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ORCIDClayton G. Webster
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- affiliation: Oak Ridge National Laboratory, TN, USA
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2020 – today
- 2024
[i16]Andrew Starnes, Clayton G. Webster:
Mamba for Scalable and Efficient Personalized Recommendations. CoRR abs/2409.17165 (2024)- 2023
- [c3]Andrew Starnes, Anton Dereventsov, Clayton G. Webster:
Increasing Entropy to Boost Policy Gradient Performance on Personalization Tasks. ICDM (Workshops) 2023: 1551-1558 - [i15]Andrew Starnes, Anton Dereventsov, Clayton G. Webster:
Increasing Entropy to Boost Policy Gradient Performance on Personalization Tasks. CoRR abs/2310.05324 (2023) - 2022
- [i14]Anton Dereventsov, Andrew Starnes, Clayton G. Webster:
Examining Policy Entropy of Reinforcement Learning Agents for Personalization Tasks. CoRR abs/2211.11869 (2022) - 2021
- [j19]Viktor Reshniak
, Clayton G. Webster:
Robust Learning with Implicit Residual Networks. Mach. Learn. Knowl. Extr. 3(1): 34-55 (2021) - [c2]Anton Dereventsov, Ranga Raju Vatsavai, Clayton G. Webster:
On the Unreasonable Efficiency of State Space Clustering in Personalization Tasks. ICDM (Workshops) 2021: 742-749 - [i13]Anton Dereventsov, Joseph D. Daws Jr., Clayton G. Webster:
Offline Policy Comparison under Limited Historical Agent-Environment Interactions. CoRR abs/2106.03934 (2021) - [i12]Anton Dereventsov, Ranga Raju Vatsavai, Clayton G. Webster:
On the Unreasonable Efficiency of State Space Clustering in Personalization Tasks. CoRR abs/2112.13141 (2021) - 2020
- [j18]Viktor Reshniak, Jeremy Trageser, Clayton G. Webster:
A Nonlocal Feature-Driven Exemplar-Based Approach for Image Inpainting. SIAM J. Imaging Sci. 13(4): 2140-2168 (2020) - [c1]Armenak Petrosyan, Anton Dereventsov, Clayton G. Webster:
Neural network integral representations with the ReLU activation function. MSML 2020: 128-143 - [i11]Yiming Xu, Akil Narayan, Hoang Tran, Clayton G. Webster:
Analysis of The Ratio of $\ell_1$ and $\ell_2$ Norms in Compressed Sensing. CoRR abs/2004.05873 (2020) - [i10]Anton Dereventsov, Clayton G. Webster, Joseph D. Daws Jr.:
An adaptive stochastic gradient-free approach for high-dimensional blackbox optimization. CoRR abs/2006.10887 (2020)
2010 – 2019
- 2019
- [j17]Max D. Gunzburger, Michael Schneier, Clayton G. Webster, Guannan Zhang:
An Improved Discrete Least-Squares/Reduced-Basis Method for Parameterized Elliptic PDEs. J. Sci. Comput. 81(1): 76-91 (2019) - [i9]Anton Dereventsov, Armenak Petrosyan, Clayton G. Webster:
Greedy Shallow Networks: A New Approach for Constructing and Training Neural Networks. CoRR abs/1905.10409 (2019) - [i8]Joseph Daws Jr., Clayton G. Webster:
A Polynomial-Based Approach for Architectural Design and Learning with Deep Neural Networks. CoRR abs/1905.10457 (2019) - [i7]Viktor Reshniak, Clayton G. Webster:
Robust learning with implicit residual networks. CoRR abs/1905.10479 (2019) - [i6]Joseph Daws Jr., Armenak Petrosyan, Hoang Tran, Clayton G. Webster:
A Weighted 𝓁1-Minimization Approach For Wavelet Reconstruction of Signals and Images. CoRR abs/1909.07270 (2019) - [i5]Viktor Reshniak, Jeremy Trageser, Clayton G. Webster:
A nonlocal feature-driven exemplar-based approach for image inpainting. CoRR abs/1909.09301 (2019) - [i4]Anton Dereventsov, Armenak Petrosyan, Clayton G. Webster:
Neural network integral representations with the ReLU activation function. CoRR abs/1910.02743 (2019) - [i3]Joseph Daws Jr., Clayton G. Webster:
Analysis of Deep Neural Networks with Quasi-optimal polynomial approximation rates. CoRR abs/1912.02302 (2019) - 2018
