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pro vyhledávání: '"Thompson, Philip A."'
Autor:
Thompson, Philip
We study least-squares trace regression when the parameter is the sum of a $r$-low-rank matrix and a $s$-sparse matrix and a fraction $\epsilon$ of the labels is corrupted. For subgaussian distributions and feature-dependent noise, we highlight three
Externí odkaz:
http://arxiv.org/abs/2310.19136
We revisit heavy-tailed corrupted least-squares linear regression assuming to have a corrupted $n$-sized label-feature sample of at most $\epsilon n$ arbitrary outliers. We wish to estimate a $p$-dimensional parameter $b^*$ given such sample of a lab
Externí odkaz:
http://arxiv.org/abs/2209.02856
Autor:
Wierda, William G *, Shah, Nirav N, Cheah, Chan Y, Lewis, David, Hoffmann, Marc S, Coombs, Catherine C, Lamanna, Nicole, Ma, Shuo, Jagadeesh, Deepa, Munir, Talha, Wang, Yucai, Eyre, Toby A, Rhodes, Joanna M, McKinney, Matthew, Lech-Maranda, Ewa, Tam, Constantine S, Jurczak, Wojciech, Izutsu, Koji, Alencar, Alvaro J, Patel, Manish R, Seymour, John F, Woyach, Jennifer A, Thompson, Philip A, Abada, Paolo B, Ho, Caleb, McNeely, Samuel C, Marella, Narasimha, Nguyen, Bastien, Wang, Chunxiao, Ruppert, Amy S, Nair, Binoj, Liu, Hui, Tsai, Donald E, Roeker, Lindsey E, Ghia, Paolo
Publikováno v:
In The Lancet Haematology September 2024 11(9):e682-e692
Autor:
Thompson, Philip E., Shortt, Jake
Publikováno v:
In Trends in Pharmacological Sciences June 2024 45(6):490-502
Autor:
Hampel, Paul J., Swaminathan, Mahesh, Rogers, Kerry A., Parry, Erin M., Burger, Jan A., Davids, Matthew S., Ding, Wei, Ferrajoli, Alessandra, Hyak, Jonathan M., Jain, Nitin, Kenderian, Saad S., Wang, Yucai, Wierda, William G., Woyach, Jennifer A., Parikh, Sameer A., Thompson, Philip A.
Publikováno v:
In Blood Advances 28 May 2024 8(10):2342-2350
Autor:
Tam, Constantine, Thompson, Philip A.
Publikováno v:
In Blood Advances 14 May 2024 8(9):2300-2309
Publikováno v:
In Heliyon 30 April 2024 10(8)
Autor:
Thompson, Philip
We study high-dimensional least-squares regression within a subgaussian statistical learning framework with heterogeneous noise. It includes $s$-sparse and $r$-low-rank least-squares regression when a fraction $\epsilon$ of the labels are adversarial
Externí odkaz:
http://arxiv.org/abs/2012.06750
Autor:
Wang, Xiaoyu, Patil, Nitin, Li, Fuyi, Wang, Zhikang, Zhan, Haolan, Schmidt, Daniel, Thompson, Philip, Guo, Yuming, Landersdorfer, Cornelia B., Shen, Hsin-Hui, Peleg, Anton Y., Li, Jian, Song, Jiangning
Publikováno v:
In Computers in Biology and Medicine January 2024 168
Akademický článek
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