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Online Learning in The Manifold of Low-Rank Matrices.

U. Shalit, D. Weinshall, and G. Chechik. NIPS, page 2128-2136. Curran Associates, Inc., (2010)

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An Online Algorithm for Large Scale Image Similarity Learning.G. Chechik, U. Shalit, V. Sharma, and S. Bengio. NIPS, page 306-314. Curran Associates, Inc., (2009)Large Scale Online Learning of Image Similarity through Ranking.G. Chechik, V. Sharma, U. Shalit, and S. Bengio. IbPRIA, volume 5524 of Lecture Notes in Computer Science, page 11-14. Springer, (2009)Learning Sparse Metrics, One Feature at a Time.Y. Atzmon, U. Shalit, and G. Chechik. FE@NIPS, volume 44 of JMLR Workshop and Conference Proceedings, page 30-48. JMLR.org, (2015)Online Learning in The Manifold of Low-Rank Matrices.U. Shalit, D. Weinshall, and G. Chechik. NIPS, page 2128-2136. Curran Associates, Inc., (2010)FuncISH: learning a functional representation of neural ISH images.N. Liscovitch, U. Shalit, and G. Chechik. Bioinformatics, 29 (13): 36-43 (2013)Building Causal Graphs from Medical Literature and Electronic Medical Records.G. Nordon, G. Koren, V. Shalev, B. Kimelfeld, U. Shalit, and K. Radinsky. AAAI, page 1102-1109. AAAI Press, (2019)Causal Effect Inference with Deep Latent-Variable Models.C. Louizos, U. Shalit, J. Mooij, D. Sontag, R. Zemel, and M. Welling. NIPS, page 6446-6456. (2017)Learning Representations for Counterfactual Inference.F. Johansson, U. Shalit, and D. Sontag. ICML, volume 48 of JMLR Workshop and Conference Proceedings, page 3020-3029. JMLR.org, (2016)Off-Policy Evaluation in Partially Observable Environments.G. Tennenholtz, S. Mannor, and U. Shalit. CoRR, (2019)Explaining Classifiers with Causal Concept Effect (CaCE).Y. Goyal, U. Shalit, and B. Kim. CoRR, (2019)

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Online Learning in The Manifold of Low-Rank Matrices.

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Disambiguation of "Shalit, Uri"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Online Learning in The Manifold of Low-Rank Matrices.

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Online Learning in The Manifold of Low-Rank Matrices.