6 citations · 26 across the 8 of their papers we have counts for
9 papers
MDL Meets Latent Confounders: LNML-based Causal Discovery
Zhongyi Que, Shin Matsushima, Kenji Yamanishi
Causal discovery with nonlinear mechanisms and latent confounders remains challenging. Existing methods often rely on either linear assumptions or causal sufficiency, limiting thei…
Detection of Unobserved Common Causes based on NML Code in Discrete, Mixed, and Continuous Variables
Masatoshi Kobayashi, Kohei Miyagichi, Shin Matsushima
Causal discovery in the presence of unobserved common causes from observational data only is a crucial but challenging problem. We categorize all possible causal relationships betw…
mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations
Toyotaro Suzumura, Akiyoshi Sugiki, Hiroyuki Takizawa +30
The growing amount of data and advances in data science have created a need for a new kind of cloud platform that provides users with flexibility, strong security, and the ability…
Statistical Learnability of Generalized Additive Models based on Total Variation Regularization
Shin Matsushima
A generalized additive model (GAM, Hastie and Tibshirani (1987)) is a nonparametric model by the sum of univariate functions with respect to each explanatory variable, i.e., $f({\m…
Grafting for Combinatorial Boolean Model using Frequent Itemset Mining
Taito Lee, Shin Matsushima, Kenji Yamanishi
This paper introduces the combinatorial Boolean model (CBM), which is defined as the class of linear combinations of conjunctions of Boolean attributes. This paper addresses the is…
DS-MLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic Regression
Parameswaran Raman, Sriram Srinivasan, Shin Matsushima +3
Scaling multinomial logistic regression to datasets with very large number of data points and classes is challenging. This is primarily because one needs to compute the log-partiti…
WordRank: Learning Word Embeddings via Robust Ranking
Shihao Ji, Hyokun Yun, Pinar Yanardag +2
Embedding words in a vector space has gained a lot of attention in recent years. While state-of-the-art methods provide efficient computation of word similarities via a low-dimensi…
Totally Corrective Boosting with Cardinality Penalization
Vasil S. Denchev, Nan Ding, Shin Matsushima +2
We propose a totally corrective boosting algorithm with explicit cardinality regularization. The resulting combinatorial optimization problems are not known to be efficiently solva…
Distributed Stochastic Optimization of the Regularized Risk
Shin Matsushima, Hyokun Yun, Xinhua Zhang +1
Many machine learning algorithms minimize a regularized risk, and stochastic optimization is widely used for this task. When working with massive data, it is desirable to perform s…