Dr. Nicholas Ruozzi
Associate Professor
Degrees:
- Ph.D., Computer Science, Yale University, 2011
- M.S., Computer Science, Yale University, 2010
- B.S., Computer Science, Cornell University, 2006
Research Interests:
- Graphical Models
- Machine Learning
- Approximate Inference and Learning
Representative Publications:
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N. Ruozzi and S. Tatikonda. Message-passing algorithms for quadratic minimization. Journal of Machine Learning Research, 14:2287-2314, 2013.
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N. Ruozzi and S. Tatikonda. Message-passing algorithms: Reparameterizations and splittings. IEEE Transactions on Information Theory, 59(9):5860-5881, Sept. 2013.
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N. Ruozzi and T. Jebara. Making pairwise binary graphical models attractive. In Advances in Neural Information Processing Systems (NIPS), Montreal, Canada, 2014.
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N. Ruozzi. Beyond log-supermodularity: lower bounds and the Bethe partition function. In Uncertainty in Artificial Intelligence (UAI), Bellevue, WA, USA, July 2013.
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N. Ruozzi. The Bethe partition function of log-supermodular graphical models. In Advances in Neural Information Processing Systems (NIPS), Lake Tahoe, NV, Dec. 2012.
