JMLR
2012
11 years 7 months ago
2012
Often, high dimensional data lie close to a low-dimensional submanifold and it is of interest to understand the geometry of these submanifolds. The homology groups of a manifold a...
JMLR
2012
11 years 7 months ago
2012
The development of accurate models and efficient algorithms for the analysis of multivariate categorical data are important and longstanding problems in machine learning and compu...
JMLR
2012
11 years 7 months ago
2012
We prove logarithmic regret bounds that depend on the loss L∗ T of the competitor rather than on the number T of time steps. In the general online convex optimization setting, o...
JMLR
2012
11 years 7 months ago
2012
“Energy” models for continuous domains can be applied to many problems, but often suffer from high computational expense in training, due to the need to repeatedly minimize t...
JMLR
2012
11 years 7 months ago
2012
We present an algorithm for fast posterior inference in penalized high-dimensional state-space models, suitable in the case where a few measurements are taken in each time step. W...
JMLR
2012
11 years 7 months ago
2012
Temporal Clustering (TC) refers to the factorization of multiple time series into a set of non-overlapping segments that belong to k temporal clusters. Existing methods based on e...
JMLR
2012
11 years 7 months ago
2012
The problem of curve registration appears in many different areas of applications ranging from neuroscience to road traffic modeling. In the present work, we propose a nonparamet...
JMLR
2012
11 years 7 months ago
2012
We consider the task of devising large-margin based surrogate losses for the learning to rank problem. In this learning to rank setting, the traditional hinge loss for structured ...
JMLR
2012
11 years 7 months ago
2012
Traditional tensor decompositions such as the CANDECOMP / PARAFAC (CP) and Tucker decompositions yield higher-order principal components that have been used to understand tensor d...
JMLR
2012
11 years 7 months ago
2012
Reducing the number of labeled examples required to learn accurate prediction models is an important problem in structured output prediction. In this paper we propose a new transd...