185 citations · 945 across the 83 of their papers we have counts for
6 papers · 1 filter
Stochastic Particle Gradient Descent for Infinite Ensembles
Atsushi Nitanda, Taiji Suzuki
The superior performance of ensemble methods with infinite models are well known. Most of these methods are based on optimization problems in infinite-dimensional spaces with some…
Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables
Masaaki Takada, Taiji Suzuki, Hironori Fujisawa
Sparse regularization such as regularization is a quite powerful and widely used strategy for high dimensional learning problems. The effectiveness of sparse regularizatio…
Fast learning rate of deep learning via a kernel perspective
Taiji Suzuki
We develop a new theoretical framework to analyze the generalization error of deep learning, and derive a new fast learning rate for two representative algorithms: empirical risk m…
Trimmed Density Ratio Estimation
Song Liu, Akiko Takeda, Taiji Suzuki +1
Density ratio estimation is a vital tool in both machine learning and statistical community. However, due to the unbounded nature of density ratio, the estimation procedure can be…
Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization
Tomoya Murata, Taiji Suzuki
In this paper, we develop a new accelerated stochastic gradient method for efficiently solving the convex regularized empirical risk minimization problem in mini-batch settings. Th…
Learning Sparse Structural Changes in High-dimensional Markov Networks: A Review on Methodologies and Theories
Song Liu, Kenji Fukumizu, Taiji Suzuki
Recent years have seen an increasing popularity of learning the sparse \emph{changes} in Markov Networks. Changes in the structure of Markov Networks reflect alternations of intera…