Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Ching-Pei Lee"'
Autor:
Huikun Zhang, Spencer S Ericksen, Ching-Pei Lee, Gene E Ananiev, Nathan Wlodarchak, Peng Yu, Julie C Mitchell, Anthony Gitter, Stephen J Wright, F Michael Hoffmann, Scott A Wildman, Michael A Newton
Publikováno v:
PLoS Computational Biology, Vol 15, Iss 8, p e1006813 (2019)
Prediction of compounds that are active against a desired biological target is a common step in drug discovery efforts. Virtual screening methods seek some active-enriched fraction of a library for experimental testing. Where data are too scarce to t
Externí odkaz:
https://doaj.org/article/4a4d5ae099294f29b49940194c4bd237
Autor:
Ching-Pei Lee, 李靜沛
101
Linear rankSVM is one of the widely used methods for learning to rank. Although its performance may be inferior to nonlinear methods such as kernel rankSVM and gradient boosting decision trees, linear rankSVM is useful to quickly produce a b
Linear rankSVM is one of the widely used methods for learning to rank. Although its performance may be inferior to nonlinear methods such as kernel rankSVM and gradient boosting decision trees, linear rankSVM is useful to quickly produce a b
Externí odkaz:
http://ndltd.ncl.edu.tw/handle/95333963386449972611
Publikováno v:
Mathematical Programming Computation. 14:543-591
In this paper, we present a limited-memory common-directions method for smooth optimization that interpolates between first- and second-order methods. At each iteration, a subspace of a limited dimension size is constructed using first-order informat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b576ee353577354d0eb0a8f7a4aea7db
https://doi.org/10.1007/s12532-022-00219-z
https://doi.org/10.1007/s12532-022-00219-z
Autor:
Stephen J. Wright, Ching-pei Lee
Publikováno v:
Mathematics of Computation. 89:2217-2248
We consider coordinate descent methods on convex quadratic problems, in which exact line searches are performed at each iteration. (This algorithm is identical to Gauss-Seidel on the equivalent symmetric positive definite linear system.) We describe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::354aa55ab058177d850b18d4bb7895a7
https://doi.org/10.1090/mcom/3530
https://doi.org/10.1090/mcom/3530
Autor:
Ching-pei Lee
For regularized optimization that minimizes the sum of a smooth term and a regularizer that promotes structured solutions, inexact proximal-Newton-type methods, or successive quadratic approximation (SQA) methods, are widely used for their superlinea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f72aa7dfbba9db6e649df6d73a214a34
Autor:
Ching-pei Lee, Stephen J. Wright
Publikováno v:
IMA Journal of Numerical Analysis. 39:1246-1275
Variants of the coordinate descent approach for minimizing a nonlinear function are distinguished in part by the order in which coordinates are considered for relaxation. Three common orderings are cyclic (CCD), in which we cycle through the componen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::973c16e1e5b12392b66c2cab4846519b
https://doi.org/10.1093/imanum/dry040
https://doi.org/10.1093/imanum/dry040
Autor:
Michael A. Newton, Ching-pei Lee, Anthony Gitter, Peng Yu, Stephen J. Wright, Gene E. Ananiev, Nathan Wlodarchak, Huikun Zhang, Scott A. Wildman, Spencer S. Ericksen, F. Michael Hoffmann, Julie C. Mitchell
Publikováno v:
PLoS Computational Biology, Vol 15, Iss 8, p e1006813 (2019)
PLoS Computational Biology
PLoS Computational Biology
Prediction of compounds that are active against a desired biological target is a common step in drug discovery efforts. Virtual screening methods seek some active-enriched fraction of a library for experimental testing. Where data are too scarce to t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c635ea432505bfd480dab1f465a93766
https://doaj.org/article/4a4d5ae099294f29b49940194c4bd237
https://doaj.org/article/4a4d5ae099294f29b49940194c4bd237
Publikováno v:
KDD
We propose a communication- and computation-efficient distributed optimization algorithm using second-order information for solving ERM problems with a nonsmooth regularization term. Current second-order and quasi-Newton methods for this problem eith
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::302ec79070bd741b97b1dfd1f627bd44
https://doi.org/10.1145/3219819.3220075
https://doi.org/10.1145/3219819.3220075
Autor:
Stephen J. Wright, Ching-pei Lee
Successive quadratic approximations, or second-order proximal methods, are useful for minimizing functions that are a sum of a smooth part and a convex, possibly nonsmooth part that promotes regularization. Most analyses of iteration complexity focus
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::918054756b6fda93b2035f5367216520
http://arxiv.org/abs/1803.01298
http://arxiv.org/abs/1803.01298