Estimating and computing density based distance metrics --- Density based distance metrics have been proposed for semi-supervised learning, non-linear interpolation and clustering. In this paper we bound rate of convergence of non-parametric estimates of density based distance metrics. We further give an asymptotically consistent graph based method for computing these metrics and bound the rate at which this approximation error goes to zero. [ICML 2005 paper] [ Semisupervised learning Book chapter - Abstract ps]
Supervised dimensionality reduction using mixture models --- A supervised, linear dimensionality reduction method based on maximum mutual information estimation of low dimensional exponential family mixture models. This is an adaptation to the supervised case of the technique used in the unsupervised scheme 'Semi-parametric exponential family PCA'. [ ICML 2005 paper - pdf] [ Technical Report - No. CS2004-0810, December 2004, UCSD Abstract ps pdf ]
Semi-parametric exponential family PCA --- A linear, probabilistic dimensionality reduction scheme based on latent variable modeling with non-parametric latent distribution estimation. [ NIPS 2004 paper - Abstract pdf ps bibtex ] [ Technical Report No. CS2004-0790, June 2004, UCSD]
Practical algorithms for modeling sparse data --- A method for estimating the distribution underlying sparse data. Instead of maximum likelihood, we consider a different estimate that maximizes the probability of the number of symbols appearing any given number of times. [ Proceedings of IEEE Symposium on Information Theory, 2004 paper - ps ] [ Proceedings of Allerton Conference, 2003 - ps ]
Independent tree adhoc multicast routing --- A technique for using alternate trees to improve the time between failure of a multicast service. [ Mobile Networks and Applications, Volume 8 , Issue 5, October 2003 - pdf ] [ Vehicular Technology Conference, 2001 - pdf ]
Z. J. Haas, J. Deng, B. Liang, P. Papadimitratos, and S. Sajama, “Wireless Ad Hoc Networks,” in Wiley Encyclopedia of Telecommunications, John G. Proakis, Editor, John Wiley & Sons, New York, 2002. [ pdf ]