Clayton G. Webster

40 papers Journal 35Unranked 5

Year	Rank	Type	Title / Venue / Authors
2024	—	`conf`	Examining Policy Entropy of Reinforcement Learning Agents for Personalization Tasks. ICPRAI (1) Anton Dereventsov, Andrew Starnes, Clayton G. Webster
2024	—	`conf`	Mamba for Scalable and Efficient Personalized Recommendations. ICDM (Workshops) Andrew Starnes, Clayton G. Webster
2024	J	`jnl`	Mamba for Scalable and Efficient Personalized Recommendations. CoRR Andrew Starnes, Clayton G. Webster
2023	—	`conf`	Increasing Entropy to Boost Policy Gradient Performance on Personalization Tasks. ICDM (Workshops) Andrew Starnes, Anton Dereventsov, Clayton G. Webster
2023	J	`jnl`	Increasing Entropy to Boost Policy Gradient Performance on Personalization Tasks. CoRR Andrew Starnes, Anton Dereventsov, Clayton G. Webster
2022	J	`jnl`	Examining Policy Entropy of Reinforcement Learning Agents for Personalization Tasks. CoRR Anton Dereventsov, Andrew Starnes, Clayton G. Webster
2021	J	`jnl`	Offline Policy Comparison under Limited Historical Agent-Environment Interactions. CoRR Anton Dereventsov, Joseph D. Daws Jr., Clayton G. Webster
2021	—	`conf`	On the Unreasonable Efficiency of State Space Clustering in Personalization Tasks. ICDM (Workshops) Anton Dereventsov, Ranga Raju Vatsavai, Clayton G. Webster
2021	J	`jnl`	On the Unreasonable Efficiency of State Space Clustering in Personalization Tasks. CoRR Anton Dereventsov, Ranga Raju Vatsavai, Clayton G. Webster
2021	J	`jnl`	Robust Learning with Implicit Residual Networks. Mach. Learn. Knowl. Extr. Viktor Reshniak, Clayton G. Webster
2020	J	`jnl`	A Nonlocal Feature-Driven Exemplar-Based Approach for Image Inpainting. SIAM J. Imaging Sci. Viktor Reshniak, Jeremy Trageser, Clayton G. Webster
2020	J	`jnl`	An adaptive stochastic gradient-free approach for high-dimensional blackbox optimization. CoRR Anton Dereventsov, Clayton G. Webster, Joseph D. Daws Jr.
2020	J	`jnl`	Analysis of The Ratio of $\ell_1$ and $\ell_2$ Norms in Compressed Sensing. CoRR Yiming Xu, Akil Narayan, Hoang Tran, Clayton G. Webster
2020	—	`conf`	Neural network integral representations with the ReLU activation function. MSML Armenak Petrosyan, Anton Dereventsov, Clayton G. Webster
2019	J	`jnl`	A Polynomial-Based Approach for Architectural Design and Learning with Deep Neural Networks. CoRR Joseph Daws Jr., Clayton G. Webster
2019	J	`jnl`	A Weighted 𝓁 CoRR Joseph Daws Jr., Armenak Petrosyan, Hoang Tran, Clayton G. Webster
2019	J	`jnl`	A nonlocal feature-driven exemplar-based approach for image inpainting. CoRR Viktor Reshniak, Jeremy Trageser, Clayton G. Webster
2019	J	`jnl`	An Improved Discrete Least-Squares/Reduced-Basis Method for Parameterized Elliptic PDEs. J. Sci. Comput. Max D. Gunzburger, Michael Schneier, Clayton G. Webster, Guannan Zhang
2019	J	`jnl`	Analysis of Deep Neural Networks with Quasi-optimal polynomial approximation rates. CoRR Joseph Daws Jr., Clayton G. Webster
2019	J	`jnl`	Greedy Shallow Networks: A New Approach for Constructing and Training Neural Networks. CoRR Anton Dereventsov, Armenak Petrosyan, Clayton G. Webster
2019	J	`jnl`	Neural network integral representations with the ReLU activation function. CoRR Anton Dereventsov, Armenak Petrosyan, Clayton G. Webster
