41 citations · 85 across the 9 of their papers we have counts for
9 papers
Distributed Bayesian Piecewise Sparse Linear Models
Masato Asahara, Ryohei Fujimaki
The importance of interpretability of machine learning models has been increasing due to emerging enterprise predictive analytics, threat of data privacy, accountability of artific…
An Interactive Greedy Approach to Group Sparsity in High Dimensions
Wei Qian, Wending Li, Yasuhiro Sogawa +3
Sparsity learning with known grouping structure has received considerable attention due to wide modern applications in high-dimensional data analysis. Although advantages of using…
On The Projection Operator to A Three-view Cardinality Constrained Set
Haichuan Yang, Shupeng Gui, Chuyang Ke +3
The cardinality constraint is an intrinsic way to restrict the solution structure in many domains, for example, sparse learning, feature selection, and compressed sensing. To solve…
Optimization Beyond Prediction: Prescriptive Price Optimization
Shinji Ito, Ryohei Fujimaki
This paper addresses a novel data science problem, prescriptive price optimization, which derives the optimal price strategy to maximize future profit/revenue on the basis of massi…
Factorized Asymptotic Bayesian Inference for Factorial Hidden Markov Models
Shaohua Li, Ryohei Fujimaki, Chunyan Miao
Factorial hidden Markov models (FHMMs) are powerful tools of modeling sequential data. Learning FHMMs yields a challenging simultaneous model selection issue, i.e., selecting the n…
Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood
Kohei Hayashi, Shin-ichi Maeda, Ryohei Fujimaki
Factorized information criterion (FIC) is a recently developed approximation technique for the marginal log-likelihood, which provides an automatic model selection framework for a…
Partition-wise Linear Models
Hidekazu Oiwa, Ryohei Fujimaki
Region-specific linear models are widely used in practical applications because of their non-linear but highly interpretable model representations. One of the key challenges in the…
Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraint
Ji Liu, Ryohei Fujimaki, Jieping Ye
We consider forward-backward greedy algorithms for solving sparse feature selection problems with general convex smooth functions. A state-of-the-art greedy method, the Forward-Bac…
Factorized Asymptotic Bayesian Hidden Markov Models
Ryohei Fujimaki, Kohei Hayashi
This paper addresses the issue of model selection for hidden Markov models (HMMs). We generalize factorized asymptotic Bayesian inference (FAB), which has been recently developed f…