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Tetsuya J. Kobayashi

3 papers hereh-index 219 citations13 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • last author3

Across the 3 of 3 papers where every author was matched, so the position is known.

fields
  • cond-mat.stat-mech1
  • cs.LG1
  • q-bio.MN1
same name
  • Tetsuya J. Kobayashi — 6 papers, h 19
  • Tetsuya J. Kobayashi — 3 papers
  • Tetsuya J. Kobayashi — 3 papers

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedAncestral Reinforcement Learning: Unifying Zeroth-Order Optimization and Genetic Algorithms for Reinforcement Learning

2 citations · 2 across the 2 of their papers we have counts for

collaborators

3 papers

cond-mat.stat-mech2025

Information geometry of perturbed gradient flow systems on hypergraphs: A perspective towards nonequilibrium physics

Dimitri Loutchko, Keisuke Sugie, Tetsuya J Kobayashi

This article serves to concisely review the link between gradient flow systems on hypergraphs and information geometry which has been established within the last five years. Gradie…

q-bio.MN2025

Information geometry of chemical reaction networks: Cramer-Rao bound and absolute sensitivity revisited

Dimitri Loutchko, Yuki Sughiyama, Tetsuya J. Kobayashi

Information geometry is based on classical Legendre duality but allows to incorporate additional structure such as algebraic constraints and Bregman divergence functions. It is nat…

cs.LG2024★ 2 cited

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.