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…
Online Markov decision processes with policy iteration
Yao Ma, Hao Zhang, Masashi Sugiyama
The online Markov decision process (MDP) is a generalization of the classical Markov decision process that incorporates changing reward functions. In this paper, we propose practic…
Theoretical and Experimental Analyses of Tensor-Based Regression and Classification
Kishan Wimalawarne, Ryota Tomioka, Masashi Sugiyama
We theoretically and experimentally investigate tensor-based regression and classification. Our focus is regularization with various tensor norms, including the overlapped trace no…
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…
Task Selection for Bandit-Based Task Assignment in Heterogeneous Crowdsourcing
Hao Zhang, Masashi Sugiyama
Task selection (picking an appropriate labeling task) and worker selection (assigning the labeling task to a suitable worker) are two major challenges in task assignment for crowds…