efficient greedy training algorithm for neural networks and applications in PDEs. CoRR Wenrui Hao, Xianlin Jin, Jonathan W. Siegel, Jinchao Xu
2021	J	`jnl`	Characterization of the Variation Spaces Corresponding to Shallow Neural Networks. CoRR Jonathan W. Siegel, Jinchao Xu
2021	J	`jnl`	Improved Convergence Rates for the Orthogonal Greedy Algorithm. CoRR Jonathan W. Siegel, Jinchao Xu
2021	J	`jnl`	Optimal Approximation Rates and Metric Entropy of ReLU$^k$ and Cosine Networks. CoRR Jonathan W. Siegel, Jinchao Xu
2020	J	`jnl`	Accuracy, Efficiency and Optimization of Signal Fragmentation. Multiscale Model. Simul. Russel Caflisch, Hung-Hsu Chou, Jonathan W. Siegel
2020	J	`jnl`	Approximation rates for neural networks with general activation functions. Neural Networks Jonathan W. Siegel, Jinchao Xu
2020	J	`jnl`	High-Order Approximation Rates for Neural Networks with ReLU CoRR Jonathan W. Siegel, Jinchao Xu
2020	J	`jnl`	Training Sparse Neural Networks using Compressed Sensing. CoRR Jonathan W. Siegel, Jianhong Chen, Jinchao Xu
2019	J	`jnl`	On the Approximation Properties of Neural Networks. CoRR Jonathan W. Siegel, Jinchao Xu
2018	—	`—`	Accelerated First-Order Optimization with Orthogonality Constraints Jonathan W. Siegel

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Approximation Rates for Shallow ReLU\(^k\) Neural Networks on Sobolev Spaces via the Radon Transform. SIAM J. Math. Anal. Tong Mao, Jonathan W. Siegel, Jinchao Xu
2026	J	`jnl`	Quantitative Approximation Rates for Group Equivariant Learning. CoRR Jonathan W. Siegel, Snir Hordan, Hannah Lawrence, Ali Syed, Nadav Dym
2025	J	`jnl`	In-Context Multi-Operator Learning with DeepOSets. CoRR Shao-Ting Chiu, Aditya Nambiar, Ali Syed, Jonathan W. Siegel, Ulisses Braga-Neto
2025	J	`jnl`	Nearly optimal bounds on the Fourier sampling numbers of Besov spaces. CoRR Jonathan W. Siegel
2025	A*	`conf`	On the expressiveness and spectral bias of KANs. ICLR Yixuan Wang, Jonathan W. Siegel, Ziming Liu, Thomas Y. Hou
2025	J	`jnl`	Optimal Recovery Meets Minimax Estimation. CoRR Ronald A. DeVore, Robert D. Nowak, Rahul Parhi, Guergana Petrova, Jonathan W. Siegel
2025	J	`jnl`	Sharp Convergence Rates for Matching Pursuit. IEEE Trans. Inf. Theory Jason M. Klusowski, Jonathan W. Siegel
2024	J	`jnl`	Approximation Rates for Shallow ReLU CoRR Tong Mao, Jonathan W. Siegel, Jinchao Xu
2024	J	`jnl`	Convergence and error control of consistent PINNs for elliptic PDEs. CoRR Andrea Bonito, Ronald A. DeVore, Guergana Petrova, Jonathan W. Siegel
2024	A*	`conf`	Equivariant Frames and the Impossibility of Continuous Canonicalization. ICML Nadav Dym, Hannah Lawrence, Jonathan W. Siegel
2024	J	`jnl`	Equivariant Frames and the Impossibility of Continuous Canonicalization. CoRR Nadav Dym, Hannah Lawrence, Jonathan W. Siegel
2024	A*	`conf`	Nesterov acceleration despite very noisy gradients. NeurIPS Kanan Gupta, Jonathan W. Siegel, Stephan Wojtowytsch
2024	J	`jnl`	On the expressiveness and spectral bias of KANs. CoRR Yixuan Wang, Jonathan W. Siegel, Ziming Liu, Thomas Y. Hou
2024	J	`jnl`	Sharp Bounds on the Approximation Rates, Metric Entropy, and n-Widths of Shallow Neural Networks. Found. Comput. Math. Jonathan W. Siegel, Jinchao Xu
