2 citations · 3 across the 5 of their papers we have counts for
5 papers · 1 filter
Detection of Unobserved Common Causes based on NML Code in Discrete, Mixed, and Continuous Variables
Masatoshi Kobayashi, Kohei Miyagichi, Shin Matsushima
Causal discovery in the presence of unobserved common causes from observational data only is a crucial but challenging problem. We categorize all possible causal relationships betw…
Hyperparameter Selection Methods for Fitted Q-Evaluation with Error Guarantee
Kohei Miyaguchi
We are concerned with the problem of hyperparameter selection for the fitted Q-evaluation (FQE). FQE is one of the state-of-the-art method for offline policy evaluation (OPE), whic…
PAC-Bayesian Transportation Bound
Kohei Miyaguchi
Empirically, the PAC-Bayesian analysis is known to produce tight risk bounds for practical machine learning algorithms. However, in its naive form, it can only deal with stochastic…
Adaptive Minimax Regret against Smooth Logarithmic Losses over High-Dimensional -Balls via Envelope Complexity
Kohei Miyaguchi, Kenji Yamanishi
We develop a new theoretical framework, the \emph{envelope complexity}, to analyze the minimax regret with logarithmic loss functions and derive a Bayesian predictor that adaptivel…
High-dimensional Penalty Selection via Minimum Description Length Principle
Kohei Miyaguchi, Kenji Yamanishi
We tackle the problem of penalty selection of regularization on the basis of the minimum description length (MDL) principle. In particular, we consider that the design space of the…