10 citations · 11 across the 3 of their papers we have counts for
10 papers · 1 filter
Fast Tucker Rank Reduction for Non-Negative Tensors Using Mean-Field Approximation
Kazu Ghalamkari, Mahito Sugiyama
We present an efficient low-rank approximation algorithm for non-negative tensors. The algorithm is derived from our two findings: First, we show that rank-1 approximation for tens…
Additive Poisson Process: Learning Intensity of Higher-Order Interaction in Stochastic Processes
Simon Luo, Feng Zhou, Lamiae Azizi +1
We present the Additive Poisson Process (APP), a novel framework that can model the higher-order interaction effects of the intensity functions in stochastic processes using lower…
Fast Rank Reduction for Non-negative Matrices via Mean Field Theory
Kazu Ghalamkari, Mahito Sugiyama
We propose an efficient matrix rank reduction method for non-negative matrices, whose time complexity is quadratic in the number of rows or columns of a matrix. Our key insight is…
Double Descent Risk and Volume Saturation Effects: A Geometric Perspective
Prasad Cheema, Mahito Sugiyama
The appearance of the double-descent risk phenomenon has received growing interest in the machine learning and statistics community, as it challenges well-understood notions behind…
Bias-Variance Trade-Off in Hierarchical Probabilistic Models Using Higher-Order Feature Interactions
Simon Luo, Mahito Sugiyama
Hierarchical probabilistic models are able to use a large number of parameters to create a model with a high representation power. However, it is well known that increasing the num…
Transductive Boltzmann Machines
Mahito Sugiyama, Koji Tsuda, Hiroyuki Nakahara
We present transductive Boltzmann machines (TBMs), which firstly achieve transductive learning of the Gibbs distribution. While exact learning of the Gibbs distribution is impossib…