2007 - [?]. PhD in Computer Science, UCSD. Advisor: Sanjoy Dasgupta.
2004 - 2007. BS in Computer Science & Discrete Math, CMU.
2001 - 2003. Diploma in Violin Performance, Juilliard.
Boosting with the Logistic Loss is Consistent.
[arXiv]
- COLT 2013.
- Optimization, generalization, and consistency guarantees for AdaBoost with
logistic and similar losses.
Margins, Shrinkage, and Boosting.
[arXiv]
- ICML 2013.
- AdaBoost, with a variety of losses, attains optimal margins
merely by multiplying the step size with a small constant.
Agglomerative Bregman Clustering. (With Sanjoy Dasgupta.)
[pdf]
[short video]
- ICML 2012.
- Provides the natural algorithm,
with attention to: handling degenerate clusters via smoothing,
Bregman divergences for nondifferentiable convex functions,
Exponential Families without minimality assumptions.
A Primal-Dual Convergence Analysis of Boosting.
[arXiv]
[jmlr]
- JMLR 13:561-606, 2012.
- This is the extended version of the NIPS paper "The Fast Convergence of Boosting".
Steepest Descent Analysis for Unregularized Linear
Prediction with Strictly Convex Penalties.
[pdf]
[video]
- NIPS Optimization Workshop 2011.
- Adaptation of some of the boosting techniques to other optimization problems,
for instance gradient descent of positive semi-definite quadratics.
The Fast Convergence of Boosting.
[pdf]
- NIPS 2011.
- AdaBoost, with a variety of losses, minimizes its empirical risk
at rate \(\mathcal O(\ln(1/\epsilon))\) when either weak learnable or possessing a
minimizer, and rate \(\mathcal O(1/\epsilon)\) in general.
Hartigan's Method: \(k\)-means without Voronoi. (With Andrea Vattani.)
[pdf]
[old javscript demo]
- AISTATS 2010.
- Hartigan's method minimizes \(k\)-means cost point by point; it terminates when
points lie within regions defined by intersections of spheres (rather than just
Voronoi cells).
Signal decomposition using multiscale admixture models. (With John Lafferty.)