54 citations · 162 across the 19 of their papers we have counts for
11 papers · 1 filter
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…
Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations
Krishnakumar Balasubramanian, Kai Yu, Guy Lebanon
We propose and analyze a novel framework for learning sparse representations, based on two statistical techniques: kernel smoothing and marginal regression. The proposed approach p…
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…
Sequential Document Representations and Simplicial Curves
Guy Lebanon
The popular bag of words assumption represents a document as a histogram of word occurrences. While computationally efficient, such a representation is unable to maintain any seque…
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 Translation, Heat Kernels and Expected Distances
Joshua Dillon, Yi Mao, Guy Lebanon +1
High dimensional structured data such as text and images is often poorly understood and misrepresented in statistical modeling. The standard histogram representation suffers from h…