Masashi Sugiyama

Japan Science and Technology Agency, Tokyo Institute of Technology, RIKEN Center for Advanced Intelligence Project, The University of Tokyo, RIKEN, Budapest University of Technology and Economics

Papers

29

Total Citations

930

H-Index

13

About

No biography available yet.

Research Focus

Computer science29 · 930 citations
Artificial intelligence28 · 886 citations
Mathematics23 · 692 citations
Machine learning16 · 552 citations
Active learning (machine learning)3 · 290 citations
Covariate2 · 274 citations
Reinforcement learning16 · 272 citations
Online machine learning1 · 268 citations
Identification (biology)1 · 268 citations
Field (mathematics)1 · 268 citations
Psychology2 · 227 citations
Function (biology)10 · 197 citations

Key Achievements

13
H-Index
29
Papers
930
Total Citations
32
Avg Citations/Paper
🏆 Most Cited Paper
Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation
268 citations · 2012
📈 Most Prolific Year: 2010 (4 Papers)
🤝 Key Collaborators: 38
🏛 Institutions: Japan Science and Technology Agency, Tokyo Institute of Technology, RIKEN Center for Advanced Intelligence Project, The University of Tokyo, RIKEN, Budapest University of Technology and Economics

Top Papers

  1. 1
    Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation
    268 citations · 2012
  2. 2
    Machine Learning in Non-Stationary Environments
    208 citations · 2012
  3. 3
    Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics
    70 citations · 2019
  4. 4
    Least-Squares Conditional Density Estimation
    63 citations · 2010
  5. 5
    Conditional Density Estimation via Least-Squares Density Ratio Estimation
    44 citations · 2010
  6. 6
    Geodesic Gaussian kernels for value function approximation
    36 citations · 2008
  7. 7
    Efficient Sample Reuse in Policy Gradients with Parameter-Based Exploration
    27 citations · 2013
  8. 8
    Discovering diverse solutions in deep reinforcement learning by maximizing state–action-based mutual information
    21 citations · 2022
  9. 9
    Efficient exploration through active learning for value function approximation in reinforcement learning
    20 citations · 2010
  10. 10
    Reward-Weighted Regression with Sample Reuse for Direct Policy Search in Reinforcement Learning
    19 citations · 2011

Key Collaborators

HHHirotaka Hachiya11 shared papersVTVoot Tangkaratt8 shared papersJMJun Morimoto4 shared papersTOTakayuki Osa4 shared papersTMTetsuro Morimura2 shared papersTKToshimitsu Kaneko2 shared papersTWThijs Wensveen2 shared papersTKTakafumi Kanamori2 shared papersNSNorikazu Sugimoto2 shared papersMTMasahiro Tomono2 shared papersJPJan Peters2 shared papersITIchiro Takeuchi2 shared papersMKMotoaki Kawanabe2 shared papersCTChristopher Towell2 shared papersHKHisashi Kashima2 shared papersSVSethu Vijayakumar2 shared papersTSTaiji Suzuki2 shared papersTATakayuki Akiyama2 shared papersKIKiyoshi IRIE2 shared papersTZTingting Zhao2 shared papers
Not generated yet