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pro vyhledávání: '"Johnson, Nicholas A."'
We study the problem of learning a partially observed matrix under the low rank assumption in the presence of fully observed side information that depends linearly on the true underlying matrix. This problem consists of an important generalization of
Externí odkaz:
http://arxiv.org/abs/2407.13731
Publikováno v:
Springer Machine Learning 2024
We study the Compressed Sensing (CS) problem, which is the problem of finding the most sparse vector that satisfies a set of linear measurements up to some numerical tolerance. We introduce an $\ell_2$ regularized formulation of CS which we reformula
Externí odkaz:
http://arxiv.org/abs/2306.04647
In this paper, we introduce a family of games called concave pro-rata games. In such a game, players place their assets into a pool, and the pool pays out some concave function of all assets placed into it. Each player then receives a pro-rata share
Externí odkaz:
http://arxiv.org/abs/2302.02126
Autor:
Chien, Jo-Fan, Liu, Hanqing, Wang, Bang-An, Luo, Chongyuan, Bartlett, Anna, Castanon, Rosa, Johnson, Nicholas D., Nery, Joseph R., Osteen, Julia, Li, Junhao, Altshul, Jordan, Kenworthy, Mia, Valadon, Cynthia, Liem, Michelle, Claffey, Naomi, O'Connor, Carolyn, Seeker, Luise A., Ecker, Joseph R., Behrens, M. Margarita, Mukamel, Eran A.
Publikováno v:
In Neuron 7 August 2024 112(15):2524-2539
Autor:
Moskowitz, Ari, Pocock, Helen, Lagina, Anthony, Ng, Kee Chong, Scholefield, Barnaby R., Zelop, Carolyn M., Bray, Janet, Rossano, Joseph, Johnson, Nicholas J., Dunning, Joel, Olasveengen, Theresa, Raymond, Tia, Morales, David L.S., Carlese, Anthony, Elias, Marie, Berg, Katherine M., Drennan, Ian
Publikováno v:
In Resuscitation October 2024 203
Autor:
Johnson, Nicholas A. G.
In this work, we introduce a novel mathematical network model for community level preventative health interventions. We develop algorithms to approximately solve this novel formulation at large scale and we rigorously explore their theoretical proper
Externí odkaz:
http://arxiv.org/abs/2109.12730
Publikováno v:
Journal of Machine Learning Research, 24(267), 1-51 (2023)
We study the Sparse Plus Low-Rank decomposition problem (SLR), which is the problem of decomposing a corrupted data matrix into a sparse matrix of perturbations plus a low-rank matrix containing the ground truth. SLR is a fundamental problem in Opera
Externí odkaz:
http://arxiv.org/abs/2109.12701
Publikováno v:
In Neuromuscular Disorders June 2024 39:48-53
Autor:
Koch, R. Tobias, Erazo, Diana, Folly, Arran J., Johnson, Nicholas, Dellicour, Simon, Grubaugh, Nathan D., Vogels, Chantal B.F.
Publikováno v:
In One Health June 2024 18
Autor:
Tawil, Rabi *, Wagner, Kathryn R *, Hamel, Johanna I, Leung, Doris G, Statland, Jeffrey M, Wang, Leo H, Genge, Angela, Sacconi, Sabrina, Lochmüller, Hanns, Reyes-Leiva, David, Diaz-Manera, Jordi, Alonso-Perez, Jorge, Muelas, Nuria, Vilchez, Juan J, Pestronk, Alan, Gibson, Summer, Goyal, Namita A, Hayward, Lawrence J, Johnson, Nicholas, LoRusso, Samantha, Freimer, Miriam, Shieh, Perry B, Subramony, S H, van Engelen, Baziel, Kools, Joost, Leinhard, Olof Dahlqvist, Widholm, Per, Morabito, Christopher, Moxham, Christopher M, Cadavid, Diego, Mellion, Michelle L, Odueyungbo, Adefowope, Tracewell, William G, Accorsi, Anthony, Ronco, Lucienne, Gould, Robert J, Shoskes, Jennifer, Rojas, Luis Alejandro, Jiang, John G *
Publikováno v:
In The Lancet Neurology May 2024 23(5):477-486