Brandon T. Willard

Computational Mechanics · 2015

A researcher in the Science corpus: 1 paper; trusts 19 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computational Mechanics — 19 citations into it; #5,816 of 10,001 by trust here, #7,501 by citations
• Mathematical Physics — 10 citations into it
• Statistics and Probability — 10 citations into it
• Artificial Intelligence — 8 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it
• Numerical Analysis — 6 citations into it
• Optimization & Training — 6 citations into it
• Computational Theory and Mathematics — 5 citations into it; #7,175 of 12,182 by trust here, #8,294 by citations

Whom Brandon T. Willard trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Robert Tibshirani
2. Amir Beck
3. Yurii E. Nesterov
4. Hédy Attouch
5. Julian Besag
6. Hui Zou
7. Trevor J. Hastie
8. Antonin Chambolle
9. Marc Teboulle
10. Mário A. T. Figueiredo

Who trusts Brandon T. Willard overall

1. Nicholas G. Polson
2. Bryan T. Kelly
3. Jan Hendrik Witte
4. J. B. Heaton
5. Shihao Gu
6. D. Xiu

Papers from Brandon T. Willard's perspective

Ordered by how strongly the researchers Brandon T. Willard trusts endorse each paper. Same corpus, a different order for every researcher.

1. Regression Shrinkage and Selection via the Lasso
2. Addendum: Regularization and variable selection via the elastic net
3. Variational Analysis
4. Proximal Algorithms
5. Nonlinear total variation based noise removal algorithms
6. Gradient-based algorithms with applications to signal-recovery problems
7. The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming
8. A General Framework for a Class of First Order Primal-Dual Algorithms for Convex Optimization in Imaging Science (SIAM Journal of Imaging Sciences)
9. On the convergence of the proximal algorithm for nonsmooth functions involving analytic features
10. A method for solving the convex programming problem with convergence rate O(1/k^2)

Their papers

• Proximal Algorithms in Statistics and Machine Learning (2015) — 169 citations