Biblio
Filters: Author is Cem Aksoylar [Clear All Filters]
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2012. Sample complexity of salient feature identification for sparse signal processing. {IEEE} Statistical Signal Processing Workshop, {SSP} 2012, Ann Arbor, MI, USA, August 5-8, 2012. :329–332.
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2013. Sparse signal processing with linear and non-linear observations: A unified shannon theoretic approach. 2013 {IEEE} Information Theory Workshop, {ITW} 2013, Sevilla, Spain, September 9-13, 2013. :1–5.
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2013. Compressive sensing bounds through a unifying framework for sparse models. {IEEE} International Conference on Acoustics, Speech and Signal Processing, {ICASSP} 2013, Vancouver, BC, Canada, May 26-31, 2013. :5524–5528.
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2014. Sparse Recovery with Linear and Nonlinear Observations: Dependent and Noisy Data. CoRR. abs/1403.3109
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2014. Information-theoretic bounds for adaptive sparse recovery. 2014 {IEEE} International Symposium on Information Theory, Honolulu, HI, USA, June 29 - July 4, 2014. :1311–1315.
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2014. Information-Theoretic Characterization of Sparse Recovery. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, {AISTATS} 2014, Reykjavik, Iceland, April 22-25, 2014. 33:38–46.
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2015. Correction to "Boolean Compressed Sensing and Noisy Group Testing". {IEEE} Trans. Information Theory. 61:1507.
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2016. Clustering and Community Detection with Imbalanced Clusters. CoRR. abs/1608.07605
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2017. Clustering and Community Detection With Imbalanced Clusters. {IEEE} Trans. Signal and Information Processing over Networks. 3:61–76.
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2017. Connected Subgraph Detection with Mirror Descent on SDPs. {ICML}. 70:51–59.
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2017. Sparse Signal Processing With Linear and Nonlinear Observations: A Unified Shannon-Theoretic Approach. {IEEE} Trans. Information Theory. 63:749–776.