2019	J	`jnl`	Robust learning with implicit residual networks. CoRR Viktor Reshniak, Clayton G. Webster
2018	J	`jnl`	Polynomial approximation via compressed sensing of high-dimensional functions on lower sets. Math. Comput. Abdellah Chkifa, Nick C. Dexter, Hoang Tran, Clayton G. Webster
2018	J	`jnl`	Reconstruction of jointly sparse vectors via manifold optimization. CoRR Armenak Petrosyan, Hoang Tran, Clayton G. Webster
2017	J	`jnl`	Analysis of quasi-optimal polynomial approximations for parameterized PDEs with deterministic and stochastic coefficients. Numerische Mathematik Hoang Tran, Clayton G. Webster, Guannan Zhang
2016	J	`jnl`	A dynamically adaptive sparse grids method for quasi-optimal interpolation of multidimensional functions. Comput. Math. Appl. Miroslav Stoyanov, Clayton G. Webster
2016	J	`jnl`	Accelerating Stochastic Collocation Methods for Partial Differential Equations with Random Input Data. SIAM/ASA J. Uncertain. Quantification Diego Galindo, Peter Jantsch, Clayton G. Webster, Guannan Zhang
2016	J	`jnl`	Explicit cost bounds of stochastic Galerkin approximations for parameterized PDEs with random coefficients. Comput. Math. Appl. Nick C. Dexter, Clayton G. Webster, Guannan Zhang
2016	J	`jnl`	Hyperspherical Sparse Approximation Techniques for High-Dimensional Discontinuity Detection. SIAM Rev. Guannan Zhang, Clayton G. Webster, Max D. Gunzburger, John V. Burkardt
2016	J	`jnl`	Numerical methods for a class of nonlocal diffusion problems with the use of backward SDEs. Comput. Math. Appl. Guannan Zhang, Weidong Zhao, Clayton G. Webster, Max D. Gunzburger
2016	J	`jnl`	Uncertainty Quantification and High Performance Computing (Dagstuhl Seminar 16372). Dagstuhl Reports Vincent Heuveline, Michael Schick, Clayton G. Webster, Peter Zaspel
2015	J	`jnl`	A Hyperspherical Adaptive Sparse-Grid Method for High-Dimensional Discontinuity Detection. SIAM J. Numer. Anal. Guannan Zhang, Clayton G. Webster, Max D. Gunzburger, John V. Burkardt
2015	J	`jnl`	A Multilevel Stochastic Collocation Method for Partial Differential Equations with Random Input Data. SIAM/ASA J. Uncertain. Quantification Aretha L. Teckentrup, Peter Jantsch, Clayton G. Webster, Max D. Gunzburger
2015	J	`jnl`	Numerical Analysis of Fixed Point Algorithms in the Presence of Hardware Faults. SIAM J. Sci. Comput. Miroslav Stoyanov, Clayton G. Webster
2014	J	`jnl`	A Hybrid Sparse-Grid Approach for Nonlinear Filtering Problems Based on Adaptive-Domain of the Zakai Equation Approximations. SIAM/ASA J. Uncertain. Quantification Feng Bao, Yanzhao Cao, Clayton G. Webster, Guannan Zhang
2014	J	`jnl`	An adaptive sparse-grid iterative ensemble Kalman filter approach for parameter field estimation. Int. J. Comput. Math. Clayton G. Webster, Guannan Zhang, Max D. Gunzburger
2014	J	`jnl`	Foreword. Int. J. Comput. Math. Rick Archibald, Clayton G. Webster
2014	J	`jnl`	Stochastic finite element methods for partial differential equations with random input data. Acta Numer. Max D. Gunzburger, Clayton G. Webster, Guannan Zhang
2008	J	`jnl`	A Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data. SIAM J. Numer. Anal. Fabio Nobile, Raúl Tempone, Clayton G. Webster
2008	J	`jnl`	An Anisotropic Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data. SIAM J. Numer. Anal. Fabio Nobile, Raúl Tempone, Clayton G. Webster