toshihiro-kamishima documents

Fairness-aware Learning through Regularization Approach
Technology Fairness-aware Learning through Regularization Approach
Correcting Popularity Bias by Enhancing Recommendation Neutrality
Technology Correcting Popularity Bias by Enhancing Recommendation Neutrality
Fairness-aware Classifier with Prejudice Remover Regularizer
Technology Fairness-aware Classifier with Prejudice Remover Regularizer
Consideration on Fairness-aware Data Mining
Technology Consideration on Fairness-aware Data Mining
Efficiency Improvement of Neutrality-Enhanced Recommendation
Technology Efficiency Improvement of Neutrality-Enhanced Recommendation
Future Directions of Fairness-Aware Data Mining: Recommendation, Causality, and Theoretical Aspects
Technology Future Directions of Fairness-Aware Data Mining: Recommendation, Causality, and Theoretical Aspects
Model-based Approaches for Independence-Enhanced Recommendation
Data & Analytics Model-based Approaches for Independence-Enhanced Recommendation
WSDM2016勉強会 資料
Data & Analytics WSDM2016勉強会 資料
KDD2016勉強会 資料
Data & Analytics KDD2016勉強会 資料
科学技術計算関連Pythonパッケージの概要
Data & Analytics 科学技術計算関連Pythonパッケージの概要
ICML2015読み会 資料
Data & Analytics ICML2015読み会 資料
RecSys2016勉強会 資料
Data & Analytics RecSys2016勉強会 資料
科学技術計算関連Pythonパッケージの概要
Data & Analytics 科学技術計算関連Pythonパッケージの概要