Author name cluster

Choon Teo

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

NeurIPS Conference 2008 Conference Paper

Tighter Bounds for Structured Estimation

Olivier Chapelle
Chuong B.
Choon Teo
Quoc Le
Alex Smola

Large-margin structured estimation methods work by minimizing a convex upper bound of loss functions. While they allow for efficient optimization algorithms, these convex formulations are not tight and sacrifice the ability to accurately model the true loss. We present tighter non-convex bounds based on generalizing the notion of a ramp loss from binary classification to structured estimation. We show that a small modification of existing optimization algorithms suffices to solve this modified problem. On structured prediction tasks such as protein sequence alignment and web page ranking, our algorithm leads to improved accuracy.

PDF Details

NeurIPS Conference 2007 Conference Paper

A Kernel Statistical Test of Independence

Arthur Gretton
Kenji Fukumizu
Choon Teo
Le Song
Bernhard Schölkopf
Alex Smola

Although kernel measures of independence have been widely applied in machine learning (notably in kernel ICA), there is as yet no method to determine whether they have detected statistically signiﬁcant dependence. We provide a novel test of the independence hypothesis for one particular kernel independence measure, the Hilbert-Schmidt independence criterion (HSIC). The resulting test costs O(m2), where m is the sample size. We demonstrate that this test outperforms established contingency table and functional correlation-based tests, and that this advantage is greater for multivariate data. Finally, we show the HSIC test also applies to text (and to structured data more generally), for which no other independence test presently exists.

PDF Details

NeurIPS Conference 2007 Conference Paper

Convex Learning with Invariances

Choon Teo
Amir Globerson
Sam Roweis
Alex Smola

Incorporating invariances into a learning algorithm is a common problem in ma- chine learning. We provide a convex formulation which can deal with arbitrary loss functions and arbitrary losses. In addition, it is a drop-in replacement for most optimization algorithms for kernels, including solvers of the SVMStruct family. The advantage of our setting is that it relies on column generation instead of mod- ifying the underlying optimization problem directly.

PDF Details