6 citations · 26 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
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…