9 citations · 25 across the 8 of their papers we have counts for
7 papers · 1 filter
Matrix Approximation under Local Low-Rank Assumption
Joonseok Lee, Seungyeon Kim, Guy Lebanon +1
Matrix approximation is a common tool in machine learning for building accurate prediction models for recommendation systems, text mining, and computer vision. A prevalent assumpti…
Learning Riemannian Metrics
Guy Lebanon
We propose a solution to the problem of estimating a Riemannian metric associated with a given differentiable manifold. The metric learning problem is based on minimizing the relat…
An Extended Cencov-Campbell Characterization of Conditional Information Geometry
Guy Lebanon
We formulate and prove an axiomatic characterization of conditional information geometry, for both the normalized and the nonnormalized cases. This characterization extends the axi…
The Landmark Selection Method for Multiple Output Prediction
Krishnakumar Balasubramanian, Guy Lebanon
Conditional modeling x \to y is a central problem in machine learning. A substantial research effort is devoted to such modeling when x is high dimensional. We consider, instead, t…
Statistical and Computational Tradeoffs in Stochastic Composite Likelihood
Joshua V Dillon, Guy Lebanon
Maximum likelihood estimators are often of limited practical use due to the intensive computation they require. We propose a family of alternative estimators that maximize a stocha…
Unsupervised Supervised Learning II: Training Margin Based Classifiers without Labels
Krishnakumar Balasubramanian, Pinar Donmez, Guy Lebanon
Many popular linear classifiers, such as logistic regression, boosting, or SVM, are trained by optimizing a margin-based risk function. Traditionally, these risk functions are comp…