6 papers
Learning-Augmented Approximation for Unrelated-Machines Makespan Scheduling
Kaito Baba, Evripidis Bampis, Giorgos Mitropoulos
Recently, Antoniadis et al. (ICLR 2025) proposed a framework for incorporating predictions to approximate NP-hard selection problems. Despite its simplicity, this approach tightly…
Conditional PED-ANOVA: Hyperparameter Importance in Hierarchical & Dynamic Search Spaces
Kaito Baba, Yoshihiko Ozaki, Shuhei Watanabe
We propose conditional PED-ANOVA (condPED-ANOVA), a principled framework for estimating hyperparameter importance (HPI) in conditional search spaces, where the presence or domain o…
Multi-Modal Multi-Agent Reinforcement Learning for Radiology Report Generation
Kaito Baba, Risa Kishikawa, Satoshi Kodera
We propose MARL-Rad, a multi-modal multi-agent reinforcement learning framework for radiology report generation that trains the entire agentic system on policy within its deployed…
Prover Agent: An Agent-Based Framework for Formal Mathematical Proofs
Kaito Baba, Chaoran Liu, Shuhei Kurita +1
We present Prover Agent, a novel AI agent for automated theorem proving that integrates large language models (LLMs) with a formal proof assistant, Lean. Prover Agent coordinates a…
Application of Contrastive Learning on ECG Data: Evaluating Performance in Japanese and Classification with Around 100 Labels
Junichiro Takahashi, JingChuan Guan, Masataka Sato +5
The electrocardiogram (ECG) is a fundamental tool in cardiovascular diagnostics due to its powerful and non-invasive nature. One of the most critical usages is to determine whether…
JRadiEvo: A Japanese Radiology Report Generation Model Enhanced by Evolutionary Optimization of Model Merging
Kaito Baba, Ryota Yagi, Junichiro Takahashi +2
With the rapid advancement of large language models (LLMs), foundational models (FMs) have seen significant advancements. Healthcare is one of the most crucial application areas fo…