Biblio
Filters: Author is Weicong Ding [Clear All Filters]
A Provably Efficient Algorithm for Separable Topic Discovery. J. Sel. Topics Signal Processing. 10:712–725.
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2016.
Learning Mixed Membership Mallows Models from Pairwise Comparisons. CoRR. abs/1504.00757
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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.
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.
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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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2015.
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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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.
A Topic Modeling Approach to Ranking. CoRR. abs/1412.3705
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2014.
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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2013.
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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.
Sensing-Aware Kernel SVM. CoRR. abs/1312.0512
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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.