Biblio
Filters: Author is Prakash Ishwar [Clear All Filters]
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2007. One-bit Distributed Sensing and Coding for Field Estimation in Sensor Networks. CoRR. abs/cs/0701196
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2008. One-Bit Distributed Sensing and Coding for Field Estimation in Sensor Networks. {IEEE} Trans. Signal Processing. 56:4433–4445.
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2011. Sensing-aware classification with high-dimensional data. Proceedings of the {IEEE} International Conference on Acoustics, Speech, and Signal Processing, {ICASSP} 2011, May 22-27, 2011, Prague Congress Center, Prague, Czech Republic. :3700–3703.
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2011. Sensing structure in learning-based binary classification of high-dimensional data. 49th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2011, Allerton Park {&} Retreat Center, Monticello, IL, USA, 28-30 September, 2011. :1521–1528.
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2012. Sensing aware dimensionality reduction for nearest neighbor classification of high dimensional signals. {IEEE} Statistical Signal Processing Workshop, {SSP} 2012, Ann Arbor, MI, USA, August 5-8, 2012. :405–408.
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2013. Sensing-Aware Kernel SVM. CoRR. abs/1312.0512
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2013. Necessary and Sufficient Conditions for Novel Word Detection in Separable Topic Models. CoRR. abs/1310.7994
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2013. An impossibility result for high dimensional supervised learning. 2013 {IEEE} Information Theory Workshop, {ITW} 2013, Sevilla, Spain, September 9-13, 2013. :1–5.
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2013. Topic Discovery through Data Dependent and Random Projections. Proceedings of the 30th International Conference on Machine Learning, {ICML} 2013, Atlanta, GA, USA, 16-21 June 2013. 28:1202–1210.
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2013. A new geometric approach to latent topic modeling and discovery. {IEEE} International Conference on Acoustics, Speech and Signal Processing, {ICASSP} 2013, Vancouver, BC, Canada, May 26-31, 2013. :5568–5572.
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2013. Dynamic topic discovery through sequential projections. 2013 Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, November 3-6, 2013. :1100–1104.
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2014. A Topic Modeling Approach to Ranking. CoRR. abs/1412.3705
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2014. Sensing-aware kernel SVM. {IEEE} International Conference on Acoustics, Speech and Signal Processing, {ICASSP} 2014, Florence, Italy, May 4-9, 2014. :2947–2951.
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2014. Efficient Distributed Topic Modeling with Provable Guarantees. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, {AISTATS} 2014, Reykjavik, Iceland, April 22-25, 2014. 33:167–175.
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2015. Necessary and Sufficient Conditions and a Provably Efficient Algorithm for Separable Topic Discovery. CoRR. abs/1508.05565
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2015. Learning Mixed Membership Mallows Models from Pairwise Comparisons. CoRR. abs/1504.00757
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2015. Most large topic models are approximately separable. 2015 Information Theory and Applications Workshop, {ITA} 2015, San Diego, CA, USA, February 1-6, 2015. :199–203.
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2015. Learning shared rankings from mixtures of noisy pairwise comparisons. 2015 {IEEE} International Conference on Acoustics, Speech and Signal Processing, {ICASSP} 2015, South Brisbane, Queensland, Australia, April 19-24, 2015. :5446–5450.
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2015. A Topic Modeling Approach to Ranking. Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, {AISTATS} 2015, San Diego, California, USA, May 9-12, 2015. 38
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2016. A Provably Efficient Algorithm for Separable Topic Discovery. J. Sel. Topics Signal Processing. 10:712–725.