Melih Elibol
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Ray: A distributed framework for emerging {AI} applications
P Moritz, R Nishihara, S Wang, A Tumanov, R Liaw, E Liang, M Elibol, ...
13th {USENIX} Symposium on Operating Systems Design and Implementation …, 2018
Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs
J Listgarten, M Weinstein, BP Kleinstiver, AA Sousa, JK Joung, J Crawford, ...
Nature biomedical engineering 2 (1), 38-47, 2018
Probabilistic matrix factorization for automated machine learning
N Fusi, R Sheth, M Elibol
Advances in neural information processing systems 31, 3348-3357, 2018
Cross-corpora unsupervised learning of trajectories in autism spectrum disorders
HM Elibol, V Nguyen, S Linderman, M Johnson, A Hashmi, F Doshi-Velez
The Journal of Machine Learning Research 17 (1), 4597-4634, 2016
Variance reduction with sparse gradients
M Elibol, L Lei, MI Jordan
arXiv preprint arXiv:2001.09623, 2020
Supervised topic models for clinical interpretability
MC Hughes, HM Elibol, T McCoy, R Perlis, F Doshi-Velez
arXiv preprint arXiv:1612.01678, 2016
Ray: A Distributed Framework for Emerging AI Applications. arXiv e-prints, page
P Moritz, R Nishihara, S Wang, A Tumanov, R Liaw, E Liang, M Elibol, ...
arXiv preprint arXiv:1712.05889, 2017
Flexible Primitives for Distributed Deep Learning in Ray
Y Bulatov, R Nishihara, P Moritz, M Elibol, I Stoica, MI Jordan
SysML Conference, 2018
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