2024	J	`jnl`	Sharp Lower Bounds on the Manifold Widths of Sobolev and Besov Spaces. CoRR Jonathan W. Siegel
2024	J	`jnl`	Sharp lower bounds on the manifold widths of Sobolev and Besov spaces. J. Complex. Jonathan W. Siegel
2023	J	`jnl`	A qualitative difference between gradient flows of convex functions in finite- and infinite-dimensional Hilbert spaces. CoRR Jonathan W. Siegel, Stephan Wojtowytsch
2023	J	`jnl`	Achieving acceleration despite very noisy gradients. CoRR Kanan Gupta, Jonathan W. Siegel, Stephan Wojtowytsch
2023	J	`jnl`	Entropy-based convergence rates of greedy algorithms. CoRR Yuwen Li, Jonathan W. Siegel
2023	J	`jnl`	Greedy training algorithms for neural networks and applications to PDEs. J. Comput. Phys. Jonathan W. Siegel, Qingguo Hong, Xianlin Jin, Wenrui Hao, Jinchao Xu
2023	J	`jnl`	Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev and Besov Spaces. J. Mach. Learn. Res. Jonathan W. Siegel
2023	J	`jnl`	Optimal Approximation of Zonoids and Uniform Approximation by Shallow Neural Networks. CoRR Jonathan W. Siegel
2023	J	`jnl`	Sharp Convergence Rates for Matching Pursuit. CoRR Jason M. Klusowski, Jonathan W. Siegel
2023	J	`jnl`	Sharp Lower Bounds on Interpolation by Deep ReLU Neural Networks at Irregularly Spaced Data. CoRR Jonathan W. Siegel
2023	J	`jnl`	Weighted variation spaces and approximation by shallow ReLU networks. CoRR Ronald A. DeVore, Robert D. Nowak, Rahul Parhi, Jonathan W. Siegel
2022	J	`jnl`	On the Activation Function Dependence of the Spectral Bias of Neural Networks. CoRR Qingguo Hong, Qinyang Tan, Jonathan W. Siegel, Jinchao Xu
2022	J	`jnl`	Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev Spaces. CoRR Jonathan W. Siegel
2022	J	`jnl`	Optimal Convergence Rates for the Orthogonal Greedy Algorithm. IEEE Trans. Inf. Theory Jonathan W. Siegel, Jinchao Xu
2021	J	`jnl`	An efficient greedy training algorithm for neural networks and applications in PDEs. CoRR Wenrui Hao, Xianlin Jin, Jonathan W. Siegel, Jinchao Xu
2021	J	`jnl`	Characterization of the Variation Spaces Corresponding to Shallow Neural Networks. CoRR Jonathan W. Siegel, Jinchao Xu
2021	J	`jnl`	Improved Convergence Rates for the Orthogonal Greedy Algorithm. CoRR Jonathan W. Siegel, Jinchao Xu
2021	J	`jnl`	Optimal Approximation Rates and Metric Entropy of ReLU$^k$ and Cosine Networks. CoRR Jonathan W. Siegel, Jinchao Xu
2020	J	`jnl`	Accuracy, Efficiency and Optimization of Signal Fragmentation. Multiscale Model. Simul. Russel Caflisch, Hung-Hsu Chou, Jonathan W. Siegel
2020	J	`jnl`	Approximation rates for neural networks with general activation functions. Neural Networks Jonathan W. Siegel, Jinchao Xu
2020	J	`jnl`	High-Order Approximation Rates for Neural Networks with ReLU CoRR Jonathan W. Siegel, Jinchao Xu
2020	J	`jnl`	Training Sparse Neural Networks using Compressed Sensing. CoRR Jonathan W. Siegel, Jianhong Chen, Jinchao Xu
2019	J	`jnl`	On the Approximation Properties of Neural Networks. CoRR Jonathan W. Siegel, Jinchao Xu
2018	—	`—`	Accelerated First-Order Optimization with Orthogonality Constraints Jonathan W. Siegel