On-line detection of continuous changes in stochastic processes. - Kohei Miyaguchi; Kenji Yamanishi (DSAA, 2015)

Detecting gradual changes from data stream using MDL-change statistics. - Kenji Yamanishi; Kohei Miyaguchi (IEEE BigData, 2016)

Structure Selection for Convolutive Non-negative Matrix Factorization Using Normalized Maximum Likelihood Coding. - Atsushi Suzuki; Kohei Miyaguchi; Kenji Yamanishi (ICDM, 2016)

Detecting changes in streaming data with information-theoretic windowing. - Ryoya Kaneko; Kohei Miyaguchi; Kenji Yamanishi (IEEE BigData, 2017)

Sparse Graphical Modeling via Stochastic Complexity. - Kohei Miyaguchi; Shin Matsushima; Kenji Yamanishi (SDM, 2017)

Normalized Maximum Likelihood with Luckiness for Multivariate Normal Distributions. - Kohei Miyaguchi (CoRR, 2017)

Online detection of continuous changes in stochastic processes. - Kohei Miyaguchi; Kenji Yamanishi (Int. J. Data Sci. Anal., 2017)

Adaptive Minimax Regret against Smooth Logarithmic Losses over High-Dimensional ε1-Balls via Envelope Complexity. - Kohei Miyaguchi; Kenji Yamanishi (CoRR, 2018)

High-dimensional penalty selection via minimum description length principle. - Kohei Miyaguchi; Kenji Yamanishi (Mach. Learn., 2018)

Cogra: Concept-Drift-Aware Stochastic Gradient Descent for Time-Series Forecasting. - Kohei Miyaguchi; Hiroshi Kajino (AAAI, 2019)

Adaptive Minimax Regret against Smooth Logarithmic Losses over High-Dimensional l1-Balls via Envelope Complexity. - Kohei Miyaguchi; Kenji Yamanishi (AISTATS, 2019)

PAC-Bayesian Transportation Bound. - Kohei Miyaguchi (CoRR, 2019)

Asymptotically Exact Error Characterization of Offline Policy Evaluation with Misspecified Linear Models. - Kohei Miyaguchi (NeurIPS, 2021)

Detection of Unobserved Common Cause in Discrete Data Based on the MDL Principle. - Masatoshi Kobayashi; Kohei Miyaguchi; Shin Matsushima (IEEE Big Data, 2022)

Variational Inference for Discriminative Learning with Generative Modeling of Feature Incompletion. - Kohei Miyaguchi; Takayuki Katsuki; Akira Koseki; Toshiya Iwamori (ICLR, 2022)

Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Prediction. - Takayuki Katsuki; Kohei Miyaguchi; Akira Koseki; Toshiya Iwamori; Ryosuke Yanagiya; Atsushi Suzuki (IJCAI, 2022)

Hierarchical Lattice Layer for Partially Monotone Neural Networks. - Hiroki Yanagisawa; Kohei Miyaguchi; Takayuki Katsuki (NeurIPS, 2022)

A Theoretical Framework of Almost Hyperparameter-free Hyperparameter Selection Methods for Offline Policy Evaluation. - Kohei Miyaguchi (CoRR, 2022)

Biases in In Silico Evaluation of Molecular Optimization Methods and Bias-Reduced Evaluation Methodology. - Hiroshi Kajino; Kohei Miyaguchi; Takayuki Osogami (CoRR, 2022)

Biases in Evaluation of Molecular Optimization Methods and Bias Reduction Strategies. - Hiroshi Kajino; Kohei Miyaguchi; Takayuki Osogami (ICML, 2023)

Worst-Case Offline Reinforcement Learning with Arbitrary Data Support. - Kohei Miyaguchi (NeurIPS, 2024)

Detection of Unobserved Common Causes based on NML Code in Discrete, Mixed, and Continuous Variables. - Masatoshi Kobayashi; Kohei Miyaguchi; Shin Matsushima (CoRR, 2024)

Socially efficient mechanism on the minimum budget. - Hirota Kinoshita; Takayuki Osogami; Kohei Miyaguchi (CoRR, 2024)

A Provable Approach for End-to-End Safe Reinforcement Learning. - Akifumi Wachi; Kohei Miyaguchi; Takumi Tanabe; Rei Sato; Youhei Akimoto (CoRR, 2025)

Path Learning with Trajectory Advantage Regression. - Kohei Miyaguchi (CoRR, 2025)

Wafer Defect Root Cause Analysis with Partial Trajectory Regression. - Kohei Miyaguchi; Masao Joko; Rebekah Sheraw; Tsuyoshi Idé (CoRR, 2025)

Sequence-Aware Inline Measurement Attribution for Good-Bad Wafer Diagnosis. - Kohei Miyaguchi; Masao Joko; Rebekah Sheraw; Tsuyoshi Idé (CoRR, 2025)

Cross-Process Defect Attribution using Potential Loss Analysis. - Tsuyoshi Idé; Kohei Miyaguchi (CoRR, 2025)