454 citations · 5.5k across the 247 of their papers we have counts for
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Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions
Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin +2
Several recent works have explored stochastic gradient methods for variational inference that exploit the geometry of the variational-parameter space. However, the theoretical prop…
Direct Estimation of the Derivative of Quadratic Mutual Information with Application in Supervised Dimension Reduction
Voot Tangkaratt, Hiroaki Sasaki, Masashi Sugiyama
A typical goal of supervised dimension reduction is to find a low-dimensional subspace of the input space such that the projected input variables preserve maximal information about…
Regularized Multi-Task Learning for Multi-Dimensional Log-Density Gradient Estimation
Ikko Yamane, Hiroaki Sasaki, Masashi Sugiyama
Log-density gradient estimation is a fundamental statistical problem and possesses various practical applications such as clustering and measuring non-Gaussianity. A naive two-step…
Homotopy Continuation Approaches for Robust SV Classification and Regression
Shinya Suzumura, Kohei Ogawa, Masashi Sugiyama +2
In support vector machine (SVM) applications with unreliable data that contains a portion of outliers, non-robustness of SVMs often causes considerable performance deterioration. A…
Structure Learning of Partitioned Markov Networks
Song Liu, Taiji Suzuki, Masashi Sugiyama +1
We learn the structure of a Markov Network between two groups of random variables from joint observations. Since modelling and learning the full MN structure may be hard, learning…