5 citations · 9 across the 6 of their papers we have counts for
6 papers
Scardina: Scalable Join Cardinality Estimation by Multiple Density Estimators
Ryuichi Ito, Yuya Sasaki, Chuan Xiao +1
In recent years, machine learning-based cardinality estimation methods are replacing traditional methods. This change is expected to contribute to one of the most important applica…
NoSQL Schema Design for Time-Dependent Workloads
Yusuke Wakuta, Michael Mior, Teruyoshi Zenmyo +2
In this paper, we propose a schema optimization method for time-dependent workloads for NoSQL databases. In our proposed method, we migrate schema according to changing workloads,…
On Using The Two-Way Cluster-Robust Standard Errors
Harold D Chiang, Yuya Sasaki
Thousands of papers have reported two-way cluster-robust (TWCR) standard errors. However, the recent econometrics literature points out the potential non-gaussianity of two-way clu…
GNN Transformation Framework for Improving Efficiency and Scalability
Seiji Maekawa, Yuya Sasaki, George Fletcher +1
We propose a framework that automatically transforms non-scalable GNNs into precomputation-based GNNs which are efficient and scalable for large-scale graphs. The advantages of our…
Scaling Private Deep Learning with Low-Rank and Sparse Gradients
Ryuichi Ito, Seng Pei Liew, Tsubasa Takahashi +2
Applying Differentially Private Stochastic Gradient Descent (DPSGD) to training modern, large-scale neural networks such as transformer-based models is a challenging task, as the m…
An Empirical Study of Personalized Federated Learning
Koji Matsuda, Yuya Sasaki, Chuan Xiao +1
Federated learning is a distributed machine learning approach in which a single server and multiple clients collaboratively build machine learning models without sharing datasets o…