- [j16]Abdellah Chkifa, Nick C. Dexter, Hoang Tran, Clayton G. Webster:
Polynomial approximation via compressed sensing of high-dimensional functions on lower sets. Math. Comput. 87(311): 1415-1450 (2018) - [i2]Armenak Petrosyan, Hoang Tran, Clayton G. Webster:
Reconstruction of jointly sparse vectors via manifold optimization. CoRR abs/1811.08778 (2018) - 2017
- [j15]Hoang Tran, Clayton G. Webster, Guannan Zhang:
Analysis of quasi-optimal polynomial approximations for parameterized PDEs with deterministic and stochastic coefficients. Numerische Mathematik 137(2): 451-493 (2017) - 2016
- [j14]Nick C. Dexter, Clayton G. Webster, Guannan Zhang:
Explicit cost bounds of stochastic Galerkin approximations for parameterized PDEs with random coefficients. Comput. Math. Appl. 71(11): 2231-2256 (2016) - [j13]Miroslav Stoyanov, Clayton G. Webster:
A dynamically adaptive sparse grids method for quasi-optimal interpolation of multidimensional functions. Comput. Math. Appl. 71(11): 2449-2465 (2016) - [j12]Guannan Zhang, Weidong Zhao, Clayton G. Webster, Max D. Gunzburger:
Numerical methods for a class of nonlocal diffusion problems with the use of backward SDEs. Comput. Math. Appl. 71(11): 2479-2496 (2016) - [j11]Diego Galindo, Peter Jantsch, Clayton G. Webster, Guannan Zhang:
Accelerating Stochastic Collocation Methods for Partial Differential Equations with Random Input Data. SIAM/ASA J. Uncertain. Quantification 4(1): 1111-1137 (2016) - [j10]Guannan Zhang, Clayton G. Webster, Max D. Gunzburger, John V. Burkardt:
Hyperspherical Sparse Approximation Techniques for High-Dimensional Discontinuity Detection. SIAM Rev. 58(3): 517-551 (2016) - [i1]Vincent Heuveline, Michael Schick, Clayton G. Webster, Peter Zaspel:
Uncertainty Quantification and High Performance Computing (Dagstuhl Seminar 16372). Dagstuhl Reports 6(9): 59-73 (2016) - 2015
- [j9]Aretha L. Teckentrup, Peter Jantsch, Clayton G. Webster, Max D. Gunzburger:
A Multilevel Stochastic Collocation Method for Partial Differential Equations with Random Input Data. SIAM/ASA J. Uncertain. Quantification 3(1): 1046-1074 (2015) - [j8]Guannan Zhang, Clayton G. Webster, Max D. Gunzburger, John V. Burkardt:
A Hyperspherical Adaptive Sparse-Grid Method for High-Dimensional Discontinuity Detection. SIAM J. Numer. Anal. 53(3): 1508-1536 (2015) - [j7]Miroslav Stoyanov, Clayton G. Webster:
Numerical Analysis of Fixed Point Algorithms in the Presence of Hardware Faults. SIAM J. Sci. Comput. 37(5) (2015) - 2014
- [j6]Max D. Gunzburger, Clayton G. Webster, Guannan Zhang:
Stochastic finite element methods for partial differential equations with random input data. Acta Numer. 23: 521-650 (2014) - [j5]Rick Archibald, Clayton G. Webster:
Foreword. Int. J. Comput. Math. 91(4): 705-706 (2014) - [j4]Clayton G. Webster, Guannan Zhang, Max D. Gunzburger:
An adaptive sparse-grid iterative ensemble Kalman filter approach for parameter field estimation. Int. J. Comput. Math. 91(4): 798-817 (2014) - [j3]Feng Bao, Yanzhao Cao, Clayton G. Webster, Guannan Zhang:
A Hybrid Sparse-Grid Approach for Nonlinear Filtering Problems Based on Adaptive-Domain of the Zakai Equation Approximations. SIAM/ASA J. Uncertain. Quantification 2(1): 784-804 (2014)
2000 – 2009
- 2008
- [j2]Fabio Nobile, Raúl Tempone, Clayton G. Webster:
A Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data. SIAM J. Numer. Anal. 46(5): 2309-2345 (2008) - [j1]Fabio Nobile, Raúl Tempone, Clayton G. Webster:
An Anisotropic Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data. SIAM J. Numer. Anal. 46(5): 2411-2442 (2008)
Coauthor Index
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