5 citations · 5 across the 5 of their papers we have counts for
6 papers
Tightly Robust Optimization via Empirical Domain Reduction
Akihiro Yabe, Takanori Maehara
Data-driven decision-making is performed by solving a parameterized optimization problem, and the optimal decision is given by an optimal solution for unknown true parameters. We o…
Causality and Robust Optimization
Akihiro Yabe
A decision-maker must consider cofounding bias when attempting to apply machine learning prediction, and, while feature selection is widely recognized as important process in data-…
Empirical Hypothesis Space Reduction
Akihiro Yabe, Takanori Maehara
Selecting appropriate regularization coefficients is critical to performance with respect to regularized empirical risk minimization problems. Existing theoretical approaches attem…
Causal Bandits with Propagating Inference
Akihiro Yabe, Daisuke Hatano, Hanna Sumita +4
Bandit is a framework for designing sequential experiments. In each experiment, a learner selects an arm and obtains an observation corresponding to . Theore…
Bi-polynomial rank and determinantal complexity
Akihiro Yabe
The permanent vs. determinant problem is one of the most important problems in theoretical computer science, and is the main target of geometric complexity theory proposed by Mulmu…
A Combinatorial Formula for Principal Minors of a Matrix with Tree-metric Exponents and Its Applications
Hiroshi Hirai, Akihiro Yabe
Let be a tree with a vertex set . Denote by the distance between vertices and . In this paper, we present an explicit combinatorial formula…