8 citations · 15 across the 9 of their papers we have counts for
9 papers
Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input
So Nakashima, Tetsuya J. Kobayashi
We investigate Optimization under Input Uncertainty (OIU), in which the input to the objective function, rather than the objective function itself, is subject to uncertainty. OIU a…
Ancestral Reinforcement Learning: Unifying Zeroth-Order Optimization and Genetic Algorithms for Reinforcement Learning
So Nakashima, Tetsuya J. Kobayashi
Reinforcement Learning (RL) offers a fundamental framework for discovering optimal action strategies through interactions within unknown environments. Recent advancement have shown…
Acceleration of Evolutionary Processes by Learning and Extended Fisher's Fundamental Theorem
So Nakashima, Tetsuya J. Kobayashi
Natural selection is general and powerful concept not only to explain evolutionary processes of biological organisms but also to design engineering systems such as genetic algorith…
Rank axiom of modular supermatroids: A connection with directional DR submodular functions
Takanori Maehara, So Nakashima
A matroid has been one of the most important combinatorial structures since it was introduced by Whitney as an abstraction of linear independence. As an important property of a mat…
Multiple Knapsack-Constrained Monotone DR-Submodular Maximization on Distributive Lattice --- Continuous Greedy Algorithm on Median Complex ---
Takanori Maehara, So Nakashima, Yutaro Yamaguchi
We consider a problem of maximizing a monotone DR-submodular function under multiple order-consistent knapsack constraints on a distributive lattice. Since a distributive lattice i…
Lineage EM Algorithm for Inferring Latent States from Cellular Lineage Trees
So Nakashima, Yuki Sughiyama, Tetsuya J. Kobayashi
Phenotypic variability in a population of cells can work as the bet-hedging of the cells under an unpredictably changing environment, the typical example of which is the bacterial…
Fitness response relation of a multi-type age-structured population dynamics
Yuki Sughiyama, So Nakashima, Tetsuya J. Kobayashi
We construct a pathwise formulation for a multi-type age-structured population dynamics, which involves an age-dependent cell replication and transition of gene- or phenotypes. By…
Subspace Selection via DR-Submodular Maximization on Lattices
So Nakashima, Takanori Maehara
The subspace selection problem seeks a subspace that maximizes an objective function under some constraint. This problem includes several important machine learning problems such a…
A Compact Representation for Modular Semilattices and its Applications
Hiroshi Hirai, So Nakashima
A modular semilattice is a semilattice generalization of a modular lattice. We establish a Birkhoff-type representation theorem for modular semilattices, which says that every modu…