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Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain.

T. Rubin, O. Koyejo, M. Jones, and T. Yarkoni. NIPS, page 1118-1126. (2016)

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Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition.T. Rubin, O. Koyejo, K. Gorgolewski, M. Jones, R. Poldrack, and T. Yarkoni. PLoS Comput. Biol., (2017)Learning to Rank With Bregman Divergences and Monotone Retargeting.S. Acharyya, O. Koyejo, and J. Ghosh. UAI, page 15-25. AUAI Press, (2012)A Simple and Provable Algorithm for Sparse Diagonal CCA.M. Asteris, A. Kyrillidis, O. Koyejo, and R. Poldrack. ICML, volume 48 of JMLR Workshop and Conference Proceedings, page 1148-1157. JMLR.org, (2016)Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems.S. Joshi, O. Koyejo, W. Vijitbenjaronk, B. Kim, and J. Ghosh. CoRR, (2019)Information Projection and Approximate Inference for Structured Sparse Variables.R. Khanna, J. Ghosh, R. Poldrack, and O. Koyejo. CoRR, (2016)Generalized Byzantine-tolerant SGD.C. Xie, O. Koyejo, and I. Gupta. CoRR, (2018)Metric Elicitation; Moving from Theory to Practice.S. Ali, S. Upadhyay, G. Hiranandani, E. Glassman, and O. Koyejo. CoRR, (2022)Target Conditioned Representation Independence (TCRI); From Domain-Invariant to Domain-General Representations.O. Salaudeen, and O. Koyejo. CoRR, (2022)Batch Active Learning from the Perspective of Sparse Approximation.M. Shen, B. Jiang, J. Zhang, and O. Koyejo. CoRR, (2022)Labeling Cost Sensitive Batch Active Learning For Brain Tumor Segmentation.M. Shen, J. Zhang, L. Chen, W. Yan, N. Jani, B. Sutton, and O. Koyejo. ISBI, page 1269-1273. IEEE, (2021)

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Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain.

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Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain.