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AAAI
2012
7 years 6 months ago
Towards Discovering What Patterns Trigger What Labels
In many real applications, especially those involving data objects with complicated semantics, it is generally desirable to discover the relation between patterns in the input spa...
Yu-Feng Li, Ju-Hua Hu, Yuang Jiang, Zhi-Hua Zhou
IJCV
2012
7 years 6 months ago
Sparse Occlusion Detection with Optical Flow
Abstract We tackle the problem of detecting occluded regions in a video stream. Under assumptions of Lambertian reflection and static illumination, the task can be posed as a vari...
Alper Ayvaci, Michalis Raptis, Stefano Soatto
CVPR
2012
IEEE
7 years 6 months ago
See all by looking at a few: Sparse modeling for finding representative objects
We consider the problem of finding a few representatives for a dataset, i.e., a subset of data points that efficiently describes the entire dataset. We assume that each data poi...
Ehsan Elhamifar, Guillermo Sapiro, René Vid...
JMLR
2012
7 years 6 months ago
Marginal Regression For Multitask Learning
Variable selection is an important and practical problem that arises in analysis of many high-dimensional datasets. Convex optimization procedures that arise from relaxing the NP-...
Mladen Kolar, Han Liu
JMLR
2012
7 years 6 months ago
Beyond Logarithmic Bounds in Online Learning
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...
Francesco Orabona, Nicolò Cesa-Bianchi, Cla...
CORR
2012
Springer
214views Education» more  CORR 2012»
7 years 11 months ago
Stochastic Low-Rank Kernel Learning for Regression
We present a novel approach to learn a kernelbased regression function. It is based on the use of conical combinations of data-based parameterized kernels and on a new stochastic ...
Pierre Machart, Thomas Peel, Liva Ralaivola, Sandr...
SIAMJO
2011
8 years 6 months ago
Rank-Sparsity Incoherence for Matrix Decomposition
Suppose we are given a matrix that is formed by adding an unknown sparse matrix to an unknown low-rank matrix. Our goal is to decompose the given matrix into its sparse and low-ran...
Venkat Chandrasekaran, Sujay Sanghavi, Pablo A. Pa...
ICASSP
2011
IEEE
8 years 7 months ago
Proximal splitting methods for depth estimation
Stereo matching is an active area of research in image processing. In a recent work, a convex programming approach was developed in order to generate a dense disparity field. In ...
Mireille El Gheche, Jean-Christophe Pesquet, Jouma...
SIAMJO
2010
130views more  SIAMJO 2010»
8 years 10 months ago
A Randomized Cutting Plane Method with Probabilistic Geometric Convergence
Abstract. We propose a randomized method for general convex optimization problems; namely, the minimization of a linear function over a convex body. The idea is to generate N rando...
Fabrizio Dabbene, P. S. Shcherbakov, Boris T. Poly...
COLT
2010
Springer
9 years 1 months ago
Composite Objective Mirror Descent
We present a new method for regularized convex optimization and analyze it under both online and stochastic optimization settings. In addition to unifying previously known firstor...
John Duchi, Shai Shalev-Shwartz, Yoram Singer, Amb...
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