A Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data. - Fabio Nobile; Raúl Tempone; Clayton G. Webster (SIAM J. Numer. Anal., 2008)

An Anisotropic Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data. - Fabio Nobile; Raúl Tempone; Clayton G. Webster (SIAM J. Numer. Anal., 2008)

Stochastic finite element methods for partial differential equations with random input data. - Max D. Gunzburger; Clayton G. Webster; Guannan Zhang (Acta Numer., 2014)

Foreword. - Rick Archibald; Clayton G. Webster (Int. J. Comput. Math., 2014)

An adaptive sparse-grid iterative ensemble Kalman filter approach for parameter field estimation. - Clayton G. Webster; Guannan Zhang; Max D. Gunzburger (Int. J. Comput. Math., 2014)

A Hybrid Sparse-Grid Approach for Nonlinear Filtering Problems Based on Adaptive-Domain of the Zakai Equation Approximations. - Feng Bao 005; Yanzhao Cao; Clayton G. Webster; Guannan Zhang (SIAM/ASA J. Uncertain. Quantification, 2014)

A Multilevel Stochastic Collocation Method for Partial Differential Equations with Random Input Data. - Aretha L. Teckentrup; Peter Jantsch; Clayton G. Webster; Max D. Gunzburger (SIAM/ASA J. Uncertain. Quantification, 2015)

A Hyperspherical Adaptive Sparse-Grid Method for High-Dimensional Discontinuity Detection. - Guannan Zhang; Clayton G. Webster; Max D. Gunzburger; John V. Burkardt (SIAM J. Numer. Anal., 2015)

Numerical Analysis of Fixed Point Algorithms in the Presence of Hardware Faults. - Miroslav Stoyanov; Clayton G. Webster (SIAM J. Sci. Comput., 2015)

Explicit cost bounds of stochastic Galerkin approximations for parameterized PDEs with random coefficients. - Nick C. Dexter; Clayton G. Webster; Guannan Zhang (Comput. Math. Appl., 2016)

A dynamically adaptive sparse grids method for quasi-optimal interpolation of multidimensional functions. - Miroslav Stoyanov; Clayton G. Webster (Comput. Math. Appl., 2016)

Numerical methods for a class of nonlocal diffusion problems with the use of backward SDEs. - Guannan Zhang; Weidong Zhao; Clayton G. Webster; Max D. Gunzburger (Comput. Math. Appl., 2016)

Uncertainty Quantification and High Performance Computing (Dagstuhl Seminar 16372). - Vincent Heuveline; Michael Schick; Clayton G. Webster; Peter Zaspel (Dagstuhl Reports, 2016)

Accelerating Stochastic Collocation Methods for Partial Differential Equations with Random Input Data. - Diego Galindo; Peter Jantsch; Clayton G. Webster; Guannan Zhang (SIAM/ASA J. Uncertain. Quantification, 2016)

Hyperspherical Sparse Approximation Techniques for High-Dimensional Discontinuity Detection. - Guannan Zhang; Clayton G. Webster; Max D. Gunzburger; John V. Burkardt (SIAM Rev., 2016)

Analysis of quasi-optimal polynomial approximations for parameterized PDEs with deterministic and stochastic coefficients. - Hoang Tran; Clayton G. Webster; Guannan Zhang (Numerische Mathematik, 2017)

Reconstruction of jointly sparse vectors via manifold optimization. - Armenak Petrosyan; Hoang Tran; Clayton G. Webster (CoRR, 2018)

Polynomial approximation via compressed sensing of high-dimensional functions on lower sets. - Abdellah Chkifa; Nick C. Dexter; Hoang Tran; Clayton G. Webster (Math. Comput., 2018)

Greedy Shallow Networks: A New Approach for Constructing and Training Neural Networks. - Anton Dereventsov; Armenak Petrosyan; Clayton G. Webster (CoRR, 2019)

A Polynomial-Based Approach for Architectural Design and Learning with Deep Neural Networks. - Joseph Daws Jr.; Clayton G. Webster (CoRR, 2019)

A Weighted 𝓁1-Minimization Approach For Wavelet Reconstruction of Signals and Images. - Joseph Daws Jr.; Armenak Petrosyan; Hoang Tran; Clayton G. Webster (CoRR, 2019)

Analysis of Deep Neural Networks with Quasi-optimal polynomial approximation rates. - Joseph Daws Jr.; Clayton G. Webster (CoRR, 2019)

An Improved Discrete Least-Squares/Reduced-Basis Method for Parameterized Elliptic PDEs. - Max D. Gunzburger; Michael Schneier; Clayton G. Webster; Guannan Zhang (J. Sci. Comput., 2019)

Neural network integral representations with the ReLU activation function. - Armenak Petrosyan; Anton Dereventsov; Clayton G. Webster (MSML, 2020)

Analysis of The Ratio of $ ell_1$ and $ ell_2$ Norms in Compressed Sensing. - Yiming Xu; Akil Narayan 001; Hoang Tran; Clayton G. Webster (CoRR, 2020)

An adaptive stochastic gradient-free approach for high-dimensional blackbox optimization. - Anton Dereventsov; Clayton G. Webster; Joseph D. Daws Jr. (CoRR, 2020)

A Nonlocal Feature-Driven Exemplar-Based Approach for Image Inpainting. - Viktor Reshniak; Jeremy Trageser; Clayton G. Webster (SIAM J. Imaging Sci., 2020)

On the Unreasonable Efficiency of State Space Clustering in Personalization Tasks. - Anton Dereventsov; Ranga Raju Vatsavai; Clayton G. Webster (ICDM (Workshops), 2021)

Offline Policy Comparison under Limited Historical Agent-Environment Interactions. - Anton Dereventsov; Joseph D. Daws Jr.; Clayton G. Webster (CoRR, 2021)

Robust Learning with Implicit Residual Networks. - Viktor Reshniak; Clayton G. Webster (Mach. Learn. Knowl. Extr., 2021)

Increasing Entropy to Boost Policy Gradient Performance on Personalization Tasks. - Andrew Starnes; Anton Dereventsov; Clayton G. Webster (ICDM (Workshops), 2023)

Mamba for Scalable and Efficient Personalized Recommendations. - Andrew Starnes; Clayton G. Webster (ICDM (Workshops), 2024)

Examining Policy Entropy of Reinforcement Learning Agents for Personalization Tasks. - Anton Dereventsov; Andrew Starnes; Clayton G. Webster (ICPRAI (1), 2024)