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01/15/2013
Matrix Approximation under Local Low-Rank Assumption
Matrix approximation is a common tool in machine learning for building a...
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10/19/2012
Learning Riemannian Metrics
We propose a solution to the problem of estimating a Riemannian metric a...
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10/03/2012
Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations
We propose and analyze a novel framework for learning sparse representat...
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07/11/2012
An Extended Cencov-Campbell Characterization of Conditional Information Geometry
We formulate and prove an axiomatic characterization of conditional info...
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06/27/2012
The Landmark Selection Method for Multiple Output Prediction
Conditional modeling x → y is a central problem in machine learning. A s...
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06/20/2012
Statistical Translation, Heat Kernels and Expected Distances
High dimensional structured data such as text and images is often poorly...
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06/18/2012
A Linear Approximation to the chi^2 Kernel with Geometric Convergence
We propose a new analytical approximation to the χ^2 kernel that converg...
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05/14/2012
A Comparative Study of Collaborative Filtering Algorithms
Collaborative filtering is a rapidly advancing research area. Every year...
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05/09/2012
Domain Knowledge Uncertainty and Probabilistic Parameter Constraints
Incorporating domain knowledge into the modeling process is an effective...
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03/06/2010
