Masashi Sugiyama

About

Masashi Sugiyama is a prominent machine learning researcher whose work spans statistical learning theory, density estimation, and reinforcement learning. He is perhaps best known for his foundational contributions to **covariate shift adaptation**, the challenge of learning effectively when training and test data distributions differ — a pervasive problem in real-world machine learning deployments. His influential 2012 book *Machine Learning in Non-Stationary Environments*, which has garnered over 470 citations across its editions, established him as a leading authority on this critical yet often overlooked problem. Beyond distribution shift, Sugiyama has made significant contributions to **conditional density estimation**, developing elegant least-squares frameworks that go beyond simple regression to capture the full richness of conditional distributions, including multimodality and heteroscedastic noise. His work in **reinforcement learning** is equally impressive, addressing challenges in policy gradient methods, sample efficiency, and multi-agent systems, including investigations into overestimation bias and diversity-seeking exploration strategies. Sugiyama's research consistently bridges rigorous mathematical theory with practical algorithmic innovation, making his work valuable to both theorists and practitioners. His sustained output across statistical machine learning and sequential decision-making has cemented his reputation as one of Japan's most influential machine learning scientists.

Research Focus

Key Achievements

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

Contact & Links