Clayton G. Webster

According to our database, Clayton G. Webster authored at least 33 papers between 2008 and 2024.

Collaborative distances:

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

2024

Mamba for Scalable and Efficient Personalized Recommendations.

Andrew Starnes, Clayton G. Webster

CoRR, 2024

2023

Increasing Entropy to Boost Policy Gradient Performance on Personalization Tasks.

Andrew Starnes, Anton Dereventsov, Clayton G. Webster

Proceedings of the IEEE International Conference on Data Mining, 2023

2022

Examining Policy Entropy of Reinforcement Learning Agents for Personalization Tasks.

Anton Dereventsov, Andrew Starnes, Clayton G. Webster

CoRR, 2022

2021

Robust Learning with Implicit Residual Networks.

Viktor Reshniak, Clayton G. Webster

Mach. Learn. Knowl. Extr., 2021

Offline Policy Comparison under Limited Historical Agent-Environment Interactions.

Anton Dereventsov, Joseph D. Daws Jr., Clayton G. Webster

CoRR, 2021

On the Unreasonable Efficiency of State Space Clustering in Personalization Tasks.

Anton Dereventsov, Ranga Raju Vatsavai, Clayton G. Webster

Proceedings of the 2021 International Conference on Data Mining, 2021

2020

A Nonlocal Feature-Driven Exemplar-Based Approach for Image Inpainting.

Viktor Reshniak, Jeremy Trageser, Clayton G. Webster

SIAM J. Imaging Sci., 2020

An adaptive stochastic gradient-free approach for high-dimensional blackbox optimization.

Anton Dereventsov, Clayton G. Webster, Joseph D. Daws Jr.

CoRR, 2020

Analysis of The Ratio of $\ell_1$ and $\ell_2$ Norms in Compressed Sensing.

Yiming Xu, Akil Narayan, Hoang Tran, Clayton G. Webster

CoRR, 2020

Neural network integral representations with the ReLU activation function.

Armenak Petrosyan, Anton Dereventsov, Clayton G. Webster

Proceedings of Mathematical and Scientific Machine Learning, 2020

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

Analysis of Deep Neural Networks with Quasi-optimal polynomial approximation rates.

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

A Polynomial-Based Approach for Architectural Design and Learning with Deep Neural Networks.

Joseph Daws Jr., Clayton G. Webster

CoRR, 2019

Greedy Shallow Networks: A New Approach for Constructing and Training Neural Networks.

Anton Dereventsov, Armenak Petrosyan, Clayton G. Webster

CoRR, 2019

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

Reconstruction of jointly sparse vectors via manifold optimization.

Armenak Petrosyan, Hoang Tran, Clayton G. Webster

CoRR, 2018

2017

Analysis of quasi-optimal polynomial approximations for parameterized PDEs with deterministic and stochastic coefficients.

Hoang Tran, Clayton G. Webster, Guannan Zhang

Numerische Mathematik, 2017

2016

Hyperspherical Sparse Approximation Techniques for High-Dimensional Discontinuity Detection.

Guannan Zhang, Clayton G. Webster, Max D. Gunzburger, John V. Burkardt

SIAM Rev., 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

Uncertainty Quantification and High Performance Computing (Dagstuhl Seminar 16372).

Vincent Heuveline, Michael Schick, Clayton G. Webster, Peter Zaspel

Dagstuhl Reports, 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

A dynamically adaptive sparse grids method for quasi-optimal interpolation of multidimensional functions.

Miroslav Stoyanov, Clayton G. Webster

Comput. Math. Appl., 2016

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

2015

Numerical Analysis of Fixed Point Algorithms in the Presence of Hardware Faults.

Miroslav Stoyanov, Clayton G. Webster

SIAM J. Sci. Comput., 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

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

2014

A Hybrid Sparse-Grid Approach for Nonlinear Filtering Problems Based on Adaptive-Domain of the Zakai Equation Approximations.

Feng Bao, Yanzhao Cao, Clayton G. Webster, Guannan Zhang

SIAM/ASA J. Uncertain. Quantification, 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

Foreword.

Rick Archibald, Clayton G. Webster

Int. J. Comput. Math., 2014

Stochastic finite element methods for partial differential equations with random input data.

Max D. Gunzburger, Clayton G. Webster, Guannan Zhang

Acta Numer., 2014

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

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