Biases in Evaluation of Molecular Optimization Methods and Bias Reduction Strategies
- 2023
- ICML 2023
Bio
Kohei Miyaguchi is a researcher at IBM Research - Tokyo. He is mainly working on machine learning. For more details including his publications, please refer to the external website.
Research Interests
- Learning theory and theory-inspired algorithms
- Reinforcement learning; offline policy evaluation
Publications
Detection of Unobserved Common Cause in Discrete Data Based on the MDL Principle
- Masatoshi Kobayashi
- Kohei Miyaguchi
- et al.
- 2022
- Big Data 2022
Hierarchical Lattice Layer for Partially Monotone Neural Networks
- 2022
- NeurIPS 2022
Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Prediction
- 2022
- IJCAI 2022
Variational Inference for Discriminative Learning with Generative Modeling of Feature Incompletion
- 2022
- ICLR 2022
Almost Hyperparameter-Free Hyperparameter Selection Framework for Offline Policy Evaluation
- 2022
- AAAI 2022
Asymptotically Exact Error Characterization of Offline Policy Evaluation with Misspecified Linear Models
- 2021
- NeurIPS 2021
Adaptive minimax regret against smooth logarithmic losses over high-dimensional L1-balls via envelope complexity
- Kohei Miyaguchi
- Kenji Yamanishi
- 2019
- AISTATS 2019
Cogra: Concept-drift-aware stochastic gradient descent for time-series forecasting
- Kohei Miyaguchi
- Hiroshi Kajino
- 2019
- AAAI 2019
Top collaborators
TK
Takayuki Katsuki
Researcher
TI
Toshiya Iwamori
Research Scientist
TO
Takayuki Osogami
Senior Technical Staff Member
HK
Hiroshi Kajino
Researcher